The relationship between nutritional and inflammatory changes during critical illness
A thesis submitted for the degree of
Doctor of Philosophy
Imperial College London
By
Liesl Wandrag
Section of Investigative Medicine
Division of Endocrinology, Diabetes and Metabolism
Faculty of Medicine
Imperial College London
July 2013
1
Abstract
Muscle mass loss in the critically ill is substantial, between 1-2% per day. Muscle wasting and weakness delays patient recovery and rehabilitation. Understanding the relationship between nutritional and inflammatory markers will help to identify a notional “nutritional tipping point”, where anabolism exceeds catabolism, which will allow us to target nutritional therapy more successfully.
Muscle depth on ultrasound, inflammatory markers, cytokines, urinary markers of protein breakdown and nitrogen balance data were collected in 78 critically ill patients. Patients lost a significant amount of muscle depth during their intensive care unit (ICU) stay. Results indicate that a nutritional tipping point is unlikely to exist during an ICU admission. Data suggests that nitrogen balance remains negative over 20 days, indicating that whilst patients should continue to receive standard nutrition support during their ICU stay, any additional nutritional strategy should be targeted outside of the ICU environment. Multilevel modelling indicated that each additional day spent on ICU was significantly associated with decreased C-reactive protein, muscle depth and interleukin-10 levels. With every year increase in a patient’s age muscle depth and 3-methylhistidine levels were significantly reduced and interleukin- 10 levels were significantly increased. Sample size estimates from current data provide some guidance for future studies, however estimates should be viewed with caution.
The feasibility of providing a leucine-enriched essential amino acid supplement to trauma ICU patients was furthermore assessed. Inflammatory and nutritional makers were assessed along with protein turnover. Although it was feasible to use the supplement in the trauma population, studying this patient group was more complicated than anticipated. Whole body protein turnover was enhanced in trauma patients with breakdown far exceeding synthesis.
These studies suggest that although it may be feasible to provide amino acid supplementation to critically ill patients, this type of intervention should be targeted outside of the ICU environment.
2 Statement of work
The majority of the work described in this thesis was performed by the author. Collaborations are listed in the acknowledgements. Assistance provided is described below:
Chapter 4:
Recruitment and data collection at St Mary’s Hospital was performed by Ms Ella Segaran whilst I was recruiting at Charing Cross and Hammersmith Hospitals for the Nutritional Tipping Point study. ICU Research nurses from Imperial College Healthcare NHS Trust assisted with screening and some of the patient recruitment. Ms May To continued to recruit patients and collect data at other sites once I started recruiting for the essential amino acid study at Kings College Hospital. Ms May To performed the ELISA for cytokine analysis.
Chapter 5:
ICU research nurse at Kings College Hospital London assisted with screening of patients. Ms May To performed the ELISA for cytokine analysis for the essential amino acid supplementation study. All stable isotope studies were performed with the assistance of two ICU research nurses.
Statistical Analysis:
Reliability statistics, descriptive statistics and sample size estimates were performed by me. The multilevel modelling in Chapter 4 was performed by a statistician.
3 Acknowledgements
I am incredibly grateful to my three supervisors, Dr Stephen Brett for his invaluable clinical expertise and unwavering support throughout this project, and Dr Mary Hickson and Prof Gary Frost for their exceptional academic support over the years. Their guidance and encouragement has been inspirational.
I am enormously grateful to my funder: The Department of Health via the National Institute for Health Research (NIHR) for providing me with a Clinical Doctoral Research Fellowship. Support of the National Institute for Health Research, through the Comprehensive Clinical Research Network should also be acknowledged.
I am grateful to Ms Ella Segaran and Ms May To for screening and recruiting for the nutritional tipping point study and to Ms To who conducted all the cytokine analysis (ELISA) under supervision of Dr Fred Tam, Imperial College London. I am very grateful for the support and guidance that Dr Tam provided during this analysis.
All Critical Care clinical, research and audit staff at both Imperial College Healthcare NHS Trust and Kings College Hospital are acknowledged for support throughout this research. At Kings College Hospital the bedside nurses administered the amino acid supplement.
I am very grateful in particular to the critical care research and clinical audit team from both Imperial College Healthcare NHS Trust and Kings College Hospital Trust. Without the input and support of the research nurses this study would have been very difficult to complete. Thank you in particular to research nurse Juan Moreno at Hammersmith Hospital who assisted with screening and recruitment on days when I had study patients elsewhere.
Research nurses in Critical Care and the Emergency Department at Kings College Hospital are thanked for their assistance with screening and with unwavering support in performing the stable isotope studies, in particular infusion preparation, blood sampling and assistance with gas sampling via the Douglas bag.
Support and advice from Critical Care Consultants at Kings College Hospital, in particular Dr Stephanie Strachan, Dr Phil Hopkins, Dr Andre Vercueil and Dr Ritesh Maharaj.
I would like to thank laboratory staff at Charing Cross Hospital for all the urinary analysis, in particular Maribel Gracia under guidance of Dr Maggie Hancock.
I would also like to thank staff at the Institute of Liver Studies, Kings College Hospital for use of freezer space and assistance with dry ice provision for sample transfer.
I am very grateful to Dr Nicola Jackson, Mrs Jo Batt, Dr Martin Whyte, Dr Moradie and Prof Margot Umpleby at Surrey University for support and guidance with stable isotope study preparation and analysis.
4 Also, thank you to Claire Black, NIHR Clinical Doctoral Research Fellow at UCL, for her invaluable input into indirect calorimetry at the start of this project.
I would like to acknowledge support from the SHS/Nutricia (Nutricia Ltd., Wiltshire, United Kingdom) for provision of the essential amino acid and leucine supplements.
I would like to thank and acknowledge support from the statistical service at University College London (UCL) for multilevel modelling analysis of the nutritional tipping point study, performed by Ms Bountziouka and Dr Wade.
Last but not least, thank you to my family and friends for their love, support and patience over the years. In particular, to my husband David for your support, astute advice, for cooking and for generally putting up with it all!
5 Copyright declaration
The copyright of this thesis rests with the author and is made available under a Creative Commons Attribution-Non Commercial-No Derivatives licence. Researchers are free to copy, distribute or transmit the thesis on the condition that they attribute it, that they do not use it for commercial purposes and that they do not alter, transform or build upon it. For any reuse or distribution, researchers must make clear to others the licence terms of this work.
6 Table of Contents
Chapter 1. Introduction ...... 21
1. 1 Muscle mass ...... 22
1.1.1 Muscle mass in health ...... 22
1.1.2 Muscle mass in critical illness ...... 23
1.2 Skeletal muscle mass control ...... 24
1.2.1 Skeletal muscle breakdown and synthesis ...... 24
1.3 Skeletal muscle breakdown ...... 26
1.3.1 The stress response ...... 26
1.3.2 Neuro-endocrine response ...... 27
1.3.3 Cytokines and muscle breakdown ...... 28
1.3.4 Signalling pathways regulating muscle breakdown ...... 30
1.3.5 Hypoxia, inflammation and wasting ...... 32
1.3.6 Reactive oxygen species (ROS) and nitric oxide (NO) ...... 34
1.3.7 Age ...... 34
1.3.8 Drugs...... 34
1.3.9 Immobility and disuse atrophy ...... 35
1.3.10 Nutritional problems on ICU ...... 37
1.4 ICU acquired weakness (ICUAW) ...... 41
1.5 Critical illness myopathy ...... 42
1.6 Muscle protein synthesis ...... 45
1.6.1Signalling pathways and proteins regulating protein synthesis ...... 46
1.7 Potential strategies to stimulate muscle protein synthesis...... 46
1.7.1 Drugs...... 46
1.7.2 Dietary manipulation ...... 48
1.7.3 Electrical muscle stimulation ...... 56
1.7.4 Early mobilisation ...... 58
7 1.8 Anabolic resistance ...... 59
1.9 Nutritional Tipping Point ...... 59
1.10 New PINI ...... 60
1.12 Hypotheses ...... 61
1.12.1 Main hypothesis ...... 61
1.12.2 Secondary hypothesis...... 62
1.12.3 Research aims ...... 62
Chapter 2. Core Methods ...... 63
2.1 Cytokines ...... 63
2.1.1 Sample preparation and storage ...... 63
2.2 Enzyme-linked immuno-sorbent assay (ELISA) ...... 64
2.2.1 Materials and chemicals ...... 64
2.2.2 Cytokines ...... 64
2.2.3 Standard solutions and buffers ...... 64
2.2.4 Capture antibody ...... 65
2.2.5 Detection antibody ...... 65
2.2.6 Standards ...... 65
2.3 Interleukin-10 and IL-6 human duoset protocol ...... 66
2.4 Calculation of results ...... 67
2.5 Assay reliability ...... 68
2.5.1Interleukin-10 intra-assay reliability ...... 68
2.5.2 Interleukin-10 inter-assay reliability ...... 68
2.6 Interleukin-6 preparation method ...... 68
2.6.1 IL-6 intra-assay reliability ...... 68
2.6.2 Il-6 inter-assay reliability ...... 68
2.7 Urinary studies: 3-methylhistidine and urinary urea ...... 69
2.8 Nitrogen balance ...... 70
2.9 Muscle depth change with ultrasound ...... 72
2.9.1 Bicep ...... 73
2.9.2 Forearm...... 74
8 2.9.3 Thigh ...... 75
2.9.4 Muscle ultrasound studies ...... 76
2.10 Activities of daily living (ADL) ...... 77
2.11 Medical Research Council (MRC) sum score ...... 78
2.12 Protein turnover via stable isotope studies ...... 80
2.12.1 Stable isotope study ...... 81
2.12.2 Blood sampling ...... 83
2.12.3 Breath sampling ...... 83
2.12.4 Analytical methods: α-Ketoisocaproate derivatisation ...... 85
2.12.5 Isotope ratio - mass spectrometry (IRMS) ...... 87
2.12.6 Leucine metabolism calculations ...... 88
2.12.7 Protein turnover calculations ...... 89
2.13 Leucine-enriched essential amino acid supplementation ...... 89
2.14 Screening and recruitment ...... 90
2.14.1 Procedure of obtaining informed consent or assent ...... 90
Chapter 3. Reliability of Methods ...... 92
Technique Reliability for Measurement in ICU ...... 92
3.1 Indirect calorimetry ...... 92
3.1.1 Alcohol burn testing ...... 94
3.1.2 Calculations ...... 94
3.1.3 Deltatrac II™ comparison exercise ...... 96
3.1.3.1 Results: Deltatrac™ comparison study ...... 97
3.1.3.2 Discussion: Deltatrac™ comparison study ...... 97
3.2 Muscle ultrasound reliability studies ...... 98
3.2.1 Tranducer calibration ...... 98
3.2.2 Intra-rater reliability study...... 99
3.2.2.1 Intra-class correlation coefficient results...... 100
3.2.2.2 Bland Altman analysis results ...... 101
3.2.2.3 Standard error of measurement results ...... 103
3.2.2.4 Discussion ...... 103
9 3.2.3 Inter-rater reliability study...... 104
3.2.3.1 Inter-rater intra-class correlation coefficient results ...... 105
3.2.3.2 Bland Altman analysis results ...... 106
3.2.3.3 Standard error of measurement results ...... 107
3.2.3.4 Inter-rater reliability discussion ...... 107
Chapter 4. Nutritional Tipping Point ...... 109
4.1 Introduction ...... 109
4.1.1 Research Question ...... 109
4.1.2 Aims ...... 110
4.2 Methods ...... 110
4.2.1 Outcome measures ...... 110
4.2.2 ICU data ...... 111
4.2.3 Study plan ...... 112
4.2.4 Sample Size Calculation ...... 113
4.2.5 Statistical Analysis ...... 113
4.2.6 Surrogate endpoints methodology ...... 116
4.3 Results: Nutrition Tipping Point Study ...... 116
4.3.1 Screening and recruitment ...... 116
4.3.2 Demographics ...... 119
4.3.3 Muscle depth loss in ICU patients ...... 121
4.3.4 Nutritional tipping point ...... 123
4.3.5 Nutritional tipping point - univariate analysis ...... 130
4.3.6 Nutritional tipping point - multilevel modelling results ...... 136
4.3.6.1 Predicted values for C-reactive protein...... 136
4.3.6.2 Predicted values for serum albumin ...... 136
4.3.6.3 Predicted values for urinary urea ...... 138
4.3.6.4 Predicted values for 3-methylhistidine ...... 139
4.3.6.5 Predicted values for total muscle depth ...... 141
4.3.7 The new prognostic inflammatory and nutritional index ...... 142
10 4.3.8 Inter-relationships between inflammatory and nutritional markers during critical illness ...... 144
4.3.8.1 C-reactive protein ...... 144
4.3.8.2 Serum albumin ...... 144
4.3.8.3 Urinary urea ...... 146
4.3.8.4 Urinary 3-methylhistidine ...... 146
4.3.8.5 Total muscle depth ...... 147
4.3.8.6 Nitrogen balance ...... 151
4.3.8.7 Interleukin-6 ...... 151
4.3.8.8 Interleukin-10 ...... 153
4.3.8.9 Multilevel modelling with nitrogen balance ...... 155
4.3.9 Functional Outcomes ...... 157
4.3.10 Data for surrogate endpoints for future nutritional clinical trials...... 161
4.4 Discussion ...... 163
4.4.1 Screening and recruitment ...... 164
4.4.2 Patient demographics ...... 164
4.4.3 Muscle depth change on ICU ...... 165
4.4.4 Nutritional tipping point ...... 168
4.4.5 Univariate analysis - correlations ...... 170
4.4.6 Multilevel modelling ...... 171
4.4.7 Prognostic Inflammatory and Nutritional Index ...... 180
4.4.8 Functional Outcomes ...... 182
4.4.9 Surrogate endpoints for future nutritional clinical trials ...... 182
4.5 Conclusion ...... 184
Chapter 5. Essential Amino Acid Supplementation in Critically Ill Trauma Patients – A Feasibility Study ...... 186
5.1 Introduction ...... 186
5.1.1Research question ...... 186
5.1.2 Research aims ...... 186
5.2 Methods ...... 187
5.2.1 Study groups ...... 187
11 5.2.2 Study outcome measures ...... 187
5.2.3 ICU data collection ...... 188
5.2.4 Screening and recruitment ...... 189
5.2.5 Randomisation ...... 190
5.2.6 Intervention: Leucine enriched essential amino acid supplementation ...... 190
5.2.7 Study plan ...... 192
5.2.8 Statistical Analysis ...... 193
5.3 Results ...... 194
5.3.1 Screening and recruitment ...... 194
5.3.2 Demographic data ...... 195
5.3.3 L-EAA supplement received ...... 196
5.3.4 Outcome measures ...... 197
5.3.5 Leucine kinetics ...... 198
5.4 Discussion ...... 201
5.4.1 Recruitment ...... 201
5.4.2 Determining whether the trauma population represent a feasible patient population to study ...... 201
5.4.3 Determining whether the intervention is feasible to study within trauma critical care ...... 202
5.4.4 Feasibility of the outcome measures and endpoints used in this study ...... 203
5.4.5 Developing a strategy that will minimise muscle mass decline in critical illness . 207
5.5 Conclusion ...... 207
Chapter 6. Conclusion and future work ...... 209
References ...... 214
Appendices ...... 238
Appendix I: Data Collection Sheet ...... 239
Appendix II: Barthel Index ...... 241
Appendix III: Katz Index ...... 242
Appendix IV: MRC Sum Score ...... 243
Appendix V: Muscle ultrasound data collection sheet ...... 244
Appendix VI: IL-10 human duoset protocol ...... 245
12 Appendix VII: The distribution of patients’ BMI and length of stay on ICU ...... 247
Appendix VIII: Spread of patient data illustrated by trajectory plots ...... 248
Appendix IX: Multilevel modelling ...... 252
Appendix X: Predicted values from multilevel modelling ...... 254
Appendix XI: KIC Standard Curve ...... 273
Appendix XII: Example of Patient KIC Curve ...... 274
13 Appendix XIII: Example curves for CO2 breath and α-KIC samples ...... 275
Appendix XIV: Permissions to republish third party documents ...... 276
Appendix XV: Ethics Approval ...... 278
Appendix XVI: Presentations ...... 281
13 Glossary of Abbreviations
ADL Activities of Daily Living
Akt Serine/Threonine Protein Kinase
α-KIC α-Ketoisocaproic Acid
APACHE II Acute Physiological and Chronic Health Evaluation score
ARDS Acute Respiratory Distress Syndrome
CIM Critical Illness Myopathy
COPD Chronic Obstructive Pulmonary Disease
CPAP Continuous Positive Airway Pressure
CRP C-Reactive Protein
CRRT Continuous Renal Replacement Therapy
DEXA Dual-Energy X-ray Absorptiometry
EAA Essential Amino Acid
EDTA Ethylenediaminetetraacetic Acid
ELISA Enzyme Linked Immuno Sorbent Assay
FiO2 Fractional Inspired Oxygen
FoxO Forkhead Box O
GCMS Gas Chromatography Mass Spectrometry
GCS Glasgow Coma Scale
HDU High Dependency Unit
HMB Beta-Hydroxy Beta-Methylbutyric Acid
ICC Intra-class Correlation Coefficient
ICNARC Intensive Care National Audit and Research Centre
14 ICU Intensive Care Unit
ICUAW Intensive Care Unit Acquired Weakness
IL-6 Interleukin-6
IL-10 Interleukin-10
ISS Injury Severity Score
L-EAA Leucine-Enriched Essentail Amino Acid
LeuRa Leucine Rate of Appearance
Mafbx Muscle Atrophy F-box Protein
3-MH 3-Methylhistidine
MPB Muscle Protein Breakdown
MPS Muscle Protein Synthesis
MRC Sum Score Medical Research Council Sum Score mTOR Mammalian Target of Rapamycin
MTBSTFA N methyl(tert-butyldimethylsily) trifluoroactemadie
MuRF-1 Muscle-Specific Ring Finger-1
Nf-κB Nuclear Factor kappa-B
NICE National Institute for Clinical Excellence
NOLD Non-Oxidative Leucine Disposal
NMBA Neuro-Muscular Blocking Agent
OFN Oxygen Free Nitrogen
PEEP Positive End Expiratory Pressure
PINI Prognostic Inflammatory and Nutritional Index
RCT Randomised Controlled Trial
RF-CSA Rectus Femoris Cross Sectional Area
15 RQ Respiratory Quotient
SAPS II Simplified Acute Physiology Score II
SD Standard Deviation
SEM Standard Error of Measurement
SOFA Sequential Organ Failure Assessment
TBI Traumatic Brain Injury
TNFα Tumour Necrosis Factor alpha
VO2 Volume of Oxygen inspired (ml/min)
VCO2 Volume of Carbon Dioxide produced (ml/min)
WCC White Cell Count
16 List of Tables
TABLE 1-1: HORMONAL EFFECT ON PROTEIN METABOLISM 28
TABLE 1-2: AMINO ACID SUPPLEMENTATION STUDIES IN ICU 54
TABLE 2-1: NITROGEN BALANCE EQUATIONS 71
TABLE 2-2: CO2 PRODUCTION RATE CALCULATION 85
TABLE 3-1: MEASURING THREE CALORIMETERS ON THE SAME ICU PATIENT 97
TABLE 3-2: INTRA-RATER RELIABILITY, A TWO WAY MIXED MODEL 101
TABLE 3-3: DESCRIPTIVE STATISTICS FROM MORNING AND AFTERNOON ULTRASOUND MEASUREMENTS FOR TOTAL MUSCLE SITES FROM ONE RATER (N=20) 101
TABLE 3-4: INTER-RATER RELIABILITY WITH ICC PER SITE, TWO WAY RANDOM MODEL 105
TABLE 3-5: DESCRIPTIVE STATISTICS FOR RELIABILITY TESTING FOR ALL MUSCLE SITES BETWEEN TWO RATERS (N=12) 106
TABLE 4-1: DEMOGRAPHIC DATA OF ICU PATIENT COHORT (N=78) 119
TABLE 4-2: BASELINE VALUES FOR NUTRITIONAL AND INFLAMMATORY MARKERS (1ST DAY OF MEASUREMENT). 120
TABLE 4-3: MUSCLE DEPTH CHANGE OVER 7 DAYS (N=45) 121
TABLE 4-4: MUSCLE DEPTH CHANGE OVER 14 DAYS (N=17) 121
TABLE 4-5: PERCENTAGE MUSCLE LOSS OVER 14 DAYS (N=17) 123
TABLE 4-6: CORRELATION MATRIX FOR MUSCLE DEPTH LOSS 133
TABLE 4-7: PREDICTED VALUES OF URINARY UREA, NITROGEN LOSS AND PROTEIN BREAKDOWN 138
TABLE 4-8: THE NEW PINI MEASURED OVER 14 DAYS 142
TABLE 4-9: THE NEW PINI MEASURED OVER 14 DAYS IN 17 PATIENTS 143
TABLE 4-10: RESULTS FROM LINEAR MIXED MODELLING TO ASSESS THE CHARACTERISTICS RELATED TO INFLAMMATORY AND NUTRITIONAL MARKERS (SQUARE ROOT TRANSFORMATIONS). 145
TABLE 4-11: RESULTS FROM MEDIAN REGRESSION MIXED MODELS ASSESSING THE CHARACTERISTICS RELATED TO NITROGEN BALANCE (PENG AND DEACON EQUATION) AND IL- 6. 152
TABLE 4-12: RESULTS FROM MULTILEVEL BINARY MODELLING TO ASSESS THE CHARACTERISTICS RELATED WITH THE ODDS OF HAVING INTERLEUKIN-10 LEVELS > 0 PG/ML (N=77). 154
TABLE 4-13: MULTILEVEL MODELLING ASSOCIATIONS WITH NITROGEN BALANCE 156
TABLE 4-14: BIOMARKERS AND BEST MODEL FIT FOR ANALYSIS INCLUDING ICU OUTCOME, STEROID USE AND NUTRITIONAL ADEQUACY 158
17 TABLE 4-15: NITROGEN BALANCE (DEACON EQUATION) MODELLED AGAINST DAY OF MEASUREMENT AND % ENERGY INTAKE. 159
TABLE 4-16: SAMPLE SIZE DETERMINATION FROM CURRENT DATA SET 162
TABLE 5-1: COMPOSITION OF L-EAA SUPPLEMENT 191
TABLE 5-2: DEMOGRAPHIC DATA OF TRAUMA ICU PATIENT COHORT 195
TABLE 5-3: L-EAA DOSES RECEIVED AND REASONS FOR OMISSIONS 197
TABLE 5-4: LEUCINE KINETICS COMPARATOR DATA 200
18 List of Figures
FIGURE 1-1: MUSCLE MASS ON THE INTENSIVE CARE UNIT: FACTORS INVOLVED IN BREAKDOWN AND POTENTIAL STRATEGIES FOR MUSCLE BUILDING 26
FIGURE 1-2: SIMPLIFIED DIAGRAM OF CATABOLIC PATHWAYS INVOLVED WITH MUSCLE ATROPHY. 30
FIGURE 1-3: SCHEMATIC OVERVIEW OF POTENTIAL DIRECT OR INDIRECT HYPOXIA SIGNALS WITH RESPECT TO THE CONTROL OF MUSCLE PROTEIN TURNOVER. 33
FIGURE 1-4: SIMPLIFIED DIAGRAM OF ANABOLIC PATHWAYS INVOLVED WITH MUSCLE PROTEIN SYNTHESIS 45
FIGURE 2-1: SERIAL DILUTION OF THE STANDARD 66
FIGURE 2-2: SIMPLIFIED DIAGRAM OF ELISA STEPS FOR IL-10 ANALYSIS 67
FIGURE 2-3: THIGH MUSCLE ULTRASOUND 73
FIGURE 2-4: CROSS SECTION OF MID UPPER ARM 74
FIGURE 2-5: CROSS SECTION OF MID FOREARM 75
FIGURE 2-6: CROSS SECTION OF MID-THIGH 76
FIGURE 2-7: STABLE ISOTOPE STUDY PROTOCOL 82
FIGURE 2-8: STABLE ISOTOPE STUDY PREPARATION WITH BLOOD SAMPLING TUBES AND BREATH SAMPLING EXETAINERS 82
FIGURE 2-9: BREATH SAMPLE COLLECTION WITH A DOUGLAS BAG 84
FIGURE 3-1: CALIBRATION OF TRANSDUCER AGAINST A TISSUE PHANTOM 99
FIGURE 3-2: BLAND ALTMAN PLOT OF MUSCLE ULTRASOUND FROM ONE RATER (N=20) 102
FIGURE 3-3: BLAND ALTMAN PLOT OF MUSCLE ULTRASOUND BETWEEN TWO RATERS (N=12) 107
FIGURE 4-1: STUDY PLAN FOR NUTRITION TIPPING POINT STUDY 112
FIGURE 4-2: PATIENT RECRUITMENT FOR THE NUTRITION TIPPING POINT STUDY 117
FIGURE 4-3: PATIENT EXCLUSIONS FOR THE NUTRITION TIPPING POINT STUDY, N=1395 118
FIGURE 4-4: MUSCLE DEPTH LOSS OVER 14 DAYS (N=17) 122
FIGURE 4-5: MEDIAN URINARY UREA (N=14), MUSCLE LOSS (N=17), 3-MH (N=14) AND CRP (N=17) OVER 14 DAYS ON ICU 127
FIGURE 4-6: MEDIAN 3-MH (N=14) AND MUSCLE LOSS (N=14) OVER 14 DAYS ON ICU. 128
FIGURE 4-7: NITROGEN BALANCE SERIAL MEASUREMENTS (A) AND NITROGEN BALANCE VALUES PER DAY OF MEASUREMENT ON ICU (B) 129
19 FIGURE 4-8: CORRELATION BETWEEN THE CHANGE IN 3-MH AND NITROGEN LOSS OVER 7 DAYS (N=21) 132
FIGURE 4-9: SPEARMAN’S RANK CORRELATION BETWEEN CRP AND IL-6 132
FIGURE 4-10: CORRELATION BETWEEN PERCENTAGE MUSCLE LOSS AND AGE 134
FIGURE 4-11: CORRELATION BETWEEN PERCENTAGE MUSCLE DEPTH LOSS AND NITROGEN LOSS (N=20), 3-MH: CREATININE RATIO (N=18), ENERGY INTAKE (N=45) AND PROTEIN INTAKE (N=45) 135
FIGURE 4-12: PREDICTED VALUES FOR CRP (MG/L) AND SERUM ALBUMIN (G/L) WITH CORRESPONDING 95% CONFIDENCE INTERVALS ACCORDING TO DAY OF MEASUREMENT ON ICU. 137
FIGURE 4-13: PREDICTED VALUES FOR URINARY UREA (MMOL/24H) AND 3-MH (µMOL/24H) WITH CORRESPONDING 95% CONFIDENCE INTERVALS ACCORDING TO THE DAY OF MEASUREMENT ON ICU 140
FIGURE 4-14: PREDICTED VALUES FOR TOTAL MUSCLE DEPTH (CM) IN 52 YEAR OLD MALE PATIENTS (L) AND 63 YEAR OLD MALE PATIENTS (R) PER DAY OF MEASUREMENT 141
FIGURE 4-15: PREDICTED VALUES FOR MUSCLE DEPTH LOSS (CM) WITH CORRESPONDING 95% CONFIDENCE INTERVALS SELECTED FOR AGE (YEARS) IN FEMALE PATIENTS 149
FIGURE 4-16: PREDICTED VALUES FOR MUSCLE DEPTH LOSS (CM) WITH CORRESPONDING 95% CONFIDENCE INTERVALS SELECTED FOR AGE (YEARS) IN MALE PATIENTS 150
FIGURE 4-17: PREDICTED VALUES FOR THE ODDS OF HAVING INTERLEUKIN-10 LEVELS > 0 PG/ML WITH CORRESPONDING 95% CONFIDENCE INTERVALS, ACCORDING TO DAY OF MEASUREMENT. 153
FIGURE 4-18: CONCEPTUAL DIAGRAM OF THE PROPOSED TRAJECTORIES OF PRO-INFLAMMATORY RESPONSES AND ANABOLIC SWITCH IN THE CRITICALLY ILL. 163
FIGURE 5-1: LEUCINE-ENRICHED ESSENTIAL AMINO ACID SUPPLEMENT SHOWING THE SUPPLEMENT AS DELIVERED (IN WHITE POTS) AND AS DISPENSED FOR THE PATIENT (BOTTLES) 192
FIGURE 5-2: STUDY PLAN OF ESSENTIAL AMINO ACID SUPPLEMENTATION STUDY 193
FIGURE 5-3: PATIENT RECRUITMENT FOR THE FEASIBILITY STUDY 194
20 Chapter 1. Introduction
For many survivors of critical illness, discharge from the intensive care unit (ICU) merely marks the start of substantial challenges prior to full recovery. A problem common to virtually all ICU patients is the deterioration in nutritional and functional status during their stay on ICU. The recent National Institute of Clinical Excellence clinical guideline ‘Rehabilitation after Critical Illness’ has recommended that research is urgently required to examine muscle mass loss in the critically ill along with developing strategies to ameliorate this loss (http://www.nice.org.uk/Guidance/CG83, accessed 1 July 2013).
In the United Kingdom, approximately 75,000 patients are discharged alive from intensive care units per year, and of these around 68,000 are discharged home. Data from ICU follow up clinics (Broomhead and Brett 2002) report physical and non-physical difficulties such as depression, anxiety and cognitive dysfunction which have a major impact on patients and their families as relationships alter and finances become strained. Furthermore, this slow non-physical recovery is compounded by a very slow recovery of physical strength and functional status. This was best characterised in a well studied cohort of patients who survived the acute respiratory distress syndrome (ARDS). Muscle weakness was confirmed to be the single greatest determinant of outcome and showed that recovery should be measured in months to years rather than days to weeks (Herridge et al. 2003).
The consequences of sepsis, trauma and multi-organ failure present a myriad of problems for clinicians throughout the patient’s pathway of care long after they have been discharged from the ICU. A prolonged stay in hospital and a lengthy period of rehabilitation is frequently required (Bolton 2005;Griffiths and Jones 2007a).
21 Although muscle loss on ICU is often masked by accumulation of fluid in tissues, muscle mass declines at a rapid rate of up to 2% loss per day (Gamrin et al. 1997;Griffiths and Jones 1999). For the elderly, who are increasingly represented in the intensive care population, this will be additional to age related muscle mass loss and will seriously compromise recovery to independance. It is known that poor nutrition following admission to hospital is linked to increased morbidity and mortality and that there is a link between quality of life and nutritional status (Hickson and Frost 2004).
1. 1 Muscle mass
1.1.1 Muscle mass in health
In order to the describe muscle mass decline that critically ill patients typically experience, a brief look at muscle mass in health is required. Structural proteins in muscle consist of actin and myosin. In health there is a fine balance between muscle protein synthesis and breakdown. A healthy 70kg adult male may have approximately 12kg of body protein of which 220g would consist of free amino acids. Skeletal muscle contains around 7kg of protein which makes up around 400-450g/kg of total body mass (Wagenmakers 1999). Muscle contains the majority of the free amino acid pool, estimated to be over 60% (Rooyackers and Nair 1997;Wagenmakers 1999). Anabolic stimuli in healthy adults include ingestion of protein or amino acids in combination with resistance exercise (Cermak et al. 2012;Cermak et al. 2013;Dickinson and Rasmussen 2010).
22 1.1.2 Muscle mass in critical illness
Skeletal muscle provides an important fuel source during times of physiological stress when muscle is broken down into amino acids and converted via gluconeogenesis in the liver to glucose, an essential fuel for the brain. Skeletal muscle also provides vital amino acid precursors for immune function (antibodies and acute phase proteins) as well as wound healing and tissue repair during trauma and severe infections. Muscle mass loss on ICU is of immense concern with patients losing between 1-2% of lean body mass per day (Gamrin 1997;Plank and Hill 2000a;Reid et al. 2004).
Muscle wasting and weakness was observed one year after discharge from ICU in a cohort of patients who survived the acute respiratory distress syndrome (Herridge 2003). Physical disability has been identified up to five years after ICU admission for severe lung injury with exercise capacity only reaching 76% of the predicted test distance for the 6-minute walk test (Herridge 2011b). Muscle weakness and wasting are important contributors to the longer term physical impairment experienced by these patients (Bienvenu et al. 2012;Herridge et al. 2011). This wasting is an aggressive and multi- factorial phenomenon with the following factors thought to contribute to the process: the neuro-endocrine response to trauma and critical illness; corticosteroid use; critical illness myopathy; nerve and neuromuscular junction changes; polyneuropathy; disuse atrophy (prolonged sedation and paralysis) and inadequate nutrition (Anzueto 1999;Stevens et al. 2007). However, despite energetic nutritional support, muscle wasting still occurs in the critically ill (Reid 2004). Preservation of muscle mass is clearly crucial for patients’ survival and their subsequent rehabilitation, however optimal nutritional support during critical illness has yet to be defined.
23 1.2 Skeletal muscle mass control
Skeletal muscle mass is controlled by myostatin and various signalling pathways which regulate between protein synthesis and breakdown (Jespersen et al. 2011). Myostatin is a protein that inhibits muscle differentiation and growth. It is produced mainly in skeletal muscle cells and acts on muscle tissue. Myostatin pathways along with the serine/threonine protein kinase (Akt)-dependent signalling pathways, mammalian target of rapamycin (mTOR), glucose synthase kinase 3β (GSK3β) and the forkhead box O (FoxO) pathways control skeletal muscle mass (Jespersen 2011;McCarthy and Esser 2010;Puthucheary et al. 2010b). The exact mechanisms and interactions of skeletal muscle mass regulation pathways in the critically ill remain unclear.
The Akt pathways relate to muscle protein synthesis and are thought to be activated by both insulin and insulin-like growth factor 1 (IGF-1) (Jespersen 2011). The mTOR pathway is activated by Akt and the GSK3β pathway is inhibited by Akt. mTOR appears to be a major regulator of muscle protein synthesis in response to growth factors and nutrients, such as essential amino acids (EAA) (Rennie, Jespersen 2011).
1.2.1 Skeletal muscle breakdown and synthesis
In healthy subjects undergoing bed rest it has been shown that the balance between muscle protein synthesis (MPS) and muscle protein breakdown (MPB) is changed mostly by a reduction in synthesis (Ferrando et al. 2006a). Muscle protein breakdown occurs in a stressed state due to increased acute phase protein synthesis in the liver and synthesis of proteins involved with immune function and wound healing. Skeletal muscle is commonly referred to as a metabolic reserve for protein metabolism (Ferrando and Wolfe 2007). Studies focusing on MPS and MPB in critical care are rare, investigators are nonetheless attempting to understand the pathophysiological changes in
24 order to develop new interventions that might facilitate rehabilitation (Puthucheary et al. 2010a;Puthucheary et al. 2012).
Muscle protein breakdown is accelerated in the critically ill, however it remains unclear whether muscle protein synthesis would be decreased or similar to that of healthy subjects. Recent investigation using protein turnover studies and muscle biopsies showed that critically ill patients had normal protein synthesis rates and that the breakdown was markedly increased (Klaude et al. 2012). Klaude et al showed that ICU patients had a 160% higher MPB compared to control subjects (healthy volunteers) and that there was no differences seen in MPS between ICU and control subjects when protein turnover was assessed (Klaude 2012) however length of stay ranged between 1-18 days and a wide range of pathology was included in this study.
Muscle mass loss on ICU, although multi-factorial and complex has largely been attributed to the following factors (see Figure1-1): Immobility or disuse atrophy (Chambers et al. 2009), age related breakdown (Attaix et al. 2005), the role of various pro-inflammatory cytokines (Winkelman 2010) and hormonal effects (Soeters and Grimble 2009). Figure 1-1 illustrates some of the factors thought to contribute to muscle breakdown in the critically ill along with potential strategies to stimulate muscle protein synthesis in these individuals, some having failed to show direct benefit.
Each of the factors thought to contribute to the process of muscle protein breakdown in critical illness will furthermore be discussed in Section 1.3. Similarly, factors that may contribute to muscle protein synthesis in the critically ill are discussed in Section 1.6. See Figure 1-1.
25
ICU Muscle Mass
Breakdown Potential Building Immobility & Disuse Atrophy Strategies Age Infection/ Sepsis Cytokines Anabolic Steroids, Insulin, GH, Hypoxia induced inflammation Testosterone, Propranolol Hypoxia and HIF-1α Amino acid supplementation Reactive Oxygen Species (Arginine, glutamine, Hormonal effect (Catecholamines creatine, BCAA, EAA Glucocorticoids) HMB, leucine v ia mTOR) Drugs (Neuro muscular blocking Early mobilisation agents(?), sedation, corticosteroids) Electrical Muscle Stimulus ↓Nutrition (GI problems, poor Mechanical Loading absorption, sedation, delay s)
Figure 1-1: Muscle mass on the Intensive Care Unit: Factors involved in breakdown and potential strategies for muscle building
[The dotted arrow denotes potential or previously attempted strategies to influence muscle synthesis]
(BCAA: branched-chain amino acid; EAA: essential amino acid; GH: growth hormone; GI: gastrointestinal; HIF-1α: hypoxia inducible factor 1 alpha; HMB: β-hydroxy β-methylbutarate; mTOR: mammalian target of rapamycin).
1.3 Skeletal muscle breakdown
1.3.1 The stress response
The metabolic response to insult or injury is generally characterised by hypermetabolism, hypercatabolism, persistent muscle wasting and hyperglycaemia. This complex interaction involves numerous mediators, hormones and cytokines. The familiar description by Sir David Cuthbertson, the so called ‘Ebb and Flow Phase’ post traumatic stress, with initial decrease
26 in metabolism followed by increased metabolic activity (Cuthbertson 1970;Cuthbertson and Tilstone 1969) has subsequently been replaced by newer definitions. The ‘Ebb’ phase was largely conducted on animal models and the ‘Flow’ phase, characterised by increased energy expenditure, insulin resistance and muscle catabolism (Griffiths et al. 1999) was deemed to be overestimating metabolic rates (Desborough 2000). More recent definitions include the Systemic Inflammatory Response Syndrome (SIRS), the Compensatory Anti-Inflammatory Response Syndrome (CARS) and Multi- organ Dysfunction Syndrome (MODS) (Bone 1992) which may themselves soon be replaced by more modern definitions based on detailed phenotype. Most clinicians would now agree to a definition which refers to an initial acute phase of the stress response which is followed by a more established phase of critical illness if organ failure persists (Cuesta and Singer 2012).
1.3.2 Neuro-endocrine response
The neuro-endocrine response to stress is activated by the hypothalamic- pituitary-adrenal (HPA) axis (Marik and Zaloga 2002). The anterior pituitary releases adreno-corticotrophic hormone (ACTH) and growth hormone, the posterior pituitary releases vasopressin, the adrenal cortex releases cortisol and aldosterone and in the pancreas glucagon is released as a direct result of the presence of adrenaline (Desborough 2000). Adrenaline stimulates glucagon, mobilises liver substrates (glycogen is converted into glucose), oxidises triglycerides into free fatty acids and converts muscle glucagon into pyruvate and lactate which is converted into glucose in the liver (Frayn 1986).
Insulin resistance occurs due to higher levels of circulating growth hormone, cortisol and fatty acids which may inhibit downstream signalling resulting in raised blood glucose levels (Marik and Raghavan 2004;Muntoni and Muntoni 2011).
27 Hormonal effects on protein metabolism post sepsis or injury are summarised in Table 1-1. Of particular interest are the catecholamines and glucocorticoids released after severe infection or injury which are important hormonal mediators resulting in proteolysis (Hasselgren 2000;Soeters 2009). Testosterone has been noted to be reduced in trauma patients potentially further exacerbating lean body mass loss (Ferrando 2000).
Table 1-1: Hormonal effect on protein metabolism
Hormone Effect on protein metabolism
Insulin ↓ protein catabolism, amino acid uptake
Insulin-like growth factors Stimulates protein synthesis
Growth Hormone Stimulates protein synthesis
Testosterone Stimulates protein synthesis
Glucagon Stimulates amino acid catabolism
Glucocorticoids Stimulates protein catabolism
Noradrenaline/Adrenaline Stimulates amino acid catabolism
Thyroid Hormones Stimulates protein catabolism
(Adapted from Awad S et al, Short term starvation and mitochondrial dysfunction, (Awad et al. 2009)
1.3.3 Cytokines and muscle breakdown
Cytokine release may contribute to skeletal muscle breakdown. Cytokines are small proteins that signal between cells to activate inflammation and to respond to infections. The metabolic response to stress is a complex interaction involving numerous mediators, hormones and cytokines. Cytokines released during trauma and critical illness regulate tissue repair and immune response (Nicolaidis 2002). Although the inflammatory response is crucial for survival the accelerated muscle breakdown is clearly deleterious to patients.
28 Interleukin-1, IL-6 and TNF, as inflammatory cytokines, are directly linked to muscle breakdown (Frost and Lang 2005;Hasselgren 2000;Soeters 2009;Wagenmakers 2001).
Interleukin-1, a pro-inflammatory cytokine linked to a systemic inflammatory reaction, it causes fever, acute-phase protein synthesis and lymphocyte reaction in response to an infection. Interleukin-1β is produced by muscle (Winkelman 2004).
Interleukin-6, a pro- and anti-inflammatory cytokine which promotes acute- phase protein synthesis, has been linked to risk of higher mortality in trauma and septic patients (Callahan and Supinski 2009). Interleukin-6 from inflammatory cells also promotes myocyte infiltration with inflammatory factors (prostaglandins) which leads to protein breakdown (Winkelman 2004).
Interleukin-10 is an anti-inflammatory cytokine which, although not linked to muscle damage, may inhibit levels IL-1, IL-6 and TNF. Interleukin-10 inhibits pro-inflammatory cytokine production from natural killer cells, neutrophils, monocytes and macrophages (Winkelman 2004).
Tumour necrosis factor, another pro-inflammatory cytokine, responds early to infection. Tumour necrosis factor alpha receptors are expressed by skeletal muscle. When TNFα binds to the receptors proteolysis takes place along with a delay of muscle repair. Overexpression of TNFα results in skeletal myopathy and muscle wasting (Winkelman 2004).
29 1.3.4 Signalling pathways regulating muscle breakdown
Various catabolic pathways have been implicated in the control of skeletal muscle breakdown or atrophy. Three main catabolic pathways include the ubiquitin-proteosome pathway (UPP), the lysosomal pathway and the calcium- dependent calpain pathway as well as caspases which have been implicated with programmed cell death (Attaix 2005;Attaix et al. 2008;Jespersen 2011;McCarthy 2010).
The regulation of these pathways is highly intricate and remains largely unknown in humans. In an attempt to understand the complex pathways I designed a simplified schematic representation, Figure 1-2:
Proteolytic Pathways
Protein Kinase
Atrogenes
(TGF-β) TNF-α Glucocorticoids Disuse ↓ Nutrients Myostatin IL-6 IGF-1 ROS
Ubiquitin-proteosome pathway Ca2+ dependent calpains & caspases
Akt NF-κB
Lysosomal FoxO
Nucleus MuRF-1
MAFbx
Muscle Atrophy
Figure 1-2: Simplified diagram of catabolic pathways involved with muscle atrophy.
IGF-1: Insulin-like growth factor 1; IL-6: Interleukin 6; TGF-β: transforming growth factor beta; TNF-α: Tumor necrosis factor alpha; ROS: Reactive oxygen species; Akt: Protein kinase B; FoxO: Forkhead Box O pathway; Nf-κB: Nuclear factor kappa-B; MuRF-1: Muscle-specific Ring Finger-1; Mafbx: Muscle atrophy F-box protein.
Stimulatory effect Inhibitory effect
30 Proteolytic pathways, along with protein kinases and atrogenes are all linked to muscle atrophy. Most of the studies relating to these pathways, the protein kinase (Akt) and atrogenes - Muscle-specific Ring Finger-1 (MuRF-1) and Muscle atrophy F-box protein (Mafbx) - were performed in cell studies or animal models (Bodine et al. 2001;Frost et al. 2007).
Akt, the serine/threonine protein kinase, is implicated in both muscle synthesis (via the mTOR pathway) and muscle atrophy via the Forkhead Box O (FoxO) pathway (de et al. 2011). FoxO is inhibited by activation or phosphorylation of Akt, and FoxO regulates both the atrogenes MuRF1 and Mafbx, see Figure 1- 2 (Constantin et al. 2011;Jespersen 2011;McCarthy 2010).
Nuclear factor kappa-B (Nf-κB) is a group of proteins involved with immune function and inflammation. The Nf-κB pathway (stimulated by cytokines or hypoxia) activates MuRF-1 and is thereby linked to muscle atrophy (de 2011;McCarthy 2010).
The calpain proteolytic system was identified for the first time in a septic animal model where calpain-1 release resulted in respiratory muscle wasting (Supinski and Callahan 2006).
Myostatin (or transforming growth factor-β, TGF-β) again was identified in animal models, TGF-β inhibits muscle growth and phosphorylation of Akt. This allows FoxO activity to increase along with the downstream atrogenes, MuRF- 1 and Mafbx (Figure 1-2), which is thought to lead to muscle breakdown (Constantin 2011;Jespersen 2011;McCarthy 2010). Myostatin also appears to inhibit the mTOR pathway linked to muscle protein synthesis (Trendelenburg et al. 2009).
Constatin et al demonstrated for the first time from muscle samples from ICU patients how catabolic pathways were affected:
31 a) Muscle protein synthesis was reduced in ICU patients compared to control subjects through reduced activation (phosphorylation) of signalling proteins. b) Protein breakdown pathways UPP and cathepsin L were upregulated at both the protein expression and mRNA level, thereby activating muscle protein breakdown. c) Both myostatin protein expression and mRNA expression were significantly higher in ICU patients than control subjects (8.5-fold, p<0.001 for protein expression and 3-fold, p<0.01 for mRNA) which the authors felt was directly linked to the reduced protein synthesis signal observed from the mTOR pathway (Constantin 2011).
Jespersen et al (2011) however observed the reverse: mRNA levels of myostatin were lower in ICU patients than controls (p<0.001); akt-mTOR signalling relating to muscle protein synthesis was higher than anticipated in ICU patients and the FoXO and atrogene pathways were lower in ICU patients than expected (Jespersen 2011). Eight patients were biopsied between days 1-3 on ICU and four patients between days 7-17.
The conflicting findings from the above two novel human studies emphasises the complexity of these pathways and highlights our poor understanding of catabolic pathways at molecular level. The heterogeneity of the ICU patients studied may also have contributed to conflicting findings.
1.3.5 Hypoxia, inflammation and wasting
A further factor proposed to contribute to skeletal muscle breakdown is hypoxia. Hypoxia induces inflammation and cytokine release may occur due to hypoxia alone, not necessarily due to an actual infection (Eltzschig and Carmeliet 2011). In environmental hypoxia, such as the high altitude environment, decreased muscle fibre mitochondria content has been
32 observed and the hypoxia-inducible factor 1 (HIF-1) and it’s subunit HIF-1α has been identified as key factors that regulate gene expression during hypoxia (Hoppeler et al. 2003;Mounier et al. 2010;Vigano et al. 2008).
In short, proposed skeletal muscle wasting mechanisms due to hypoxia include: HIF-1α activation of the NF-κB catabolic pathway; HIF-1α inhibition of Akt activation and hypoxia reducing mTOR’s capacity for protein synthesis (de 2011). See Figure 1-3 below:
Figure 1-3: Schematic overview of potential direct or indirect hypoxia signals with respect to the control of muscle protein turnover.
(Printed with permission from De Theije et al, Hypoxia and muscle maintenance regulation: implications for chronic respiratory disease’ published in Current Opinion in Clinical Nutrition and Metabolic Care 2011, 14:548-553 and from “Wolters Kluwer Health”, see Appendix).
33 1.3.6 Reactive oxygen species (ROS) and nitric oxide (NO)
Muscle breakdown may in part be caused by oxidative stress (NO and ROS) by activation of the nuclear factor kappa Beta and forkhead Box O signalling pathways (Dodd et al. 2010;McCarthy 2010), Figure 1-2. These assumptions have again only been studied in cell and animal models, human studies are still lacking.
1.3.7 Age
Increasing age results in skeletal muscle wasting. Sarcopenia or age related muscle wasting is characterised by diminished anabolic signals and promotion of catabolic signals, such as pro-inflammatory cytokines. The prevalence of sarcopenia has been described as exceeding 40% muscle mass loss in healthy subjects over 80 years of age and over 25% muscle loss in 75-80 year olds (Baumgartner et al. 1998). Sarcopenia has been attributed to decreased physical activity, poor nutrition, increased cytokine release, oxidative stress, mitochondrial dysfunction and deranged growth hormone levels with older age (Carmeli et al. 2002;Kamel 2003;Morley and Baumgartner 2004;Roubenoff 2000). With aging muscle protein synthesis is reduced (Attaix 2005;Rooyackers et al. 1996) however muscle protein breakdown in the elderly is not as clearly understood due to methodological issues with earlier studies (Attaix et al. 1998;Mitch and Goldberg 1996). With the elderly representing a large proportion of our population on intensive care, muscle wasting due to both sarcopenia and critical illness per se can be expected.
1.3.8 Drugs
Many medications given to critically ill patients have been implicated in muscle wasting of these patients. Neuro-muscular blocking agents (NMBA) were initially thought to contribute to ICU acquired weakness (ICUAW) and potentially muscle wasting, however previous studies failed to include full
34 neurological examinations making causal relationships hard to establish (Stevens et al. 2009). A recent study suggests that there were no differences seen in ICUAW between patients in the NMBA group compared to the group not receiving this agent (Papazian et al. 2010). An integrative review (Puthucheary 2012) confirms no causal relationship.
Corticosteroids may however be linked to muscle wasting (Hasselgren et al. 2010) and steroids have certainly been associated with the development of ICU acquired weakness (Amaya-Villar et al. 2005;De et al. 2002;Herridge 2003). In animal studies low dose chronic methylprednisolone administration resulted in reduced diaphragm function in rats over a 6 month study period (van Balkom et al. 1997) and acute high-dose corticosteroid administration reduced diaphragmatic function in rabbits whereas pulse-dose administration only caused marginal reduction in function (Sassoon et al. 2008). Urinary nitrogen excretion as a surrogate measure of protein breakdown was increased by 33% in head-injured patients receiving steroid treatment for 5 days compared to those patients without steroid treatment (Deutschman et al. 1987).
1.3.9 Immobility and disuse atrophy
Immobility and disuse of muscle of critically ill patients contribute to skeletal muscle breakdown. Prolonged bed rest reduces whole body protein synthesis along with muscle protein synthesis significantly. Muscle protein synthesis appears to be reduced rather than breakdown of muscle playing a major contributory role (Ferrando 2006a;Rennie et al. 2010).
In healthy volunteers undergoing 14 days of bed rest muscle protein synthesis was reduced by 50% (p<0.03) along with significant reductions observed in lean body mass (p<0.05) and nitrogen balance (p<0.03) (Ferrando et al. 1996). Bed rest studies as a model for disuse atrophy of critical illness clearly do not take into account the severity of injury or infection, nor does it reflect
35 the hormonal and cytokine response experienced by critically ill patients. Subjects undergoing bed rest with additional cortisol infused to simulate a stress response displayed lower rates of muscle protein synthesis than the group receiving cortisol and EAA supplementation (Paddon-Jones et al. 2003a). In experimental models where space flight is simulated or limbs are immobilised, muscle wasting has been recorded as between 1.5-2% per day for the first 3 weeks post bed rest (Adams et al. 2003); with a reduction in muscle size, strength is also affected.
In a 5 week bed rest study, gastrocnemius and soleus muscles in the calf decreased by 12% in size as measured by MRI with 26% reduction in plantar flexors observed (Leblanc et al. 1988). After 6 weeks of bed rest knee extensor cross sectional area was reduced by 14% and strength by 25-30% (Berg et al. 1997). Resistance exercise on the other hand preserved muscle strength after 14 days of bed rest (Bamman et al. 1998).
In rats undergoing 28 day hind limb suspension, proteolysis was thought to be increased by oxidative stress (Lawler et al. 2003). However in humans undergoing a 14 day bed rest period, markers of oxidative stress did not change at all (Glover et al. 2010). Few changes occurred in markers of muscle breakdown in this same human study; quadriceps cross sectional area was significantly reduced by 5.7% (p<0.0001) after the 14 day bed rest. However, the ubiquitin-protein markers were not significantly different after the 14 days of immobility, despite a transient increase at day 2 (Glover 2010).
In contrast to the Glover study, Jones et al assessed candidate genes associated with atrophy and anabolism during 2 weeks of immobilisation followed by 6 weeks of exercise rehabilitation and identified increases in proteolytic pathways (Jones et al. 2004). Quadriceps lean mass was significantly reduced after 2 weeks (4.7%, p<0.001) as was isometric strength (27% reduction, p<0.001). The ubiquitin-proteosome pathway showed
36 increased gene expression for the 20S proteosome α7-subunit by 26% (p<0.05), MAFbx by 62% (p<0.01) and GSK3-α by 28% (p<0.01). There was a reduction in muscle specific calpain 3 by 36%, (p<0.001) compared with basal results (Jones 2004). Remarkably, as soon as the immobilisation period ended and the polyester casts were removed from subjects they were asked to perform a series of maximal intensity contractions and muscle biopsies were then repeated. There were significant reductions seen in atrogenes MAFbx (39%, p<0.01) and MuRF1 (33%, p<0.05) as well as a significant reduction in myostatin expression (48%, p<0.05). Expression of calpain 1 was significantly increased (32%, p<0.05) as was calpain 2 (109%, p<0.001). Suppression of muscle breakdown and stimulation of muscle hypertrophy and remodelling was therefore observed (Jones 2004). After 6 weeks of exercise rehabilitation leg lean mass and leg strength returned to baseline levels (Jones 2004).
Although some studies promote higher protein and energy intakes to counteract losses incurred during critical illness from sepsis, injury and disuse (Ferrando et al. 2010;Mansoor et al. 2007;Paddon-Jones et al. 2004;Stein et al. 1999), others have found that positive energy balance during 35 days of bed rest was associated with greater muscle atrophy (Biolo et al. 2008).
Medical management of the critically ill has fortunately progressed such that patients are kept lightly sedated whilst sitting out of bed in a chair, whilst being mechanically ventilated, is encouraged (Bailey et al. 2009;Kress 2009;Schweickert et al. 2009). Some of the effects experienced from prolonged bed rest may therefore hopefully be reduced.
1.3.10 Nutritional problems on ICU
Poor nutrition on the ICU may contribute to skeletal muscle breakdown. It has been well documented that patients on ICU suffer inadequate feeding (Figure 1-1) mainly secondary to GI problems, ileus or large gastric residual volumes, sedative use, delays with initiation of feeding or interruption to feeding
37 (Heyland et al. 2010;McClave et al. 1999;Montejo 1999;Reid 2006;Wandrag et al. 2011). Negative energy balance has been associated with increased infectious complications (Villet et al. 2005) and may also prolong ICU stay (Dvir et al. 2006). Patients tend to lose a significant amount of weight during their ICU stay (Kvale et al. 2003) and lean depletion makes up a large proportion of the overall weight loss (Plank et al. 1998;Plank and Hill 2003;Reid 2004). Patients with multi-organ failure displayed protein breakdown that by far exceeded any synthesis of protein (Arnold et al. 1993).
Nutritional problems on the ICU are numerous, poor nutrition may exacerbate muscle loss. Monitoring of the nutritional status in this environment is challenging. Few validated techniques to assess change in body composition in this population are available due to fluid accumulation and large daily fluid fluctuation. Equally, performing body composition assessments via computed tomography (CT) or dual energy X-ray absorptiometry (DEXA) scanning is not feasible either in the critically ill.
Nutritional monitoring of these patients is not the only challenge, optimum nutrition support is yet to be defined. The amount of calories critically ill patients should receive to improve outcome remains a topic of much debate (Heyland et al. 2011a;Stapleton et al. 2007). It is known that both under-and overfeeding may be harmful (Reid 2006), however the amount of calories that might be deemed ‘adequate’ is less evident. Patients appear to have improved outcomes when caloric intake is matched to measured energy expenditure, such as lower hospital mortality observed in a group fed to measured energy expenditure (Singer et al. 2011). Indirect calorimetry is the gold standard method for detecting energy expenditure in the ICU, however old equipment, such as the Deltatrac™, is no longer manufactured or serviced by industry. Modern indirect calorimeters have failed to validate against the Deltatrac™ (Sundstrom et al. 2012). For most, access to any measure of indirect calorimetry is near impossible and clinicians therefore rely on predictive equations to estimate energy expenditure. Predictive equations are
38 known to be flawed, they agree poorly to continuous measurement of energy expenditure with the further increased risk of under- and over feeding patients (Reid 2007).
Most commonly permissive hypocaloric feeding is advocated in the obese critically ill patient (Dickerson et al. 2002;Dickerson 2005;Dickerson 2011), although some promote this practice in the non-obese (Arabi et al. 2010). Others suggest that hypocaloric feeding correlates with increased infectious complications with the further difficulty that negative energy balances may be near impossible to reverse during the remainder of the patient’s critical care stay (Villet 2005). The authors of a landmark observational study performed in 2772 critically ill patients oppose the hypocaloric feeding principle completely. In this study patients received an average of 1034kcal/day and an average of 47g protein per day. An increase of 1000kcal/day was associated with a reduced mortality, with 60 day odds ratio for mortality of 0.76 (95% CI 0.61- 0.95), p=0.014. Additionally, a significant increase in the number of ventilator free days was observed when increasing energy intakes to 1000kcal/day, 3.5 ventilator free days (95% CI 1.2-5.9), p=0.03 (Alberda et al. 2009). This effect was only observed in patients with a BMI of < 25 and ≥ 35kg/m2.
Nutritional problems on the ICU extend to assessment and monitoring of protein requirements in the critically ill, with limited validated techniques available in this population (McClave et al. 2009). Similarly, robust techniques to determine protein turnover, such as stable isotope studies, are intricate, costly and mostly reserved for the research arena.
Limited available evidence and poor quality of studies assessing protein requirements make formulation of clinical guidelines unachievable (Hoffer and Bistrian 2012). Guidelines are often based on ‘best practice’ or expert opinion rather than a robust evidence base. The amount, type and dose of protein or amino acid to optimise clinical outcomes remain uncertain (Plank 2013). The American Society of Critical Care Medicine along with the American Society of
39 Parenteral and Enteral nutrition advocate protein intakes of 1.2 - 2.0g/kg per day (McClave 2009) whilst the European Society of Parenteral and Enteral Nutrition give no specific recommendation due to lack of evidence base (Kreymann et al. 2006). Some suggest that ICU patients receive only half of their protein requirement during their ICU stay due to limitations to clinician’s awareness of protein requirements and protein metabolism during critical illness (Hoffer 2012). Guidelines generally point towards giving patients 1.2 - 2.0g/kg/day with a recent study suggesting intakes should exceed 2g/kg/d to improve nitrogen balance (Dickerson et al. 2012).
Allingstrup et al studied 113 severely ill ICU patients and concluded that higher protein and amino acid provision, not energy provision, was associated with lower mortality (Allingstrup et al. 2012). This study was observational in design and prospective randomised controlled trials would need to confirm this finding, it does however seem that after decades of focus on energy provision and energy balance in critical care the focus is shifting more towards adequate protein provision and protein balance.
An area of ICU nutrition that remains highly controversial is that of immunonutrition, where nutrients are added to artificial nutrition with the aim of having immune modulating effects. Popular immunonutrients over the last few decades include arginine (Beale et al. 1999;Beale et al. 2008), glutamine (Andrews and Griffiths 2002;Griffiths 2001;Griffiths et al. 2002), omega 3 fatty acids (Gadek et al. 1999;Pontes-Arruda et al. 2006;Pontes-Arruda et al. 2011), nucleotides and selenium to name a few (Marik and Zaloga 2008). Glutamine was previously advocated in ICU nutrition guidelines in intravenous format (Heyland et al. 2003;Heyland et al. 2004;Kreymann 2006;McClave 2009) as it showed decreased infectious complications (Houdijk et al. 1999), decreased hospital length of stay (Morlion et al. 1996) and improved long term survival for ICU patients (Griffiths et al. 1997). Recent studies point towards glutamine supplementation making no difference to any of the clinical endpoints studied (Andrews et al. 2011;Heyland and Dhaliwal 2013).
40 Additionally supplementation appeared to increase ICU mortality of patients with multi-organ failure (Heyland 2013). It seems that the season for glutamine supplementation in intensive care has come to an end.
And finally, perhaps as the ‘holy grail’ of nutritional care in the critically ill, a notional “nutritional tipping point” (where anabolism is likely to exceed catabolism and nutritional therapy can be targeted) has yet to be identified in this setting. No current studies suggest that patients become anabolic during an ICU stay, current literature suggests that patients remain in a negative nitrogen balance. Previous studies have however mainly used nitrogen balance only, no studies have used a combination of nutritional and inflammatory markers to see if a nutritional tipping point could be identified during an ICU stay. If such a tipping point could be identified nutritional strategies could be targeted at that point to maximise efforts of supporting patients into an anabolic or recovery phase.
1.4 ICU acquired weakness (ICUAW)
Muscle catabolism combined with polyneuromyopathy causes prolonged ICU weakness (Vincent and Norrenberg 2009). Single risk factors identified in the development of ICUAW include disuse atrophy, sepsis, severity of illness, corticosteroid use, hyperglycaemia (Griffiths and Hall 2010), systemic inflammation, multi-organ failure and neuromuscular blocking agents (De et al. 2009;Stevens 2007;Weber-Carstens et al. 2009). Weakness may be caused by polyneuropathy, critical illness myopathy or both, often referred to as a critical illness neuromyopathy (Latronico et al. 2005;Latronico and Guarneri 2008;Weber-Carstens 2009).
An expert group defined ICUAW as a clinically observed weakness where no other cause other than critical illness would be suspected (Stevens 2007). Three further ICUAW subcategories include:
41 Critical illness polyneuropathy (CIP): ICUAW with electrophysiological evidence of axonal polyneuropathy
Critical illness myopathy (CIM): ICUAW with electrophysiological and/or histological evidence of myopathy
Critical illness neuromyopathy (CINM): Electrophysiological and/or histological evidence where CIP and CIM occur simultaneously.
Several limitations hinder the diagnosis of ICUAW, least of all the lack of a gold standard to diagnose ICUAW or its subcategories. Clinical assessments include neurological examination and manual muscle testing by the Medical Research Council (MRC) score which is described further in the methods Chapter (Chapter 2). Although the MRC score is simple to perform it requires full cooperation of the patient as does grip strength assessment with handgrip dynamometry. Poor scores from grip strength assessment may reflect poor cognition of patients recently discharged from ICU rather than an absolute deterioration in functional status.
Lack of agreement over the general terminology used to decribe or define weakness, myopathy and neuropathy in studies limit application of research.
1.5 Critical illness myopathy
Myopathies are likely caused by electrical alterations in the muscle (inactivation of sodium channels), microvascular alterations, altered calcium homeostasis, activation of proteolytic pathways, catabolic hormones and potential glutamine deficiency and antioxidant depletion (Hermans et al. 2008). Critical illness myopathy can be divided into three subgroups as identified by muscle biopsy: diffuse non-necrotising myopathy, acute nectrotising myopathy and thick filament myopathy (Hermans et al. 2009b).
42 Neuro-muscular abnormalities are reasonably common with up to 50% of ICU patients with sepsis, multi-organ failure or prolonged mechanical ventilation affected by this (Stevens 2007). Hermans et al examined interventions proposing to prevent critical illness polyneuropathy and critical illness myopathy in their systematic review (Hermans et al. 2009a). They concluded that intensive insulin therapy reduces the incidence of critical illness polyneropathy and/or critical illness polyneuromyopathy.
Muscle weakness can lead to increased morbidity, duration of mechanical ventilation and ICU length of stay (Hough et al. 2009) and was confirmed to be the single greatest determinant of outcome - where recovery should be measured in months to years rather than days to weeks (Cheung et al. 2006;Herridge 2003).
Research has clearly demonstrated that patients who survive a period of critical illness have persistent muscle weakness and neuromuscular dysfunction which could last years after the original insult (Herridge 2003). Follow up on a group of survivors of the acute respiratory distress syndrome by Herrdige et al has shown limitations to both exercise capacity (six-min walk test) and in health-related quality-of-life (HRQL) 1 year after hospital discharge (Herridge 2003). The six-minute walk test, although impoved over the 12 month period, was still lower than the predicted value for each subject. When assessed again at 5 years it was only 76% of predicted distance (Herridge 2011a), and recovery appeared to have plateaued. Patients reported that muscle wasting and weakness were the main contributory factors. Only 49% of these patients returned to work one year after hospital discharge due to fatigue and weakness. Although HRQL had improved over the 12 month follow up period, scores remained below those of the control population for all domains apart from ‘emotional role’ (Herridge 2003). At 5 year follow up physical component assessment scores were much lower in the patient group than an age and sex matched control group, 41 versus 50 respectively (Herridge 2011a).
43 De Jonghe et al (De et al. 2007) assessed respiratory muscle weakness in 116 mechanically ventilated patients, in particular the correlation of respiratory and limb muscle weakness and the effect thereof on delayed weaning. After one week of mechanical ventilation severe respiratory muscle weakness was identified and this was associated with limb weakness (measured via the Medical Research Council score) and delayed weaning. This was the first study to report an association between the reduction of limb strength and respiratory muscle weakness and delayed separation from mechanical ventilation.
In a group of patients ventilated for 5 days or longer, Ali et al (Ali et al. 2008a) were able to demonstrate that ICUAW (as assessed by MRC sum score and handgrip dynamometry) was independently associated with hospital mortality. Although morbidity effects had previously been described, this was the first study to show a mortality association.
With the level of cooperation required to perform the MRC sum score or handgrip dynamometry, assessments are not easily attained in critically ill patients or in patients with prolonged neurological impairment. Muscle strength testing may therefore be difficult to assess in this context. Investigators have moved towards assessment of changes in muscle depth or circumference as repeat CT or DEXA scanning may not be practical and can be very costly. Ultrasound techniques have been used to determine depth or circumference change of skeletal muscle in critically ill patients (Campbell et al. 1995;Reid 2004;Seymour et al. 2009).
44 1.6 Muscle protein synthesis
A schematic representation of anabolic pathways linked to muscle protein synthesis is seen below, Figure 1-4:
Protein Kinase
Growth Insulin Mechanical factors Amino acids IGF-1 loading
P Akt
P
mTOR
GSK3β S6k 4E-BP1
Protein Synthesis
Figure 1-4: Simplified diagram of anabolic pathways involved with muscle protein synthesis
IGF-1: Insulin-like growth factor 1; Akt: Protein kinase B; mTOR: mammalian target of rapamycin; GSK3β: glycogen synthase kinase 3β; S6k: ribosomal protein S6 kinase; 4e-BP1: eukaryotic translation initiation factor 4E binding protein 1.
(The dotted arrows denote that protein turnover had not yet been demonstrated in ICU patients).
45 1.6.1Signalling pathways and proteins regulating protein synthesis
The main pathway implicated in muscle protein synthesis is the Akt/mTOR signalling pathway. Akt is activated by insulin and IGF-1. An additional pathway involved with muscle protein synthesis includes the Akt/GSK3β pathway (Constantin 2011;Jespersen 2011;Lang et al. 2007;McCarthy 2010). In the few ICU studies where anabolic pathways have been explored results have been surprising. In the first, septic patients displayed significant activation of both Akt and mTOR compared to control subjects, suggesting protein synthesis was enabled and not suppressed as once thought (Jespersen 2011). In the second study, although gene expression for the Akt pathway was significantly higher in ICU patients than controls, the corresponding proteins were not (Constantin 2011). In this study anabolic signalling was reduced in ICU patients compared to control subjects. The activated gene expression may perhaps be related to inhibition of protein synthesis.
Constatin et al and Jespersen et al appear to be the first groups to have identified both the anabolic and catabolic signalling pathways in ICU patients (Constantin 2011;Jespersen 2011).
1.7 Potential strategies to stimulate muscle protein synthesis
In the following section strategies which were studied in the hope of increasing muscle protein synthesis or reducing muscle breakdown in the critically ill are described.
1.7.1 Drugs
A number of potential strategies to promote muscle protein synthesis in the ICU population have been explored (Figure 1-1), some of which have been shown to be harmful e.g. growth hormone administration.
46 Supplemental growth hormone given to critically ill patients increased mortality (Takala et al. 1999). Although earlier studies did not demonstrate harm to patients any positive effects from growth hormone (reduced nitrogen loss) were only very brief (Voerman et al. 1992).
In one study growth hormone combined with insulin-like growth factor 1 and glutamine containing parenteral nutrition was sufficient for ICU patients to be in an overall positive protein balance (Carroll et al. 2004). Some studies suggest improved muscle protein synthesis and reduced lean body mass loss with insulin administration (Jeschke et al. 2004;Thomas et al. 2002). High dose insulin did not appear to limit protein breakdown (Whyte et al. 2010).
In children with severe burn injury Propranolol (a beta-blocker) and Oxandrolone (a testosterone analogue) showed promise in maintaining lean body mass and indeed in reversing some of the muscle breakdown experienced (Demling 1999;Demling and DeSanti 1997;Herndon et al. 2001;Herndon et al. 2012;Jeschke et al. 2007). 10mg of Oxandrolone given to children with severe burn injury every 12h also decreased hospital length of stay (Wolf et al. 2006) and 0.1mg/kg every 12h reduced hospital stay and improved lean body mass (Jeschke 2007).
Propranolol seems to be an effective treatment in children with severe burns to help reduce muscle wasting as demonstrated by stable isotope studies (Herndon 2001). The proposed mechanisms are not clear, however treatment reduces heart rate, energy expenditure, thermogenesis and peripheral lipolysis as well as increasing protein synthesis (Gauglitz et al. 2011).
Herndon et al studied 25 children with severe burns in which 13 were randomised to receive propranolol and 12 remained untreated as control subjects (Herndon 2001). Propranolol treatment decreased energy
47 expenditure, 1321 (SE: 173) kcal in the treatment group versus 1670 (SE: 221) kcal, p<0.001 in the control group. Treatment furthermore impoved protein turnover kinetics and lean mass loss, 9% in control patients and 1% in the Propranolol group, (p=0.003). Beta-blockade appeared to decrease catecholamine activity which in turn might explain the improvements observed in protein kinetics.
Jeschke et al studied 235 children with severe burn injury and randomised them (4:1) to standard burn care (n=190) or Oxandrolone in addition to standard burn care (n=45) (Jeschke 2007). Oxandrolone treated patients had a significantly shorter ICU stay compared to control subjects, 0.56 (SEM:0.02) versus 0.48 (SEM:0.02) day/% (p<0.05) when length of stay was normalised to total body surface burn area. There were no significant differences between groups for REE assessments. Control patients lost between 9-10% lean body mass during the study period whilst Oxandrolone treated patients demonstrated an increase in lean body mass by 8% (p<0.05). Mean muscle strength, although decreased in both groups, was significantly higher in the treated group than in control patients, 55 Newton Meter versus 33 Newton meter (p<0.05). Oxandrolone treatment therefore appeared to reverse catabolism in children suffering from severe burns.
No studies were found where Propranolol treatment were given to adult critically ill patients to assess changes in lean body mass or protein kinetics.
Anabolic steroids such as testosterone do not seem to have a role in preventing or treating muscle breakdown in the critically ill (Chang et al. 1998;Gauglitz 2011;Spratt 2001).
1.7.2 Dietary manipulation
The role that amino acid supplementation may have in promoting muscle protein synthesis in the critically ill requires further investigation. Numerous protein and amino acid preparations have been studied over the past decades with varying levels of success. Historically when branched-chain amino acids
48 (BCAA) were studied in the ICU population, studies often failed to address baseline nutrition. Furthermore the ratio of BCAA (valine: leucine: isoleucine) of 1:1:1 used now appears sub-optimal (Cynober and Harris 2006). Interestingly when plasma levels of amino acids were assessed, total plasma amino acids and plasma EAA levels were not different between ICU patients and controls, however both leucine and valine levels were lower, 22% (p=0.08) and 21% (p<0.05) repectively (Jespersen 2011).
Protein losses were halved when trauma and sepsis patients’ protein intake increased from 0.9 to 1.2g of protein/kg/day (Ishibashi et al. 1998). No improvements were found beyond this amount which could indicate that the quality of nitrogen supply warrant further investigation. Enteral supplementation of the amino acids cysteine, serine, threonine and aspartate have been shown to partially restore muscle protein synthesis in polytrauma patients on ICU (Mansoor 2007) and a derivative of the amino acid leucine, Beta-hydroxy-B-Methylbutarate (HMB), has shown improved nitrogen balance in ICU trauma patients (Kuhls et al. 2007).
Essential amino acid (EAA) supplementation appears to provide a potent anabolic stimulus and improved muscle protein anabolism after a cortisol infusion was given to healthy subjects to simulate a stress response. This form of supplementation may help to minimise muscle loss following debilitating injury (Paddon-Jones et al. 2003b). The EAA supplement was created to mimic the proportion of EAA found in muscle since it was thought that this would stimulate muscle protein synthesis more than normal food or whole protein. EAA supplementation showed an increase in muscle protein synthesis and with repeated stimulation this might be translated into maintenance of muscle mass (Ferrando et al. 2006b). The branched-chain amino acid leucine in particular is known to promote muscle protein synthesis by stimulating the mTOR signalling pathway (Rennie et al. 2006). A metabolite of leucine, beta-hydroxy-beta-methylbutyrate (HMB), has shown some
49 promise in restoring lean body mass in cachectic patients (Clark et al. 2000;May et al. 2002).
Four ICU supplementation studies focusing solely on enteral amino acid and amino acid metabolites in the critically ill have been identified to assess what effect supplementation may have on muscle wasting. This was conducted as part of a systematic review I performed (not published yet) where study quality was assessed following GRADE methodology (Atkins et al. 2004). One study was classed as a high quality study, two as moderate quality and one as low quality, Table 1-2.
High quality study
Kuhls et al studied 100 trauma ICU patients comparing HMB supplementation (3g HMB), HMB with additional arginine and glutamine (HMB+; 3g HMB, 14g L-arginine, 14g L-glutamine) or an iso-caloric, iso-nitrogenous placebo product (Kuhls 2007). The study aim was to assess the effect on nitrogen balance and muscle breakdown. The findings indicate that nitrogen balance improved with HMB alone. The mean nitrogen balance was -9, -10.9 and -6.5g/day for the control, HMB+, and HMB groups respectively (p=0.05). Interestingly the HMB+ group (with additional arginine and glutamine in the formula) appeared to remain in a more negative nitrogen balance throughout the study, suggesting a possible detriment to added arginine or glutamine. The 3-MH and urinary nitrogen results suggest that supplementation did not affect muscle breakdown in any group. The main significant effect from HMB compared to both control and HMB+ patients appeared to be in the positive nitrogen balance observed over the 14 days study period, -9, -10.9 and - 6.5g/day for the control, HMB+, and HMB groups respectively (p=0.05). There were no significant differences between groups for mortality, 28 day antibiotic use, inflammatory markers, hospital and ICU length of stay, ventilator days and number of new infections. This was a well conducted study which followed a randomised, double-blinded and placebo-controlled design. The authors provided baseline nutrition at 25kcal/kg and 1.5g protein/kg) via
50 standard enteral feeding with protein powder additions for patients with higher protein requirements.
However, the study did have some important limitations. Although a sample size of 100 was reported, only data for 72 patients were analysed, leading to a potential type II error. Twenty-eight subjects were excluded from statistical analysis as they did not meet 50% compliance for supplementation. Additionally 3-MH was used as a sole marker for muscle proteolysis despite some of its known limitations as a biomarker (i.e. not a sensitive marker of specific tissue pools), whilst pre-albumin was used as a surrogate for protein synthesis. Furthermore, it was not clear whether urinary nitrogen was measured in the laboratory or calculated by formula using urinary urea.
Despite the limitations HMB supplementation appears to be a promising dietary supplement to improve nitrogen balance in severely injured trauma patients, and the addition of arginine and/or glutamine appears to neutralise any benefit of HMB alone.
Moderate quality studies
Twenty eight ICU patients were given EAA (containing 6 times the amount of glutamine that was present in the control formula) or non-EAA supplements with the aim of comparing plasma amino acid differences, nitrogen balance was also assessed (Jensen et al. 1996). Baseline nutrition was provided at 30-35kcal/kg/day, however no mention was made of protein requirement. The authors found no clinical benefit with enteral glutamine-enriched EAA supplementation (no difference found in immune function, plasma amino acid levels or in nitrogen balance). Although this study received a moderate grading due to block randomisation, double blinding and placebo-controlled design it had several limitations. Limitations include small sample size with lack of statistical power, high dropout rate (only 19 patients were analysed), unclear overall aims, unmatched differences between groups (age and BMI both higher in the experimental group) and a heterogeneous ICU population (majority trauma patients, however not all).
51 Mansoor et al performed a study in 12 trauma ICU patients where six patients were randomised (Mansoor 2007) to threonine, serine, cysteine and aspartic acid supplementation (AA patient group) and six to alanine supplementation (Ala patient group) to determine effects on protein metabolism. The two ICU groups were also compared to healthy control subjects (n=8). This unusual mixture of amino acids (AA group) was selected based on the authors’ previous work demonstrating a demand for these particular amino acids in septic rats (reduced weight loss, muscle catabolism and an enhanced recovery) (Breuille et al. 2006). The intervention and control supplements used in ICU patients were isocaloric and isonitrogenous providing 34kcal/kg/d and 1.55g protein/kg/d. Outcome measures included plasma amino acids, cytokines, nitrogen balance, protein turnover, muscle breakdown via 3-MH, muscle biopsies and indirect calorimetry. In the ICU patients no significant differences between the two groups (AA and Ala) were found in either plasma protein or whole body protein kinetics. Healthy controls remained in positive balance for protein kinetics.
Nitrogen balance remained negative in both ICU patient groups after 10 days with no significant differences observed between the two groups (-35g/kg control -32g/kg intervention, p>0.05). This particular blend of amino acids did not change leucine kinetics between the two ICU patient groups. Muscle protein synthesis was significantly higher in the healthy control subjects than the two ICU patient groups, 2.2%/d in healthy controls versus 1%/d in Ala patient group and 1.5%/d in the AA group (p<0.05). There was a trend towards increased muscle protein synthesis in the AA group compared to the Ala group, 1.5%/d versus 1%/d (p=0.065). There were no significant differences between the two ICU patient groups for 3-MH excretion. This study was limited mainly by the small sample size, lack of statistical power and short interventional period. Nutrition support was commenced in parenteral format prior to randomisation to enteral supplementation (day 3) which is not standard practice.
Neither of the two moderate quality studies described demonstrated beneficial effects (muscle preservation or improved nitrogen balance) from EAA or mixed amino acid supplementation in the critically ill.
52 Low quality study
The final ICU amino acid supplementation study included 34 ventilated COPD patients given 3g/d HMB supplementation over 7 days to examine what effects HMB might have on inflammation, protein metabolism and pulmonary function (Hsieh et al. 2006). The HMB supplement was given in addition to standard enteral feed. The group reported significant reductions in CRP from baseline in the HMB group and not the control group - in the HMB group 111.56 (SD: 91.47) at baseline versus 46.19 (SD 45.29) mg/L afterwards (p<0.05); in the control group 110.59 (SD: 81.35) at baseline vs 72.65 (SD: 64.72) mg/L afterwards. For WCC there was also a significant reduction in the HMB group but not in the control group compared to baseline - in the HMB group 14 (SD: 6.6) at baseline versus 9.64 (SD: 3.19) 103/mm3 (p<0.01); in the control group 12.21 (SD: 4.04) at baseline versus 11.10 (SD: 5.17) 103/mm3 afterwards. A non-significant reduction in blood urea nitrogen (BUN) was observed in the HMB group (p=0.079). No changes were reported in body weight between groups after the study period. The authors report anti- catabolic effects however protein metabolism was not adequately assessed in this study. This study was limited by small numbers with lack of statistical power analysis, a short interventional period, and a lack of true randomisation, subjects were only divided into groups by assigning patients in alternate order of their ICU admission. No placebo product was used, with control subjects received standard feeding only. Thus, although this data again points to the potential benefits of HMB, the design flaws limit the value of the data.
From these ICU amino acid supplementation studies HMB supplementation appears to be the most promising dietary intervention tested to date to improve nitrogen balance in severely injured trauma patients.
Dietary supplementation studies in the critically ill are mostly limited by lack of statistical power, small sample size, short interventional study periods, lack of robust markers for protein metabolism, muscle mass and body composition measurements.
53 Table 1-2: Amino Acid Supplementation Studies in ICU
Study Quality Markers Population Intervention Comparison Outcome measures Outcomes
Kuhls et al, High N = 100 1: 3g HMB (N=28) 3.Placebo with 24h urine collection Urinary excretion of N2: n/s difference between groups (p=0.23) 2006 trauma ICU standard feed Randomised patients 2:3g HMB+ (with over 14 days 3-MH (muscle Nitrogen balance was affected by Rx (p=0.05) Arg, Gln) (N=22) with 28d follow breakdown) Double-blinded N=72 analysed up (N=22) Mean N2-bal (g/d): -9 Controls With standard Pre-albumin Placebo feed. (0.104MJ/kg -10.9 HMB+ group or 25kcal/kg and ICU outcomes Controlled 1.5g protein/kg). -6.5 HMB group Feed tolerance Power calculation 14 days HMB+ group more negative N2 bal than HMB group (p < 0.05). included (N=100) Antibiotic days with 28d follow up No Rx difference for mortality, 28d antibiotic use, LOS, Infections ICU LOS, ventilator days, or no of infections.
N/s change in pre-albumin, CRP or IL-6
Jensen et al, Moderate N=28 EN with EAA (6x EN with non- CRP, TLC, Alb No difference: Albumin, pre-albumin, N2 balance or 1996 gln amount) EAA instead of Block randomised ICU patients extra Plasma AA energy expenditure (18-75y) (10 days) glutamine Double blinded Indirect Calorimetry BCAA ratio: Placebo N2 balance val:leu:iso of 1:4:1 Controlled EN provided at Small (N=19 analysed) 0.125-0.146MJ) (30-35kcal/kg).
54
Study Quality Markers Population Intervention Comparison Outcome measures Outcomes
Mansoor et al, Moderate N=12 N=6 Feed with N=6 Feed with Plasma proteins. AA did not change plasma protein synthesis nor whole body 2007 Threonine, Alanine (Ala Block randomised Trauma ICU Serine, Cysteine, group) TNF-a, IL1-b, IL-6). protein synthesis. patients Aspartic acid (AA Double blinded group) (10 days) Protein turnover. General negative N2 bal in both groups, no significant differences
Controlled Feed: Same kcal & N2 balance, 3-MH between Ala or AA groups. nitrogen Small N=12 EN of 0.146MJ/kg Muscle biopsies. Leucine kinetics:↑in both ICU patient groups compared to the control + 8 Healthy Short study (35kcal/kg) and controls (on Indirect Calorimetry (p<0.05). 1.55g prot/kg meat free controlled diet) Trend towards ↑muscle protein synthesis in AA group compared to Ala group, 1.5%/d versus 1%/d (p=0.065).
No significant differences 3-MH excretion.
Hsieh et al, Low N=34 3g HMB No CRP, WCC. Significant ↓ in CRP (p<0.05) & WCC (p<0.01) in HMB group. 2006 ventilated supplement via supplement Not randomised COPD patients NGT (1.5g bd) over 7 days BUN, s-creatinine ↓ BUN (n/s) over 7 days. Blinded Lung function HMB group: Significant ↓ in creatinine (p<0.05) No mention of Controlled composition of Body weight standard enteral No Placebo feed Small sample
Alb: Albumin; AA: Amino Acids; Arg: Arginine; BUN: Blood Urea Nitrogen; COPD: Chronic Obstructive Pulmonary Disease; CRP: C-Reactive Protein; HMB: β-hydroxy methylbutarate; EN: Enteral
Nutrition; Gln: Glutamine; IC: Indirect Calorimetry; IL: Interleukin; LOS: Length of stay; 3MH: 3 methylhistadine; N2 Bal: Nitrogen Balance; NGT: Naso gastric tube; N/S: Non-Significant; REE: Resting Energy Expenditure; TNF-a : Tumor Necrosis Factor alpha; WCC: White cell count.
55 1.7.3 Electrical muscle stimulation
A further strategy proposed to maintain muscle mass in the critically ill includes electrical muscle stimulus (Gerovasili et al. 2009;Zanotti et al. 2003), Figure 1-1. Electrical muscle stimulation (EMS) has shown initial promise in terms of preservation of muscle mass (Gerovasili 2009;Routsi et al. 2010).
Gerovasili et al studied 26 mechanically ventilated patients randomised into the EMS group (n=13) or control group (n=13) (Gerovasili 2009). Electrical muscle stimulus was applied daily from day 2-9 over the quadriceps and peroneous muscles of both legs. The control group received no EMS. Muscle mass was assessed by ultrasound (cross sectional diameter of rectus femoris- vastus intermedius) at baseline and 7 days afterwards. There were no differences at baseline between the groups. Although muscle loss occured in both groups, the EMS group displayed significantly lower rates of wasting in the right rectus femoris, EMS group -8 (SD: 3.9)% compared to controls -13.9 (SD:6.4)%, p=0.009, and right vastus intermedius, EMS group -12.5 (SD:7.4)% versus -21.5 (SD:15.3)%, p=0.034. On the left side the diameter was less in the EMS group versus control patients, however statistical significance was not met. Sample size in this study was small, however EMS seemed to preserve lower extremity muscle mass in ICU patients.
Gruther et al studied 46 ICU patients in a pilot RCT where patients were stratified into 2 groups, one as an acute immobilisation group (defined as < 1 week from ICU admission to EMS treatment) and the second as a long term immobilisation group (defined as > 2 weeks from ICU admission to EMS treatment). Within these two groups patients were randomised to EMS or sham treatment, EMS was provided over the quadriceps muscle bilaterally (Gruther et al. 2010). Thickness of quadriceps muscle was measured on ultrasound. In the acute group muscle thickness was not significantly different between EMS and control patients at baseline, 28.9 and 32.9mm respectively 56
(p=0.457). Both groups in the acute patient group had significant reductions of muscle thickness after 4 weeks, 18.3mm in the EMS group (p=0.002) and 20.1 in the control group (p<0.001) versus baseline. In the long term immobilisation group the EMS group showed a significant increase in muscle thickness from baseline, 18.4 to 19.3mm (p=0.036) whereas control patients did not demonstrate a significant increase in muscle depth, 18.6 versus 18mm (p=0.162). Although this is a small pilot study evidence suggests that EMS might improve muscle thickness of long term ICU patients. Muscle strength was not assessed and it therefore remains to be seen whether increased muscle thickness leads to improved strength and function.
Routsi et al were the first to assess whether EMS treatment could affect muscle function in ICU patients (Routsi 2010). Stimulus was given daily to the interventional group (within 48h of ICU admission) to quadriceps and peroneous muscle. EMS lasted for 55 min per day until ICU discharge. Manual muscle strength testing was performed by two independent investigators. A sample size calculation was performed (N=44), although 70 patients were recruited to the two groups, 28 (40%) died in the EMS group with a further 11 (16% ) unable to perform the MRC sum score. In the control group 22 (30%) patients died with 22 (30%) unable to perform the MRC functional assessment. The final analysis included 24 EMS and 28 control patients. Critical illness polyneuromyopathy (CIPNM) was diagnosed in 3 (12.5%) of the EMS group versus 11 (39.3%) in the control group (OR 0.22, 95%CI of 0.05 to 0.92, p=0.04). The MRC sum score was significantly higher in the EMS group compared to the control group, 58 (IQR: 33-60) and 52 (IQR:2-60) respectively, p=0.04. Limitations of the study include not using sham EMS in the control group and the small sample size. With patient exclusions due to death and inability to perform strength testing, the study was underpowered. Despite the limitations, 55 minutes per day of EMS appeared to prevent development of CIPNM in critically ill patients. This is the first study in which function was assessed post EMS administration in the critically ill, larger RCTs will be required to confirm these findings.
57 In a post-hoc analysis of the Routsi study, Karatzanos et al assessed whether EMS affected strength of various muscle groups in ICU patients (Karatzanos et al. 2012). Stimulated patients scored significantly higher than control patients for MRC scores in wrist flexion, hip flexion, knee extension and ankle dorsiflexion of both sides. No difference in handgrip strength was observed, however a significant difference was found in handgrip strength between patients diagnosed with ICUAW in comparison to those without, 6.6 (SD:4.4) kg versus 23.4 (SD:8.9) kg, p<0.01.
Some studies indicate no difference in muscle volume post EMS (Poulsen et al. 2011). Poulsen et al studied 8 male ICU patients with septic shock, patients were immobile for a median of 3 days (1 - 4) prior to entry into the study. Electrical muscle stimulus was applied for 60min per day to the quadriceps muscle of one leg, patients served as their own controls. Muscle volume was assessed at baseline and 7 days later via CT. The non stimulated leg decreased by 16% over 7 days (IQR: 4-21%) from baseline, p=0.03 or 2.3% per day. The stimulated leg decreased by 20% (IQR: 3-25%) from baseline, (p=0.04) or 2.9% per day. There was no significant difference between the stimulated and unstimulated leg, p=0.12. This study was limited by the small sample size.
Large prospective studies are required to evaluate the exact role of electrical muscle stimulus in terms of muscle strength preservation.
One such study currently underway, the early rehabilitation in critical care (eRiCC) study, will compare functional electrical muscle stimulation-cycling to cycling alone and to standard physiotherapy (Parry et al. 2012).
1.7.4 Early mobilisation
Studies looking at early mobilisation of ventilated patients have demonstrated feasibility although prospective studies will be required to evaluate the impact of such strategies on muscle mass per se as well as potential improvements
58 in functional recovery and strength (Bailey 2009;Kress 2009;Schweickert and Kress 2011).
1.8 Anabolic resistance
Although various strategies to promote muscle protein synthesis have been discussed in Section 1.7 ‘anabolic resistance’ may inhibit some of these. Anabolic resistance has been described as the inability of muscle to retain its mass due to inadequate muscle protein synthesis in addition to enhanced muscle protein breakdown (Rennie 2009). This may largely be due to older muscle’s resistance to insulin and a decreased ability with age to assimilate amino acids from blood into muscle thereby reducing muscle protein synthesis. Age along with disuse atrophy appear to be the main contributors to anabolic resistance (Rennie 2009). It is currently not clear what affect ‘anabolic resistance’ might have on critically ill patients.
1.9 Nutritional Tipping Point
Attempts have been made in the past to identify a type of “nutritional tipping point”. Such a point being an identifiable moment when a patient transitions from a state dominated by catabolism, to one characterised by anabolism. A previous study described the use of a prognostic inflammatory and nutritional index (PINI) which might provide some indication of when patients progress from an ongoing catabolic state to that of anabolism (Ingenbleek and Carpentier 1985). The index was developed to evaluate nutritional status and to improve diagnosis and monitoring of critically ill patients in terms of morbidity and mortality. This index was rarely used in the years following publication as α1-acid glycoprotein and pre-albumin were too difficult to measure. Decades later these two proteins are still not routinely measured in most hospitals. The original formula consisted of measurement of CRP, orosomucoid (or α1-acid glycoprotein), albumin and pre-albumin (or
59 transthyretin). Multivariate analysis of 11 serum proteins had been performed to identify these four markers with the highest sensitivity and specificity.
The PINI consists of the following equation:
PINI = [orosomucoid (g/L) x CRP (mg/L)]/ [Albumin (g/L) x transthyretin (g/L)]
As the PINI correlated well with clinical outcome of the patients studied, stratification was performed by risk of complications or death (Ingenbleek and Carpentier 1985):
> 30: Life-risk patients
21-30: High-risk patients
11-20: Medium-risk patients
1-10: Low-risk patients
< 1: Non-infected subjects
1.10 New PINI
Recently, and in view of the difficulty in obtaining α1-acid glycoprotein measurements, a ‘new PINI’ has been identified (Ziegler et al. 2011). Alternative biological formulas were compared in a prospective study from plasma samples of 106 patients in intensive care, surgical and medical wards. In the new formulas, the logarithm of CRP was used to account for the enormous potential daily fluctuation in CRP and to therefore avoid overestimation of mortality and morbidity risk according to the original PINI formula. Six newly proposed formulas were compared and correlated with the serum protein values obtained from patients using the original PINI. The six
60 formulas included were: Log (CRP)/ (Alb x pre-albumin); Log (CRP) / pre- albumin; CRP/Albumin; CRP / (Albumin x pre-albumin); CRP/ pre-albumin and Log (CRP) / Albumin. Correlation coefficients ranged between 0.78 and 0.94, p<0.0001, however the last three formulas mentioned correlated best with the original PINI with r= 0.94, r=0.90 and r=0.91 respectively (Ziegler 2011).
From the six formulas, the Log (CRP)/ Albumin is of particular interest because of its ease of use with readily available markers in the hospital environment. Log (CRP)/Albumin correlated well with the original PINI formula with r=0.91.
This new simplified PINI formula is used in this research (Chapter 4).
This research aimed in the first instance to identify a notional nutritional tipping point, where anabolism is likely to exceed catabolism and nutritional therapy can be targeted. Nutritional problems on ICU have been highlighted (Section 1.3.10), targeting nutritional strategies in the very acute catabolic phase may be counter-productive, however if a nutritional tipping point could be identified nutritional strategies could be targeted at that stage to maximise efforts of supporting patients into an anabolic or recovery phase. A further aim of this research was to evaluate the feasibility of using an essential amino acid supplement to attenuate muscle wasting in critically ill trauma patients.
1.12 Hypotheses
1.12.1 Main hypothesis
Understanding the relationship between inflammatory and nutritional markers of critically ill patients will help to identify a point where patients become anabolic and will assist in the development of a strategy to minimise muscle mass decline in critical illness and facilitate recovery.
61 1.12.2 Secondary hypothesis
Timing interventions in the knowledge of a notional nutritional tipping point will assist in optimising recovery.
1.12.3 Research aims
Study 1
a) To identify a nutritional tipping point where patients move from catabolism to anabolism during a period of critical illness.
b) To explore inter-relationships between inflammatory and nutritional markers during critical illness.
c) To explore data for surrogate endpoints to be used in future nutritional clinical trials where effect size and sample size calculations could be determined from our current data set.
Study 2
a) To determine whether the trauma population represent a feasible group to study within critical care.
b) To determine whether the intervention (Leucine enriched essential amino acid supplementation) is feasible to study within critical care.
c) To assess whether the outcome measures and endpoints used in this study would be feasible (practical and scientifically sound) for use in future clinical trials.
d) To develop a strategy that will minimise muscle mass decline in critical illness.
62 Chapter 2. Core Methods
In this chapter the core research methods used in my PhD research project has been outlined. The majority of the research methods described were used in both the Nutritional Tipping Point (chapter 4) and the essential amino acid supplementation (chapter 5) studies of this research. Stable isotope studies were performed during the amino acid supplementation study only. Quantitative methods only have been used throughout this research project.
2.1 Cytokines
2.1.1 Sample preparation and storage
14ml of arterial blood was taken from each study patient by the researcher, research nurse or bedside nurse. Blood was taken in EDTA (Ethylenediaminetetraacetic Acid) tubes. Samples were put on ice immediately and spun at 3000 rpm for 10 minutes. Aliquots of between 6-8 plasma samples of 1ml each were prepared in screw capped micro centrifuge freezer tubes. Tubes were clearly labelled with each patient’s unique study number and the sampling date. Samples were stored at -80°C in a locked freezer in the dedicated ICU laboratory area at each of our hospital sites. Locked freezers were fitted with an alarm system linking to staff emergency telephone numbers should the alarm sound. A grid labelling system was kept as a duplicate electronic record of where samples were stored in the -80°C freezer and in which boxes samples would be found to avoid confusing patient samples later. Cytokine analysis of plasma samples were performed via enzyme-linked Immuno-sorbent Assay (ELISA) in batches by Ms. May To, research assistant, under the supervision of Dr Fred Tam, Imperial College London, Hammersmith Hospital Campus.
63 2.2 Enzyme-linked immuno-sorbent assay (ELISA)
2.2.1 Materials and chemicals
ELISA grade streptavidin HRP (R&D systems, Oxford, UK).
2.2.2 Cytokines
Human IL-10 and IL-6 duosets were obtained from R&D systems, Oxford, UK. These were stored between 2-8°C as per manufacturer’s guidance.
2.2.3 Standard solutions and buffers
Phosphate buffered saline (PBS)
10mM sodium phosphate, 0.9% sodium chloride (Gibco, UK) or PBS – 8g/L
NaCl, 0.20g/L KCl, 1.44g/L Na2NPO4.2H2O (Di sodium hydrogen phosphate),
0.20g/L KH2PO4 (Potassium di-hydrogen phosphate), pH 7.2-7.4, 0.2 µm filtered.
Wash Buffer
0.05% Tween® 20 in PBS, pH 7.2-7.4
Reagent Diluent
1% BSA in PBS, pH 7.2-7.4, 0.2 µm filtered
Substrate Solution
1:1 mixture of Colour Reagent A (H2O2) and Colour Reagent B (Tetramethylbenzidine)
The following formula was used to calculate the capture antibody, detection antibody and standards aliquot concentrations and volumes
C1V1 = C2V2
64 2.2.4 Capture antibody
Capture antibody was provided from the manufacture as 360ug/mL of mouse anti-human IL-10. This was reconstituted with 1.0mL of PBS to produce a working concentration of 2ug/ml. One micro plate requires a volume of 11ml:
360ug/ml x V1 = 11ml x 2ug/ml
Rearranging the formula V1 = (11ml x 2ug/ml) / 360ug/ml
V1 = 0.06ml or 61.11ul
2.2.5 Detection antibody
Detection antibody: 27ug/ml was provided. This was reconstituted with 1ml of reagent diluent. To achieve a working dilution of 0.15ug/ml for one microplate that requires a volume of 11ml:
27ug/ml x V1 = 11ml x 0.15ug/ml
Rearranging the formula V1 = (11ml x 0.15ug/ml) / 27ug/ml
V1 = 0.06ml or 61.11ul
2.2.6 Standards
Standard: 95ng/ml was provided. This was reconstituted with 0.5ml of reagent diluents to achieve a working dilution of 2ng/ml for one microplate that requires a volume of 11ml.
95ng/ml x V1 = 11ml x 2ng/ml
Rearranging the formula V1 = (11ml x 2ng/ml) / 95ng/ml
V1 = 0.23ml or 23ul
Serial dilution of the standard (Figure 2-1)
65 A seven point standard curve using 2-fold serial dilutions in reagent diluent is required. The highest concentration of the standard was 2000pg/ml and the lowest 0pg/ml. To achieve standard dilutions eight 1.5ml micro centrifuge tubes were required.
Figure 2-1: Serial dilution of the standard
2.3 Interleukin-10 and IL-6 human duoset protocol
The IL-10 and IL-6 human duoset protocol was followed using the manufacturer’s instructions, see Appendix.
66 This process is summarised in a simplified diagram for IL-10 analysis, Figure 2-2.
Figure 2-2: Simplified diagram of ELISA steps for IL-10 analysis
2.4 Calculation of results
Duplicate readings for each standard and sample were averaged and the average zero standard optical density was subtracted. A standard curve was created by reducing the data using computer software capable of generating a four parameter logistic curve-fit. If samples have been diluted, the concentration read from the standard curve had to be multiplied by the dilution factor. The standard curve optical density ranged from 0.095 - 1.437 nm.
67 2.5 Assay reliability
2.5.1Interleukin-10 intra-assay reliability
Intra-assay reliability was determined in 7 samples. Coefficient of variation ranged between 0.64 - 9.78%.
2.5.2 Interleukin-10 inter-assay reliability
Inter-assay reliability was determined in 7 samples. Coefficient of variation (CV) ranged between 1.93 - 39.64%.
2.6 Interleukin-6 preparation method
A similar technique has been followed for IL-6 analysis. With serial dilution the highest concentration of the standard was 600pg/ml and the lowest was 0pg/ml. The standard curve optical density ranged from 0.04 - 3.71nm.
2.6.1 IL-6 intra-assay reliability
Intra-assay reliability was determined in 5 samples with CV ranging between 3.31 and 7.15%.
2.6.2 Il-6 inter-assay reliability
Inter-assay reliability was determined in 5 samples, CV ranged between 0.65 and 28.36%.
68 2.7 Urinary studies: 3-methylhistidine and urinary urea
Non-preservative containers were used for 24h urine collection, all patients were catheterised. Twenty four hour urine collections were performed on days 1, 3, 7, 14 and 20 of the study. Containers were labelled with patient information along with the date, start and end collection times of the 24h urine collection. Nursing staff were trained on the correct collection procedure and a written collection protocol was supplied to bedside nurses. Samples were analysed for urinary urea (mmol/24h) and urinary 3-methylhistidine (3-MH) (µmol/24h), which was used as a surrogate marker of skeletal muscle breakdown. A JEOL amino acid analyser with cation exchange chromatography (JEOL UK Ltd., Herts, UK) was used to analyse samples with ninhydrin detection and one inferred standard. Urine urea was measured by a kinetic urease using an Abbott Architect assay with Abbott reagents. Reference ranges for urinary urea and 3-MH per 24h urine sample are not provided in the laboratory.
Urinary 3-MH has been described as a useful marker of skeletal muscle breakdown, particularly if a diet is meat free (Elia et al. 1981). When meat and chicken is consumed urinary excretion of 3-MH will be enhanced as meat is an exogenous source of 3-MH. As ICU patients are artificially fed and artificial nutrition support is meat-free the dietary effect on 3-MH is therefore bypassed. 3-Methylhistidine is derived from the contractile proteins actin and myosin. A linear relationship between protein breakdown, assessed by 15N-glycine stable isotope studies and 3-MH excretion, has been reported (Lowry et al. 1985). Although a number of newer methods have been described to determine muscle breakdown more accurately than 3-MH, such as microdialysis techniques, (Chinkes 2005;Tesch et al. 2008) 3-MH urinary analysis only has been used in this research project. Urinary 3-MH sampling remains popular in studies assessing muscle breakdown within the critical care context (Kuhls 2007;Mansoor 2007;Nissen et al. 1996;Stein 1999;Voerman et al. 1995).
69 2.8 Nitrogen balance
Nitrogen balance is described as the difference between nitrogen input (diet, enteral or parenteral nutrition) and nitrogen loss. Nitrogen loss includes urinary losses and additional losses via skin, faeces, dialysate, fistulae or abdominal wounds.
Urinary nitrogen was not measured directly in this research as this analysis was not feasible in our institution and financial constraints did not allow for samples to be measured elsewhere. Urinary nitrogen therefore had to be calculated. Numerous equations are available to calculate nitrogen loss and nitrogen balance, Table 2-1 shows some of the equations used. Many of the references for the equations are old and use various assumptions, some are poorly referenced making overall interpretation of nitrogen loss difficult. For the purpose of this research both the Deacon equation (Deacon et al. 2009) and the Parenteral and Enteral Nutrition Group of the British Dietetic Association equations were used, Table 2-1. The Deacon equation was chosen as this equation was used in our laboratory and published in the United Kingdom and the PENG equation was recommended by the British Dietetic Association, this equation would be used by dietitians in clinical practice within the United Kingdom.
70 Table 2-1: Nitrogen balance equations
Source Source Equation (N Bal g/day) Equation Reference author
Allan Deacon, Calculations in Nin- Nout: Nitrogen excretion (Deacon 2009) Roy Laboratory Science (g/24h) = urea excretion Sherwood, 2009 (ACB Venture (mmol/24h) x 0.028 James Publications, ISBN Hooper 9780902429437) 20% for other urinary losses and a further 2g/day for losses by other routes.
British Dietetic Parenteral & Enteral N in – N out (Lee and Hartley 1975) Association Nutrition Group (UK) N out: urinary urea (mmol/24h) x 0.033 + obligatory losses. 2- 4gN/d (hair, skin, faeces)
0.6gN per 1 degree above 37.5
(Voerman Annals of Surgery N out (g/d) + change in serum Equation not referenced 1992) 1992;216(6):648-655 urea (mmol/L/d) x 0.019 x body weight
(McCowen et Crit Care Med N in – N out (N out: urine urea (Mackenzie et al. 1985) al. 2000) 2000;28(11):3606 N 24h) – 4g (non-urea nitrogen & renal losses correction factor)
(Dickerson et Nutrition 2005; 21 N in – urinary urea (g/d) – 4 (Blumenkrantz et al. 1980) al. 2005) (3): 332 Serum urea > 5mg/DL added to N losses
(Grimble et al. JPEN 1988: 12: 100- N in - [Urine urea Measured in patients 1988) 6. nitrogen/0.85]
(Japur et al. J Crit Care 2010 N Bal=N in – N out (Blackburn et al. 1977) 2010) N Losses = total urinary nitrogen + 4g/d, (skin and faeces). NB is negative if <0 and positive if > 0
71 2.9 Muscle depth change with ultrasound
Muscle thickness measurements by ultrasound has been previously validated in critically ill oedematous patients (Campbell 1995). The muscle depth that correlated best with lean mass from Dual-Energy X-ray Absorptiometry (DEXA) was measured over the bicep, the anterior forearm and the anterior thigh on ultrasound.
In this research muscle thickness was measured with either a Sonosite M Turbo or a Sonosite S Series™ ultrasound machine with a 5 MHz linear array transducer (Sonosite Ltd, Hitchin, Herts, UK) during the Nutritional Tipping Point study and reliability studies. During the amino acid supplementation study a Sonosite Micromaxx ultrasound machine was used with a 5 MHz linear array transducer (Sonosite Ltd, Hitchin, Herts, UK). Maximum transducer depth was selected at 6cm. Participants were supine and measurements were made on the right side of the body, unless access lines or limb injury determined a different position. Measurements were made with a tape measure on the bicep, forearm and thigh to mark a halfway point on the limb from which the ultrasound measurement would be made, following the protocol of Campbell et al and Reid et al (Campbell 1995;Reid 2004). Markings were made in indelible ink to ensure that the same site would be measured throughout the patient’s ICU stay. Three ultrasound measurements were made per site using the built-in electronic calliper on a frozen real-time cross-sectional image. The average of three measurements for each site was used, up to a 0.2cm difference was accepted. The mean value from the bicep, forearm and thigh was combined to provide a daily total muscle depth (cm). A substantial amount of ultrasound gel was applied to ensure that the probe could rest gently on the skin without compressing muscle or distorting underlying soft tissue. The transducer was placed perpendicular to the long axis of the thigh prior to measurements being made. In Figure 2-3 the skin and fascia appear convex rather than a level or ‘flat’ image which illustrates that muscle was not compressed at the time of measurement.
72
Figure 2-3: Thigh muscle ultrasound
2.9.1 Bicep
The elbow was flexed to 90 and a point on the skin was marked between the tip of the olecranon and the acromion with indelible ink. With the elbow extended and the patient in a supine position, the forearm was supinated. A thick layer of ultrasound gel was applied to the marked area to limit compression of the underlying tissue. The ultrasound probe was applied at the pen mark on the upper arm to obtain a cross-sectional (axial) view which included the humerus, biceps and brachialis muscle, subcutaneous tissue and skin. The probe was then moved medially around the upper arm on the pen mark until the upper surface of the flat plane of the humerus was parallel with the top of the screen and in the centre of the screen. The image was frozen and labelled ‘B1’ (Biceps). Beginning at the part of the periosteum of the humerus, the internal calliper on the SonoSite ultrasound machine was used to measure the on screen vertical distance to the muscle/subcutaneous tissue interface. This image was saved, then the callipers were removed from the image and the image saved again. This process was repeated for images labelled B2 and B3. The mean value of three measurements was used. Figure 2-4 illustrates the mid upper arm cross section.
73
Figure 2-4: Cross section of mid upper arm
(Printed with permission from R Whitaker, www.instantanatomy.net)
2.9.2 Forearm
The patient’s arm was extended and the forearm kept in a supinated position. A point between the antecubital skin crease and the ulnar styloid was marked with indelible ink. The ultrasound probe was applied at the pen mark on the radial (lateral) side of the forearm to obtain a cross-sectional (axial) view which included the radius. The probe was then moved medially to the pen park on the anterior surface of the forearm until both the radius and ulna were in view and then manipulated such that the upper surface of the radius and ulna seen on screen were parallel with the top of the screen and in the centre of the screen. The image was frozen and labelled ‘F1’ (Forearm) and F2, F3 for subsequent measurements. The thickness of the flexor compartment was measured anteriorly between the superficial fat-muscle interface and the interosseous membrane; radial or lateral side of the forearm (Figure 2-5). Three measurements were taken and the mean of the three measurements was used.
74
Figure 2-5: Cross section of mid forearm
(Printed with permission from R Whitaker, www.instantanatomy.net)
2.9.3 Thigh
The patient was in the supine position with the knee extended. Two measurement points were identified – one as a halfway point and another 12 cm proximal to the superior portion of the patella. This ‘T12cm’ additional measurement point did not feature in the original protocol. This was included on the basis that with a probe depth of 6cm the femur could often not be visualised in patients with thicker muscle or in overweight and obese patients. Halfway point: The midpoint between the tip of the greater trochanter and the lateral joint line of the knee was identified. This point was marked. The T12cm point: The point 12cm proximal to the superior portion of the patella was identified and marked. The thickness of the quadriceps muscle group anteriorly between the superficial fat-muscle interface and the femur was measured (Vastus intermedius and Rectus Femoris, see Figure 2-6). The ultrasound probe was applied at the pen mark on the anterior surface of the thigh to obtain a cross-sectional (axial) view which included the femur, quadriceps muscles, subcutaneous tissue and skin. The probe was manipulated to ensure the femur was in the centre of the screen and the image frozen and labelled ‘T1, T2 and T3’ (Thigh halfway point) or ‘T12-1,
75 T12-2 and T12-3’ (T12cm from superior portion of patella) as appropriate. The means for both the halfway point and T12cm were obtained. At no point was pressure placed on the probe or on the muscle to avoid compression of the soft tissue.
Figure 2-6: Cross section of mid-thigh
(Printed with permission from R Whitaker, www.instantanatomy.net)
When serial measurements were performed on alternate days or twice weekly, these were taken from the exact site previously identified and marked. The muscle depth change could then be compared for total muscle depth and for each individual site.
2.9.4 Muscle ultrasound studies
Two reliability studies encompassing muscle ultrasound are included in this research project (Chapter 3) before patient data is presented. The first is a healthy subject (n=20) reliability study (hospital and college staff) where intra- rater reliability was assessed. The second reliability study was performed between two raters (N=12) to confirm reliability of my technique with a research assistant’s technique. Patient data are presented in chapters 4 and 5.
76 2.10 Activities of daily living (ADL)
Two questionnaires that are routinely used to assess functional status have been used, the Katz Index (Katz et al. 1963) and the Barthel Index (Mahoney and BARTHEL 1965). These indices were selected for this study due to their ease of use and previous use within the ICU setting (De Rooij et al. 2008;Garrouste-Org et al. 2006). The Katz Index assesses basic activities of daily living and relates to a series of questions assessing a patient’s level of independence. These are assessed by scoring function in terms of bathing, dressing, toileting, transfer, continence and feeding) Patients score “0” if they are highly dependent and “6” if they are independent, see Appendix.
The Barthel Index consists of assessment of 10 areas of activities of daily living. Scoring options for each component ranges between 0-5, 0-10 or 0-15. In the Barthel Index assessments include feeding, bathing, grooming, dressing, bowels, bladder, toilet use, transfer, mobility and use of stairs. Patients can score anywhere between 0 -100, 0 indicating the highest level of dependence and 100 being completely independent, see Appendix. Reliability and validity has been established for this tool, mainly in stroke patients (Wade and Collin 1988;Wade and Hewer 1987).
These questionnaires were completed on study days 1, 3, 7 and 14 for both the Nutritional Tipping Point and essential amino acid supplementation studies of this research. However, the Katz and the Barthel indices performed poorly in this population. Although these indices may be sensitive to change in declining health status of older adults outside of the ICU, they appear to be limited in their ability to measure the small increments of change that may be observed in the critically ill (Hayes et al. 2000). The value of these indices are most likely found within the context of follow up on survivors of critical illness (Brunello et al. 2010;Fletcher et al. 2003;Griffiths and Jones 2007b;Im et al. 2004;Navarrete-Navarro et al. 2003) rather than in the acute ICU environment.
77 By comparison, a landmark long term ICU follow-up study assessing functional recovery in survivors of critical illness used the 6-minute walk test and physical components of the Short Form-36 questionnaire (Herridge 2011).
Despite the potential limitations of these indices prospective studies in the critically ill include the use of these tools for functional assessment. In a randomised study assessing the impact of early exercise on functional recovery after critical illness, both the Katz and the Barthel Indices were used in addition to the Medical Research Council Sum Score assessment of strength (Schweickert 2009). Others have used these tools in survivors of critical illness: the Barthel Index was used to assess functional status one week after ICU discharge (van der et al. 2008), a modified Katz index was used for a 2 month follow up study (Wu et al. 1995), the Barthel Index was used at 28 days and 180 days after ICU discharge (Brunello 2010), the Katz Index was used during a 26 month follow up study to examine long term outcomes after prolonged mechanical ventilation (Bigatello et al. 2007) and the Katz Index was used to assess long term function one year after prolonged mechanical ventilation (Chelluri et al. 2004).
2.11 Medical Research Council (MRC) sum score
The MRC sum score is used to assess generalised weakness after critical illness. This test is also used to determine ICU Acquired Weakness (ICUAW) (De 2002). The assessment consists of a bedside test where muscle strength is graded in twelve muscle groups. Six assessments are conducted on both the left hand and right hand side of the body. The upper extremity assessments include shoulder abduction, elbow flexion and wrist extension. The lower extremity assessments include hip flexion, knee extension and foot dorsi-flexion.
Scoring is performed per site by providing a value from 0-5 for the following actions:
78 0 - No visible contraction
1 - Visible muscle contraction, but no limb movement
2 - Active movement, but not against gravity
3 - Active movement against gravity
4 - Active movement against gravity and resistance
5 - Active movement against full resistance
Scoring ranges from 0 for complete paralysis to 60 for full strength, see Appendix. ICUAW is identified as a score of < 48 out of 60 (De 2002). The MRC sum score is deemed a reliable technique to determine muscle weakness in patients with neurological conditions (Kleyweg et al. 1991;Molenaar et al. 1999).
Inter-observer agreement of the MRC sum score has been discussed in a number of studies. The first study was conducted in Guillain-Barre patients (Kleyweg 1991), the second in ICU survivors (Fan et al. 2010) and a third study in a general ICU cohort (Hough et al. 2011). Inter-observer agreement has also been established between the MRC Sum score and hand grip strength assessment (Hermans et al. 2012).
Limitations to manual strength testing in the critically ill include that patient cooperation is required, this may be an issue for patients suffering with delirium or slow neurological recovery. In a general ICU cohort only 30 (22%) of patients were able to perform the MRC sum score assessment due to coma, delirium and injury (Hough 2011). Actual strength may be underestimated if patients lack cognition, understanding or concentration.
The MRC sum score was not included at the outset of this research study and was only included in the amino acid supplementation study (Chapter 5). The
79 decision was made to include the MRC sum score as the Katz and Barthel Indices did not appear to be sensitive enough in depicting functional change in the ICU cohort. Advice was sought from Dr Nicolas Hart, a UK based expert in ICUAW, he recommended the use of the MRC sum score as a measure of strength. I received training from an experienced clinician before patient data collection commenced.
In order to conduct the test the researcher had to assess the alertness and cooperation of the patient by asking the following three questions:
1. “Do you know where you are?”
2. “Could you please stick your tongue out?”
3. “Could you please squeeze my fingers?”
As 100% cooperation is required, patients had to respond positively to all three questions before testing could be performed. (Appendix IV, assessment form).
2.12 Protein turnover via stable isotope studies
Stable isotope studies with 1- 13C leucine are ideal to explore dynamic aspects of protein metabolism in patients. 1- 13C leucine is considered a method of reference for whole-body protein turnover in most physiological conditions (Elia 1981;Wagenmakers 1999). As leucine is an essential amino acid, leucine rate of appearance (RaLeu) is derived from protein breakdown in the absence of consuming any dietary leucine. The 1- 13C leucine stable isotope is usually given as a primed intravenous infusion, at around 150 minutes a plateau will be reached in either the arterial leucine enrichment (Matthews et al. 1980) or in the venous α-ketoisocaproic acid (α-KIC) enrichment, which is described as a ‘steady state’. α-Ketoisocaproic acid is a transamination product of leucine and reflects intracellular levels of leucine (Matthews et al. 1982), it is therefore commonly measured instead of leucine as it represents leucine content in muscle (Garlick and Cersosimo 1997).
80 2.12.1 Stable isotope study
Five 1- 13C leucine stable isotope studies were performed in trauma ICU patients (chapter 5). Each study was conducted at the bedside of each patient on either the surgical or medical intensive care units at Kings College Hospital London; all patients were mechanically ventilated and sedated. A similar protocol was followed to Carroll et al (Carroll 2004) for conducting protein turnover studies in the critically ill. To detect protein turnover a primed intravenous infusion of 1- 13C leucine 1mg/kg; 1mg/kg/h (MassTrace, Somerville, MA) was administered for the duration of the study (0min to
180min). A bolus dose of NaHCO3 was used to prime the bicarbonate pool. Regular blood and breath samples were taken for the measurement of the enrichment of α-ketoisocaproic acid (α-KIC), the concentration of leucine and the enrichment of expired CO2. Patients received standard enteral feed over 16h with an 8h break in feeding to allow for administration of the 1- 13C leucine stable isotope, such that dietary leucine intake did not interfere. See Figure 2-7 and Figure 2-8 for study protocol and preparation.
81 1-13C Leucine Infusion
Tracer sample Time (min)
- 10 min 0 min 150 min 160 170 180 Steady State 13 *Bolus: NaH CO3 *Bolus: 1-13CLeucine
13 Infusion* 1- C Leucine Infusion
Blood*
Breath*
* Breath samples: x 4 per time point
* * Bolus and infusion administered peripherally
* Blood sampling can be taken arterial/central/ peripheral
Figure 2-7: Stable isotope study protocol
Figure 2-8: Stable isotope study preparation with blood sampling tubes and breath sampling exetainers (blue, white and pink top) (Photo: L Wandrag)
82 2.12.2 Blood sampling
Blood samples were taken from indwelling arterial catheters at the following time points: -10min, 0min, 150min (steady state), 160min, 170min and 180min, a 5ml lithium heparin tube was filled for α-KIC analysis at each time point. Blood samples were placed on ice immediately and centrifuged at 3000rpm for 10min at 4°C. Plasma was aliquoted into 1ml freezer tubes and labelled accordingly. The samples were stored at – 80 °C until analysis.
2.12.3 Breath sampling
Breath samples were collected from the expiratory port of the ventilator (Servo 300, Siemens, Berlin, Germany or Servo I, Maquet, Sweden) at the following time points: -10min, 0min, 150min (steady state), 160min, 170min, 180min as described in other studies (Carroll 2004;Russell-Jones et al. 1998). At each time point 5 exetainer tubes were filled with expired gas. Breath sampling in ventilated patients was complex; various methods were explored prior to patient data acquisition and advice was sought from experts in the field (Dr Kenneth Smith, Nottingham University and Prof Margot Umpleby, Surrey University). The first method considered for breath sampling was the use of gas sampling collection bags however the volume of expired CO2 still had to be determined. The second method explored was a Douglas bag collection technique for expired gas. This technique was tested in a number of non- research ICU patients before study data commenced. The Douglas bag was arranged with a flow meter attached to the tubing, attached to the expiratory port of the ventilator, Figure 2-9. This allowed for the volume of expired gas to be recorded. All the air was removed out of the Douglas bag before sampling commenced. Samples were taken via a 3-way tap system so as not to contaminate the system with room air. Test samples were given to the research team at Surrey University and analysed on mass spectrometry before patient data collection commenced. Overall this technique of gas sampling was labour intensive, two people were required to perform gas sampling, with one person checking the flow meter and the second one
83 syringing expired gas into exetainer tubes. A third person had to perform blood sampling at the given time points and had to administer the isotope infusion, Figure 2-7. I therefore had to conduct stable isotope studies with the assistance of two ICU research nurses.
Figure 2-9: Breath sample collection with a Douglas bag
(Photo: L Wandrag)
During patient data collection labelling of the exetainer tubes reflected each time point as described above. One additional exetainer tube was filled per sampling period and the concentration of CO2 was measured on a gas analyser (Radiometer ABL 825 flex) in the ICU in order to calculate the CO2 production rate, see Table 2-2.
84 Table 2-2: CO2 production rate calculation
-10 min 0 min 150 min 160 min 170 min 180 min
Volume in 45L 48L 45L 38L 38L 34L Douglas bag
Sampling 2’59’’ 2’49’’ 2’43’’ 2’27’’ 2’15’’ 2’05’’ time (min)
L/min 17.4 19.3 18.5 16.7 17.7 16.6
VCO2 % 1.1% 1.2% 1.5% 2.1% 2.0% 2.0%
VCO2% of 0.1914 0.2316 0.2775 0.3507 0.354 0.332 L/min
CO2 191.4 231.6 277.5 350.7 354 332 production rate (ml/min)
2.12.4 Analytical methods: α-Ketoisocaproate derivatisation
12C KIC (unlabelled) and 13C KIC (labelled)
Standard stock solutions were used (1mg/ml).
For quality control the following was used:
Quality Control 1: Non-spiked plasma from healthy subject. 4ml baseline plasma sample was used.
Quality Control 2: Spiked sample 5.2%. 5ml spiked with 1.2µl 13C KIC 0.0198g
Quality Control 3: Spiked 10.2%. 5ml spiked with 2.3µl 13C KIC 0.0198g
Patient samples were fully thawed and mixed thoroughly by placing these on the Vortex or rocker. Plasma samples were then centrifuged (Sorvall Legend RT Centrifuge, Thermo Electron Corporation, UK) at 4°C at 2500rpm for 10
85 minutes to precipitate any proteins. 100µl of each patient plasma sample and the quality control was aliquoted into small labelled glass test tubes. The pellet that formed after samples were centrifuged was not disturbed and a clean pipette tip was used for each sample. 1ml ethyl alcohol was added to each plasma sample and this was mixed again on the Vortex. Samples were then centrifuged at 4°C at 2500rpm for 10 minutes. The supernatant was transferred to a labelled 1 dram vial using a clean glass pasteur pipette, one for each sample. Samples were then dried under Oxygen Free Nitrogen (OFN) at 50°C. The Parker Filtrations Nitroflow unit was turned on for approximately 3 minutes before use and temperature was checked for 50C. 200µl distilled water was added to each sample once they were completely dry. This was followed by 100µl O-phenylenediamine powder freshly made in 4M HCL 2% (w/v) (i.e. 0.02g/1ml). 6ml was required for patient samples therefore 1.2g for 6ml. The three standards were also added at this stage, distilled water and the O-phenylenediamine and HCL mixture was added to derivatise. Tubes were capped and mixed well by placing on the Vortex. The heat block was turned on to reach 90°C. Samples were placed on the heat block at 90°C for 1 hour. Tubes were left overnight. The following morning 1ml ethyl acetate was added; samples were capped and placed on the Vortex for 15 seconds. Once the layers had separated the top organic layer was transferred to a clean 1 dram vial containing one level spatula of sodium sulphate. A clean glass pasteur pipette was used for each sample. The extraction of original sample tube was repeated with a further 1ml of ethyl acetate. This was then combined with ethyl acetate extracts. Tubes were capped and mixed by placing on Vortex. Dried ethyl acetate was transferred to clean labelled 1 dram vials. Tubes were placed under OFN to evaporate to dryness at room temperature. The heat block was turned on again and allowed to reach 120°C. 100µl acetonitrile and 100µl N methyl N (tert- butyldimethylsily) trifluoroactemadie (MTBSTFA) was added to tubes which were again capped and mixed by placing on the Vortex. Tubes were then heated at 120°C for 45 minutes. Afterwards, tubes were cooled and excess derivatising reagents were removed under OFN at room temperature before reconstituting with 100µl decane. Samples were capped and mixed by placing
86 on the Vortex before transfer to autosampler vials for gas chromatography mass spectrometry (GCMS) measurements.
α-Ketoisocaproic acid enrichment was measured as the quinoxalinol-tert- butyldimethylsilyl derivative under selected ion monitoring by gas chromatography (Hewlett-Packard 5890)-mass spectrometry (MSD 5971A; Hewlett-Packard, Berkshire, UK) at mass-to-charge ratios 259 and 260, (see Appendices XI and XII). Plasma α-KIC enrichment is a measure of intracellular leucine enrichment (Matthews 1982).
2.12.5 Isotope ratio - mass spectrometry (IRMS)
13 C enrichment of breath CO2 was measured on a VG SIRA series II isotope ratio mass spectrometer (VG Isotech, Cheshire, UK, modified with a Roboprep G inlet system, Europa Scientific, Cheshire, UK). Mass spectrometers are used to determine the steady state enrichment of the metabolite by measuring the abundance of ionised particles at a specified mass. The expired breath sample in the exetainer tube is delivered to the machine. Once injected, gas is moved through a scrubber tube to remove any moisture from the sample. It is then passed through the gas chromatograph where the different isotopic species are separated. When the gas of interest is extracted from the column it is diverted to the mass spectrometer. As the sample gas passes through the mass spectrometer it is ionised and the isotopes are separated according to mass-to-charge (m/z) ratio. The isotopic species m/z 44, 45 and 46 are collected simultaneously at separate detectors, each total beam area being integrated separately, allowing measurement of the isotope ratios.
This technique was first described by Matthews (Matthews 1980) for analysis of leucine stable isotope analysis in human subjects.
87 2.12.6 Leucine metabolism calculations
Measurements of leucine metabolism were calculated using standard isotope dilution equations (Matthews 1980;Whyte 2010).
Leucine production rate (Ra: a measure of whole body protein degradation) was calculated as:
Ra =F [1/ (APE x 0.01)]
F is the isotope infusion rate (µmol/kg/min) and APE is plasma α-KIC enrichment.
Leucine disappearance rate (Rd) was assumed to be equal to Ra.
Leucine oxidation rate (OX) was calculated as:
OX = (APECO2 x CO2Ra)/APEKIC
CO2Ra is the production rate of CO2 and APECO2 the enrichment of expired
CO2.
13 In all cases, leucine oxidation was corrected by assuming that 80% of CO2 was expired. A correction factor of 0.899 for CO2 recovery was used:
Leucine oxidation x 0.899 (Rodriguez et al. 1986).
Non-oxidative leucine disposal (NOLD, a measure of whole body protein synthesis) was calculated as the difference between Rd and OX.
NOLD (µmol/kg/min) = LeuRd-LeuOX (corrected)
Net leucine balance (µmol/kg/h) = (NOLD-LeucineRa) x 60
88 2.12.7 Protein turnover calculations
Net protein balance was estimated using the net leucine balance (NOLD - Ra), assuming 8 g leucine/100 g whole body protein.
Whole body protein synthesis (g protein/kg/d) = NOLD (1440/590*)
Whole body protein breakdown (g protein/kg/d) = LeuRa (1440/590*)
* Assuming 590µmol leucine/g protein (Matthews 1980).
Protein Balance (g protein/kg/d) = whole body protein synthesis - whole body protein breakdown.
2.13 Leucine-enriched essential amino acid supplementation
Leucine-enriched essential amino acid (L-EAA) supplementation was used as an intervention in critically ill trauma patients (Chapter 5). During the planning phase of this research Professor Robert Wolfe, an international expert on the regulation of muscle metabolism and transport of amino acids between blood and muscle tissue, was contacted for his opinion on the use of L-EAA supplementation in the critically ill. Prof Wolfe reported this was “an excellent rationale for using this supplement in ICU as a greater stimulus for muscle protein synthesis can be provided than with intact protein while giving less of a nitrogen load. We have done some work showing the L-EAA effect persists in insulin resistance, but I know of no such study in the ICU.” (Personal communication, 22.10.2008).
The dose of L-EAA supplementation to promote muscle protein synthesis used by Prof Wolfe’s group was 11g twice daily (Borsheim et al. 2008). I therefore mixed 100g of EAA powder supplement with 35g leucine powder supplement (SHS, Nutricia Ltd, Wiltshire, UK) to obtain the ratio of amino acids required. This 135g powder mixture was divided into 25g doses. The 25g dose contained 18.5g EAA plus 6.5g leucine powder, containing a total of
89 9.5g leucine, 2.7 g valine and 2.1g isoleucine. The branched-chain amino acid ratio for this supplement was 1:5:1 for valine: leucine: isoleucine ratio. The 25g dose was divided into five daily doses administered with 100ml water per dose via enteral feeding tubes. By providing doses as five 100ml supplements distributed throughout the day the osmolarity was distributed (360mOsm/kg per dose). Borsheim et al used 22g per day (Borsheim 2008). The optimal dose of leucine to promote muscle protein synthesis is approximately 0.12g leucine per kg of lean body mass in healthy people (Drummond and Rasmussen 2008).
There are counter arguments to providing leucine supplementation in the critically ill. Two papers point towards leucine supplementation not making any difference in septic rats as sepsis was thought to interfere with mTOR signalling (Kazi et al. 2011;Lang 2007).
2.14 Screening and recruitment
This study was given a favourable ethics opinion for conduct in the NHS by the North West London REC 1/Camden & Islington Research Ethics Committee with study reference 10/H0722/40. The essential amino acid supplementation study was registered in the controlled clinical trials register with study ID number ISRCTN79066838. During the Nutritional Tipping Point study patients were recruited from adult intensive care units at Charing Cross, Hammersmith and St Mary’s Hospitals from September 2010 to February 2013. The essential amino acid supplementation study patients were recruited from Kings College Hospital from medical and surgical intensive care units from May 2012 until the end of January 2013.
2.14.1 Procedure of obtaining informed consent or assent
In line with the Mental Capacity Act 2005 for persons who lack capacity a legal representative could provide ‘provisional assent’ in vulnerable groups
90 such as critically ill patients, whilst the patient is unable to do so. A ‘Personal Consultee’ was approached in the first instance. If no such a person existed, a ‘Nominated Consultee’ was appointed who was not connected to the research study. Under the Act ‘assent’ was sought from these individuals. Retrospective consent was sought once appropriate.
91 Chapter 3. Reliability of Methods
Technique Reliability for Measurement in ICU
In this chapter reliability of techniques used in this research is described. The indirect calorimeter obtained for this research (the older Deltatrac I™ model) required various gas, flow and alcohol burn tests in order to obtain a functional calorimeter. These steps along with a comparison study between three Deltatrac II models are described in this chapter. As this equipment failed to work in our institution, no subsequent patient data collection was performed.
The second reliability study pertains to the muscle ultrasound technique used in this research. This technique in which muscle thickness is measured has been previously validated in an ICU setting against DEXA scanning. Reliability of this technique had to be established for the researcher (intra-observer reliability) which is described in this chapter. In addition, as the research assistant and I were collecting muscle ultrasound data from different hospital sites, inter-observer reliability for the research assistant and I had to be determined (sections 3.2.2 and 3.2.3).
3.1 Indirect calorimetry
Indirect calorimetry (measuring O2 consumption and CO2 production to determine energy expenditure) is described as the ‘gold standard’ method to determine energy expenditure in the critically ill (Boullata et al. 2007;Bracco et al. 1995;Frankenfield 2010). The Deltatrac™ metabolic monitor used to measure indirect calorimetry has been validated (Takala et al. 1989;Tissot et al. 1995) and has been widely used over the past few decades in mechanically ventilated patients.
92 The measurement technique will not be described in detail, however the Deltatrac™ metabolic monitor utilises the following principles:
CO2 production (VCO2) is measured with infrared analysers on a flow or air-dilution principle.
The respiratory quotient (RQ) is then calculated by Haldane transformation*.
O2 consumption (VO2) is calculated via paramagnetic sensor and from the RQ.
* The Haldane transformation assumes that only O2 and CO2 are exchanged in the lungs whilst nitrogen gas remains equal in inspired and expired air:
Vi = Ve x FeN2/FiN2
(Vi: Air volume inspired; Ve: Air volume expired; FeN2 and FiN2: Fractional concentrations of nitrogen for expired and inspired air) (Wilmore and Costill 1973).
The RQ is calculated via the Haldane transformation as:
(1-FiO2)/[(FiO2-FeO2)/FeCO2-FiO2)]
Where FiO2 is inspired oxygen fraction and FeCO2 is expired carbon dioxide fraction (Takala 1989).
A Deltatrac I™ was obtained for the purpose of this research. Gas, flow, pressure calibration and alcohol burn tests had to be performed before equipment could be used for research purposes. Service contracts and support from industry are no longer available. I performed these procedures systematically in the Deltatrac I™.
93 3.1.1 Alcohol burn testing
Alcohol burn testing is required to determine the Respiratory Quotient (RQ) and therefore evaluate the performance of the Deltatrac™. If a known amount of ethanol is burnt (5ml) then it is possible to determine the RQ. Ethanol is oxidized according to the following equation:
C2H5OH + 3 O2 = 2 CO2 + 3 H2O
From the equation above the RQ can be calculated as RQ=VCO2/VO2 = 2/3 = 0.67. At 25°C the density of ethanol is 0.78522g/L and 5ml of pure ethanol
(100%) will yield 3820ml of CO2. 0.64-0.69 is an acceptable RQ range post alcohol burn.
3.1.2 Calculations
From a flow calibration test performed with 5ml 99.7% ethanol, testing yielded a total of 3760ml VCO2 over 23min.
Equation for new flow rate:
= 1.03 x (3820ml/total VCO2 in ml) x old flow
= 1.03 x (3820ml/3760ml) x 40.2
= 1.03 x (1.01596) x 40.2
= 42.1ml/min
42.1ml/min was added in “Factory Settings” and written on the rear of the equipment.
94 The alcohol burn test was repeated after gas calibration, pressure calibration and the new flow rate was determined. Alcohol burn testing continued for approximately 30min; the last 15 readings of the RQ were averaged as per manufacturer’s instructions.
From our test, last 15 RQ’s =10.1/15
= 0.67 (accepted range is between 0.64-0.69)
With an RQ of 0.67, patient trials could be attempted on the Deltatrac I™.
A series of ICU patient trials were conducted on the Deltatrac I™ in our institution. Ventilators on our unit include the Siemens 300 (Siemens Healthcare, Surrey) and Maquet Servo I (Maquet Ltd., Tyne & Wear
Sunderland). One successful trial yielded VCO2: 164 ml/min, VO2: 217ml/min, energy expenditure of 1450kcal and RQ: 0.73. Thereafter no physiological measurements were ever obtained. VO2 consistently displayed an error reading of 999, with no RQ and no energy expenditure obtained. Advice was sought from Pekka Meriläinen (Parameter Research Director from GE Healthcare, Finland). A suggested trial canopy run was conducted and failed.
A number of experts in the field of indirect calorimetry were contacted for advice. Prof Singer reported that the Deltatrac™ did not appear to be compatible with many of the modern ventilators (e.g. Wella, Siemens, Maquet). Prof Singer used Puritan Bennett 840 and Evita 4 during Deltatrac™ research and patients were heavily sedated (Singer 2011).
Prof Wernerman confirmed that the Deltatrac™ appeared not to function well with all modern ventilators. Patients in Prof Wernerman’s recent study were ventilated using the Evita XL ventilator, (Dräger, Germany) in pressure support or pressure controlled modes (Sundstrom 2012).
Modern ventilators supply respiratory gas into the breathing circuit throughout the entire breathing cycle – this is to allow sensitive detection of patient
95 respiratory effort. This ‘bias flow’ or ‘flow by’ modes, which have been incorporated to assist the patient with breathing, may interfere with Deltatrac™ measurements.
3.1.3 Deltatrac II™ comparison exercise
Since the Deltatrac 1 did not work in our institution I decided to evaluate the Deltatrac 11. Three such machines were available in London from University College London, Guys & St Thomas’ NHS Foundation Trust and Chelsea & Westminster NHS Foundation Trust. These were compared on the Intensive Care Unit at Charing Cross Hospital. The aim of this study was to compare the results of these calorimeters on the same critically ill patient on the same day, in steady state. This was done as investigators locally were questioning the reliability of the Deltatrac, unusual values for VO2 and RQ were observed and investigators wondered whether the bias flow from ventilators could potentially interrupt readings. We therefore wanted to assess the readings that would be obtained from three different calorimeters that were compared on the same patient, same day and same ventilator, even though bias flow could not be turned off in this ventilator.
A suitable stable mechanically ventilated patient was identified on pressure support mode of ventilation with a FiO2 of 0.35. The patient was fed over 24h as per ICU protocol on a 1kcal/ml fibre based feed which provided 1800kcal and 72g protein per 24h. There was no evidence of vomiting and the gastric residual volumes were < 200ml over 24h indicating that the patient was absorbing her enteral feed. There were no contra-indications to indirect calorimetry: FiO2 was below 60%, the patient was not on any renal replacement therapy, no gas leaks were evident and no chest drains were in situ either.
All Deltatrac II™ calorimeters had flow calibrations, gas calibrations and alcohol burn testing performed on-site prior to testing. The machines were warmed up for 30 minutes per manufacturer’s recommendations prior to calibration and respiratory quotients’ ranged from 0.67 to 0.69, suggesting
96 appropriate calibration. A steady state was accepted when values for VCO2 and VO2 were within 10% difference over 10 minutes during testing. The room temperature on the intensive care unit was recorded as 23 - 24°C during testing. The ventilator used was a Servo-i ventilator (Maquet Critical Care AB, Sweden), bias flow could not be turned off in this ventilator.
3.1.3.1 Results: Deltatrac™ comparison study
Measurements from the three Deltatrac calorimeters are summarised in Table 3-1.
Table 3-1: Measuring three calorimeters on the same ICU patient
St Thomas’ University College Chelsea & % Deltatrac London Deltatrac Westminster Difference Deltatrac
VCO2 166.6 177 172.2 5.9%
(ml/min)
VO2 201.2 221.8 213.9 9.3%
(ml/min)
RQ 0.83 0.799 0.805 3.7%
REE 1375 1506 1454 8.7%
(kcal)
3.1.3.2 Discussion: Deltatrac™ comparison study
The findings of this study show that all three Deltatrac II™ machines measured within 10% of each other on the same ICU patient (Table 3-1). This suggests that they are all working consistently. As no other measure was available to compare the Deltatrac II™ to or validate against it could only be
97 concluded that the Deltatrac II™ appeared to work consistently, however accuracy could not be measured in this study.
In summary, patient data collection could not be obtained for indirect calorimetry as Deltatrac I™ measurements were not feasible in our institution. All other Deltatrac II™ metabolic monitors in London were in use at the time and were not available either on loan.
3.2 Muscle ultrasound reliability studies
Three reliability studies for muscle ultrasound measurements were performed to ensure intra- and inter-rater reliability and calibration of the equipment including:
Transducer calibration against an industry standard phantom limb Intra-rater reliability testing (N=20 healthy subjects) Inter-rater (two raters) reliability testing (N=12 healthy subjects)
3.2.1 Tranducer calibration
In order to calibrate the transducer against an industry standard tissue phantom (Gammex, Nottingham, UK) and to determine the accuracy of the ultrasound machine’s internal calliper, the transducer was applied to the top of the ‘tissue phantom’. The tissue phantom consisted of a gel container with wires visible on ultrasound, these wires are positioned at known, fixed distances from each other. The transducer was applied to the top of the phantom and the callipers were used to measure the distance between the different wires, Figure 3-1.
98 Second wire. 2.02cm measured to this point.
First wire. Measured from this point.
Figure 3-1: Calibration of transducer against a tissue phantom
3.2.2 Intra-rater reliability study
Twenty healthy volunteers (eight male, 12 female) were recruited from staff at Imperial College Healthcare NHS Trust. Muscle depth measurements were made on each subject at four different sites as per methodology described in Chapter 2, section 2.9 (page 71). Each subject had two measurements, one in the morning and one in the afternoon of the same day. I recorded the results separately and did not access the morning readings prior to the afternoon test to avoid biasing the results.
Statistical analyses were performed using Statistical Package for Social Sciences 20 (SPSS IBM, Chicago, IL) and GraphPad Prism 6.02 for Windows (GraphPad Software, La Jolla, CA).
The following reliability tests were selected:
Intra-class correlation coefficients (ICC): To determine degree of correspondence and agreement in one model (Shrout and Fleiss 1979). For intra-class correlation with one rater measuring 20 subjects
99 on two different occasions a two way mixed model was used (rater as a fixed effect, subjects as random effects, hence a mixed model). Bland Altman analysis: to determine levels of agreement (Bland and Altman 1986). Standard error of measurement (SEM): To reflect response stability or reliability of the response by assessing the standard deviation of the measurement error.
These three measurements (ICC, Bland Altman and SEM) were selected on the basis that these tests provide a more accurate indication of muscle ultrasound reliability than correlations or coefficients of variations (Rankin and Stokes 1998).
Coefficient of variation was not used as this is not considered to be an appropriate measure of reliability. Within-subject standard deviation is of importance, not the between-subject standard deviation which is mostly supplied with CV calculations (Chinn 1990;Rankin 1998).
Pearson’s or Spearman’s correlations were not used either as this would demonstrate linear association only, not agreement.
3.2.2.1 Intra-class correlation coefficient results
The results are shown in (Table 3-2) with good reliability suggested by the high ICC. Nevertheless some variation is seen between the four sites.
100 Table 3-2: Intra-rater reliability, a two way mixed model
Site Intra-class 95% Correlation Confidence Coefficient * Interval
Total sites** 0.984 0.958-0.993
Bicep 0.719 0.415-0.878
Forearm 0.600 0.226-0.820
Thigh 1.000 1.000-1.000
Thigh (12cm) 0.950 0.871-0.981
* > 0.90: good reliability and validity; > 0.75: good reliability; < 0.75: poor to moderate reliability
** Total sites = bicep + forearm+thigh+thigh (12cm)
3.2.2.2 Bland Altman analysis results
Table 3-3 summarises the descriptive statistics for Bland Altman analysis from morning and afternoon readings, showing small differences between the two measurements. This suggests that my measurement technique produced consistent results in the same individual.
Table 3-3: Descriptive statistics from morning and afternoon ultrasound measurements for total muscle sites from one rater (N=20)
N Min Max Mean SD SEM
Difference 20 -0.79 0.76 -0.05 0.39 -
Morning 20 5.62 13.75 10.51 1.83 0.23cm
Afternoon 20 5.61 13.66 10.56 1.77 0.22cm
SD: standard deviation; SEM: standard error of measurement; SEM = SD1-reliability coefficient.
101 Figure 3-2 below shows the Bland Altman agreement plot for my morning and afternoon readings. Mean difference score was -0.05cm, the 95% upper limit of agreement (2 SD above the mean) was 0.73cm. The 95% lower limit of agreement (2 SD below the mean) was -0.83cm. This analysis is used to assess level of agreement visually by assessing the difference between measurements plotted compared to the mean value for each subject. 95% of the difference values fall within 2 standard deviations above and below the mean difference value, 95% limits of agreement (Bland 1986;Ottenbacher and Tomchek 1993). Discrepancies are small, 0.78cm (2 SD) above and below the mean with the limits of agreement (-0.83 and 0.73) small enough to be confident in the level of agreement. The mean difference close to zero indicates good agreement, the standard deviation close to zero indicates low variation.
Figure 3-2: Bland Altman plot of muscle ultrasound from one rater (N=20)
102 3.2.2.3 Standard error of measurement results
Descriptive statistics for my morning and afternoon readings is summarised in Table 3-3. Standard error of measurement was calculated separately for morning and afternoon measurements and suggested that error was low and consistent between morning and afternoon readings.
3.2.2.4 Discussion
Overall it can be concluded that my muscle ultrasound technique demonstrated good reliability with good levels of agreement when the technique was repeated at different time points on the same subjects. Measurement error was low.
An advantage of using the ICC includes that it takes into account individual differences between raters and subjects, biological variability and variability in testing conditions or between days of testing (Shrout 1979). Of interest is the breakdown in individual site scores per measurement (Table 3-2). Intra-class correlation coefficients were > 0.9 for total sites, thigh and T12cm indicating good reliability. The ICC for bicep and forearm individually do not show high ICC scores. The bicep and particularly the forearm were initially the most difficult measurements to perform in healthy subjects and the ICU patient group. With the forearm the main issue was in identifying the exact location ‘on-screen’ where the measurement should be taken from. It was not always straightforward to visualise the structures and to find a level bony surface to measure from. Therefore repeatability of this particular measurement was initially found to be difficult. Reliability for total measurement sites, thigh and T12cm were good. Ultrasound measurements of bicep and forearm only should not be considered. Total measurement sites were included in the final analysis.
Standard error of measurement reflects the standard deviation of measurement error. The SEM has the advantage that it can be interpreted clinically, measurement errors for muscle depth on ultrasound of 0.23cm and
103 0.22cm from maximum measurements of 13.75cm and 13.66cm respectively seem acceptable. It should however be noted that differences of up to 0.2cm were accepted in research subject measurements as outlined in the protocol.
Coefficient of Variation (CV) and correlations were not included due to their limitations as reliability tests, CV was not included as this is not considered to be an appropriate measure of reliability. Within-subject standard deviation is of importance, not the between-subject standard deviation which is mostly supplied with CV calculations (Chinn 1990;Rankin 1998). Pearson’s or Spearman’s correlations were not included as this would demonstrate linear association only, not agreement.
3.2.3 Inter-rater reliability study
Since patient recruitment was to occur on several sites a research assistant was employed to help with data collection. This meant I needed to assess the consistency of our measurement techniques. Thus, inter-rater reliability testing was carried out.
Twelve healthy volunteers (two male, ten female) were recruited from staff at Imperial College Healthcare NHS Trust. Measurements were made at four sites following the protocol outlined in Chapter 2. Subjects were measured on the same day by the research assistant and I. The images were stored for analysis later, and the two raters did not have access to the other’s measurements.
Statistical analyses were performed using Statistical Package for Social Sciences 20 (SPSS IBM, Chicago, IL) and GraphPad Prism 6.02 for Windows (GraphPad Software, La Jolla, CA).
104 Selected reliability tests include:
Intra-class correlation coefficients - to determine degree of correspondence and agreement in one model between two raters. The intra-class correlation coefficient was calculated, however, on this occasion a two way random effects model was chosen as both the raters and the subjects were seen as random effects. Bland Altman analysis - to determine levels of agreement between measurements of two raters. Standard error of measurement - to reflect response stability or reliability of the response by assessing the standard deviation of the measurement error.
3.2.3.1 Inter-rater intra-class correlation coefficient results
Results shown in Table 3-4 indicate good reliability as demonstrated by high ICC. Some variation occurs between the four sites.
Table 3-4: Inter-rater reliability with ICC per site, two way random model
Site Intra-class 95% Correlation Confidence Coefficient * Interval
Total sites** 0.965 0.882-0.990
Bicep 0.937 0.798-0.982
Forearm 0.793 0.427-0.936
Thigh 0.866 0.550-0.965
Thigh (12cm) 0.827 0.504-0.947
* > 0.90: good reliability and validity; > 0.75: good reliability; < 0.75: poor to moderate reliability; **Total sites = bicep + forearm + thigh + thigh12cm
105 3.2.3.2 Bland Altman analysis results
Descriptive statistics of Bland Altman analysis for two raters are summarised in Table 3-5.
Table 3-5: Descriptive statistics for reliability testing for all muscle sites between two raters (N=12)
N Min Max Mean SD SEM
Difference 12 -0.95 1.00 0.02 0.51 -
Rater 1 12 7.61 13.50 11.28 1.84 0.34cm
Rater 2 12 7.14 13.72 11.23 2.00 0.37cm
SD: standard deviation; SEM: standard error of measurement; SEM = SD1-reliability coefficient.
Figure 3-3 shows the Bland Altman plot from two raters. The mean score is 0.02. The 95% upper limit of agreement is 1.04cm and the 95% lower limit of agreement is -1.0cm. 95% of the difference scores fall within 2 standard deviations above and below the mean difference score (95% limits of agreement). Discrepancies are larger than that measured by one rater (one rater 0.78cm versus two raters 1.02cm). Limits of agreement are wider (-1.0 and 1.04).
106
Figure 3-3: Bland Altman plot of muscle ultrasound between two raters (N=12)
3.2.3.3 Standard error of measurement results
Descriptive statistics for muscle ultrasound measurements between two raters are depicted in Table 3-5. There is a 0.03cm difference between rater 1 and 2 SEM, error is consistent between the two raters however this was higher than SEM recorded by one rater on morning and afternoon readings.
3.2.3.4 Inter-rater reliability discussion
Good reliability has been demonstrated between the researcher and research assistant for this muscle ultrasound technique with ICC > 0.9.
Bland Altman analysis showed wider limits of agreement than that observed in the analysis of one rater. There was a 0.03cm difference recorded for standard error of measurement between the two raters, error was consistent between two raters however higher than that observed in one rater. One possible explanation for this difference could be time constraint. Error could
107 have been introduced when researchers attempted to speed measurements up as the equipment was continually requested for clinical use during this reliability study. Future muscle ultrasound reliability studies should ensure that measurements could be undertaken undisturbed.
108 Chapter 4. Nutritional Tipping Point
4.1 Introduction
In this observational study in critically ill patients I wanted to assess whether a notional ‘nutritional tipping point’, where anabolism starts to exceed catabolism, could be identified within a period of critical illness. If such a point could be identified, nutritional strategies could be targeted in an effort to minimise protein breakdown. Nutrition guidelines often refer to targeting nutritional interventions once patients are in the ‘anabolic’ or ‘recovery phase’, however this point has not yet been defined during a period of critical illness.
Additionally the inter-relationships between nutritional and inflammatory markers in the critically ill have been explored and are described in this chapter. Lastly this current data set was explored for surrogate endpoints that could be used in future nutritional clinical trials.
This observational study was conducted in adult patients at Imperial College Healthcare NHS Trust on Intensive Care Units at Charing Cross, Hammersmith and St Mary’s Hospitals.
4.1.1 Research Question
Is there a “nutritional tipping point” (the point where anabolism starts to exceed catabolism) where a nutritional intervention is more likely to be effective in attenuating muscle mass loss on ICU?
109 4.1.2 Aims
1) To identify a nutritional tipping point where anabolism starts to exceed catabolism during a period of critical illness. 2) To explore inter-relationships between inflammatory and nutritional markers during critical illness. 3) To explore data for surrogate endpoints for use in a future nutritional clinical trial where effect size and sample size could be determined from the current dataset.
4.2 Methods
4.2.1 Outcome measures
The data collection methods for outcome measures were described in Chapter 2, the study plan describing the timing of data collection can be seen in Figure 4-1.
The primary outcome measures were muscle depth change and nitrogen balance. Muscle depth change (cm) was measured by ultrasound on alternate days during the first week of the study and twice weekly thereafter, see chapter 2, section 2.9, p72.
Nitrogen balance (g/day) was calculated using both the British Dietetic Association’s Parenteral and Enteral Nutrition Group (PENG) recommended equation and the Deacon equation (Deacon 2009), see chapter 2, section 2.8, p70.
Secondary outcome measures include:
Inflammatory markers C-reactive protein (CRP) (mg/L) and serum albumin levels (g/L) were obtained from the Imperial College Healthcare Trust clinical chemistry results database. CRP and albumin levels were collected on days 1, 3, 7, 14 and 20 of the study. The new prognostic inflammatory and nutritional index (PINI) was calculated from CRP and serum albumin levels. The ‘new PINI’ method includes calculation of log (CRP) /Albumin. Cytokines
110 IL-6 (pg/ml) and IL-10 (pg/ml) were measured from plasma samples collected on days 1, 3, 7, 14 and 20 of the study, see chapter 2, section 2.1, p64.
Urinary studies for 3-methylhistidine (µmol/24h) and urinary urea (mmol/24h) were performed on days 1, 3, 7, 14 and 20 of the study, see chapter 2, section 2.7, p69.
4.2.2 ICU data
Other data collected included estimated weight (kg) and height (m) of patients to ascertain pre-admission body mass index (BMI, kg/m2). Medical notes were checked for recent measurements. Relatives were asked for weight and height data if none were recorded in the medical notes. Additionally the age (years) and gender of each patient was recorded. Daily energy (kcal) and protein (grams) intakes were recorded from ICU observation charts. Gastric residual volumes (ml) and incidence of vomiting were collected to describe tolerance to enteral feeding. Occurrence of diarrhoea was also recorded.
Various ICU data were recorded as descriptors of the type and severity of the illness or injury: diagnosis, past medical history, Acute Physiology and Chronic Health Evaluation (APACHE) II score, admission Sequential Organ Failure Assessment (SOFA) score, Glasgow Coma Scale, ventilation status, use of renal replacement therapy, ICU length of stay (days) and ICU outcome (dead or alive). Although the SOFA was designed for serial collection during a period of critical illness, in two of the hospitals admission SOFA was collected only.
Functional outcomes via the Katz and Barthel indices were recorded (Chapter 2, section 2.10, p77). Both indices were used on the same patient at the same time point. Any uncertainty in functional assessment was clarified with the senior ICU physiotherapist.
111 Other ICU data thought to potentially influence muscle integrity or muscle depth measurements were recorded including use of steroids during ICU stay, sedation use, insulin administration (units/24h), fluid balance (ml/24h) and physiotherapy (particularly mobility or rehabilitation therapy).
4.2.3 Study plan
The study plan for this study is depicted below in Figure 4-1 where data collection from each time point can be seen.
Study Plan
Day of Study Screening & Consent 1 3 7 14 20
•CRP, Alb •CRP, Alb • CRP, Alb •CRP, Alb •CRP, Alb • IL-6, IL-10 • IL-6, IL-10 • IL-6, IL-10 • IL-6, IL-10 • IL-6, IL-10 • Urinary studies • Urinary studies • Urinary studies • Urinary studies •Urinary studies • Nitrogen • Nitrogen • Nitrogen • Nitrogen Balance Balance Balance Balance •Nitrogen • Katz & Barthel • Katz & Barthel • Katz & Barthel • Katz & Barthel Balance •Katz & Barthel
Study 1 Muscle ultrasound alternate days for week 1, twice weekly thereafter
Figure 4-1: Study plan for Nutrition Tipping Point study
112 4.2.4 Sample Size Calculation
By convention for regression analysis and multilevel models, ten patients should be recruited per variable observed. Eight variables will be included in the model (C-reactive protein, albumin, IL-6, IL-10, urinary urea, 3- methylhistidine, nitrogen balance and muscle depth), therefore 80 patients should be required.
4.2.5 Statistical Analysis
Descriptive statistics analyses were performed using Statistical Package for Social Sciences 20 (SPSS IBM, Chicago, IL) and GraphPad Prism 6.02 for Windows (GraphPad Software, La Jolla, CA). Significance was set at 0.05%. Descriptive data were checked for normality and subsequently presented as mean (SD) or median (IQR) for continuous data. Categorical data are presented as frequencies. Histograms and bar plots were used in order to describe the distribution of patient characteristics. (Appendix VII, p247).
CRP, albumin, urinary urea, 3-methylhistidine, nitrogen balance, total muscle depth, IL-6 and IL-10 were measured serially in patients. Multilevel models were used to determine the changes over time taking into account the correlated nature of repeats within individuals. Multilevel modelling was selected as there were multiple predictors i.e. more than one explanatory term in the model with a repeated measures design of a single outcome; this type of analysis is multivariate analysis (Hidalgo and Goodman 2013). In this way the individual trajectories were compared and contrasted according to other factors (day of measurement, age, gender, BMI, ICU length of stay). The precise form of multilevel model depended on the distribution of that particular outcome. Normality was tested for all outcomes using histograms and PP plots and several transformations were also tested. Where the outcome was transformed to square root transformation to obtain normal distribution (CRP, albumin, urinary urea, 3-MH and total muscle depth) linear multilevel models
113 were used. Interleukin-10 could not be transformed to normality but was dichotomised at <0pg/ml and >0pg/ml, since this was a clinically meaningful cut-off, and a logistic multilevel model was used to characterise changes over time. Nitrogen balance (assessed via the PENG and Deacon equations) and IL-6 could not be transformed to normality, for these non-parametric (median) multilevel models were used. Day of measurement (Day 1, 3, 7 or 14 of study) was included as a factor in all models. Day 1 of study was not necessarily day 1 on ICU as patients were recruited to the study within 72h of their ICU admission. Other variables (age, gender, BMI and ICU length of stay) were incorporated in turn to find the best fitting models. Both forwards and backwards stepwise approaches were used to verify that the results did not differ according to method selected. For nitrogen balance, other biomarkers were also investigated as potential predictors.
Normalising transformations (i.e. square, square root, inverse, inverse of square root and logarithmic) were applied for C-reactive protein, albumin, urinary urea, 3-methylhistidine, muscle depth, interleukin-10, interleukin-6 and nitrogen balance (The Parenteral and Enteral Nutrition Group (PENG) of the Bristish Dietetic Association and Deacon equations were used). Square root transformation produced normality for CRP, albumin, urinary urea, 3-MH and muscle depth. None of the tested transformations led to normal distribution for nitrogen balance (PENG and Deacon equations), IL-10 or IL-6. Patients were dichotomized to those with IL-10 levels equal to 0.00 pg/ml (i.e. undetectable IL-10 values) and those with IL-10 levels > 0.00 pg/ml.
For variables where a transformation was used in order to achieve normally distributed data and for normally distributed variables, linear multilevel models were used to evaluate the association of day of measurement (i.e. time) with the biomarker levels, taking into account age, gender, BMI and total duration of stay on the ICU. (Jose Pinheiro, Douglas Bates, Saikat DebRoy, Deepayan Sarkar and the R Development Core Team (2013). R package version 3.1- 108). Multilevel modelling for binary responses was used for IL-10 to evaluate
114 the association of day of measurement (i.e. time) with the odds of having IL- 10 levels >0.00 pg/ml, taking into account age, gender, BMI and total duration on the ICU. (Douglas Bates, Martin Maechler and Ben Bolker (2012). R package version 0.999999-0).
For those variables where no transformation was found appropriate median multilevel models were used in order to evaluate the association of day of measurement (i.e. time) with a change in median levels of response variables, taking into account age, gender, BMI and total length of stay in the ICU. (Marco Geraci (2012). R package version 1.02).
Results regarding square root transformations are presented as b-coefficients (95% Confidence Intervals), while results for logarithmic transformations are presented as exponentiated b-coefficients (95% Confidence Intervals).
Predicted values for each response variable were also calculated and presented along with their corresponding 95% confidence intervals. Predicted values are a range of values one might expect to see for each biomarker given that biomarkers were modelled against fixed variables: day of measurement, age, gender, BMI and ICU length of stay. The strength of association between biomarkers and fixed variables were therefore assessed. Tables with predicted values are presented in Appendix X, p254.
Further analyses of factors related to biomarkers were performed taking into account ICU outcome (alive/dead), steroid use (yes/no) and nutritional adequacy (calories received versus prescribed and grams of protein received versus prescribed). Calories received versus prescribed were documented as “% energy intake” and grams of protein received versus prescribed were documented as “% protein intake”. These variables were included in the model with best fit derived from the previous analysis (where biomarkers were modelled against day of measurement, age, gender, BMI, ICU length of stay) and multiple regression models were performed. (Section 4.3.9, p157 results; section 4.4.7, p180 discussion).
115 For comparison of the new prognostic inflammatory and nutritional index (new PINI) a Friedman test was used (two way analysis of variance by ranks) for non-parametric data. This was used as an alternative to repeated measures ANOVA for non-parametric data to detect change over time in PINI scores from days 1 to day 14.
4.2.6 Surrogate endpoints methodology
Data were explored for plausible surrogate endpoints to be used in a future nutritional clinical trial where effect size and sample size could be determined from the current dataset. Values from CRP, serum albumin, urinary urea, 3- MH and muscle depth were assessed from the current data set. A 5%, 10%, 15% and 20% effect size was calculated from each mean value for days 7 and 14 and the difference between days 7 to 14 to determine what a meaningful change in the result might look like. A sample size estimate was then determined from each of these changes. Early intervention before day 7 was thought unlikely to change an endpoint, therefore results from day 7 and 14 were used. For muscle depth results for day 1 and day 14 were however used as the first day of muscle ultrasound measurement on ICU reflected the highest muscle bulk. In each case the power (β) was selected as 0.8 and alpha was selected as 0.05.
4.3 Results: Nutrition Tipping Point Study
4.3.1 Screening and recruitment
This observational study was conducted from September 2010 to February 2013, during which 1475 ICU patients were screened for potential inclusion into this study. The target of recruiting 80 patients for this study was achieved. The number of patients excluded from this research was 1395 patients.
116 Patient screening, exclusions and recruitment are shown in Figure 4-2. Although 80 patients were recruited to the study two patients died before testing commenced. The total number of patients included in the analysis is therefore 78. Additional patients could not be recruited due to time constraints. Patients were recruited within 72h of their ICU admission, ‘Day 1’ refers to day 1 of study, which occured between day 1-3 of the patient’s ICU admission. APACHE II score was determined according to day of admission to the ICU, not day 1 of study.
N= 1475 patients screened
N=1395 excluded, did not satisfy criteria N=765 Extubated/Lev el 2 patients N=156 Died/not f or activ e treatment N=117 No research staf f /logistical issues N=63 No next of kin within 72h N=58 Re-admissions N=58 Recent ICU admissions N=54 Other ICU research studies N=23 Psy chiatric reasons N=101 Other N= 80 patients recruited
N=2 patients died prior to testing
N=78 patients included
CXH HH SMH KCH N=28 N=29 N=20 N=1
Figure 4-2: Patient recruitment for the Nutrition Tipping Point study
(CXH: Charing Cross Hospital; HH: Hammersmith Hospital; SMH: St Mary’s Hospital; KCH: Kings College Hospital).
117 The majority of patients were recruited from Charing Cross and Hammersmith Hospitals. One patient was recruited from Kings College Hospital during the time that I was recruiting there for the amino acid supplementation study (See Chapter 5). Reasons for patient exclusion have been indicated in Figure 4-2 and Figure 4-3. The majority of exclusions 765 (55%) were due to patient extubation within 48h of their ICU admission. ‘Other’ reasons included patients unable to provide retrospective consent due to learning difficulties or dementia, patients enrolled into other studies, patients receiving long term steroids and wheelchair bound patients.
60%
50%
40%
30%
20%
10%
0%
Figure 4-3: Patient exclusions for the Nutrition Tipping Point study, N=1395
118 4.3.2 Demographics
Demographic data are presented in Table 4-1.
Table 4-1: Demographic data of ICU patient cohort (N=78)
Demographic factors ICU patients N=78
Age (years)
Mean (SD) 59 (16)
Range 24-89
Sex Male n (%) 54 (69.2%)
BMI (kg/m²)
Median (IQR) 25.9 (22 -30.6)
APACHE II score
Mean (SD) 21.6 (7.7)
ICU Length of stay (days)
Median (IQR) 10 (6 -16)
Diagnostic categories:
Vascular surgery 6 (7.6%)
Major Trauma 6 (7.6%)
Traumatic Brain Injury 6 (7.6%)
Cardiology/Cardiac surgery 14 (17.9%)
HIV 2 (2.5%)
Gastroenterology 3 (3.8%)
Respiratory failure 19 (24%)
Neurology/neurosurgery 9 (11.4%)
Gastrointestinal Surgery 3 (3.8%)
Sepsis/Septic Shock 8 (10.1%)
Multi-organ failure 1 (1.3%)
Renal Failure 1 (1.3%)
ICU: Intensive Care Unit; SD: standard deviation; IQR: interquartile range; BMI: Body Mass Index; APACHE II: Acute physiological and chronic health evaluation II; HIV: Human immunodeficiency virus.
119 Baseline values for nutritional and inflammatory markers at day one of measurement are presented in Table 4-2. Notably median CRP and IL-6 values at baseline are higher than the normal range (<5 mg/L and > 1.8pg/mL respectively). Median serum albumin is lower than the normal range (35- 50g/L), 3-MH (a surrogate marker for muscle breakdown respectively) is elevated and nitrogen balance is negative at baseline. No laboratory normal range is quoted for urinary urea per 24h. On day one of measurement an amplified inflammatory response and catabolic response is evident.
Table 4-2: Baseline values for nutritional and inflammatory markers (1st day of measurement). Results are presented as median (IQR).
Biomarker (Normal range) Valid Median (IQR)
C-reactive protein, mg/L 78 110 (67, 196) (0.0-5.0 mg/L)
Serum albumin, g/L 78 22 (17, 26) (35-50 g/L)
Urinary urea, mmol/24h 57 243 (118, 419) (no laboratory normal range)
3-Methylhistidine, µmol/24h 58 274 (178, 448) (75-226µmol/24h)*
Nitrogen balance (PENG equation), g/day 54 -10 (-15, -2.75) (> 0 g/day)
Nitrogen balance (Deacon equation), g/day 54 -5.67 (-9.85, 1.01) (> 0 g/day)
Muscle depth, cm 75 6.78 (4.37, 9.51)
Interleukin-10, pg/mL 78 0.00 (0.00, 211) (7.8pg/ml)**
Interleukin-6, pg/mL 75 34 (8.7, 102) (<1.8pg/ml)***
* (Lukaski et al. 1981;Sjolin et al. 1989) ** (Nemunaitis et al. 2001) *** (Haack et al. 2007)
120 4.3.3 Muscle depth loss in ICU patients
Of the 78 patients entered into this study three were excluded from muscle ultrasound results as they were measured at one time point only. Fourty five patients completed 7 days of ultrasound measurement and displayed a significant muscle depth loss over 7 days on ICU, Table 4-3. Seventeen patients completed 14 days of ultrasound measurement and displayed a significant muscle depth loss over this period, Table 4-4 and (Figure 4-4).
Table 4-3: Muscle depth change over 7 days (N=45)
Starting muscle End muscle P value depth (cm), N=45 depth (cm), N=45
Mean (SD) 7.60 (3.68) 6.50 (3.13) * <0.0001
95% CI 6.45-8.75 5.52-7.47
* paired t-test
Table 4-4: Muscle depth change over 14 days (N=17)
Starting muscle End muscle P value depth (cm), N=17 depth (cm), N=17
Mean (SD) 8.48 (3.18) 6.81 (2.22) * <0.0001
95% CI 6.85 - 10.12 5.67 - 7.95
* paired t-test
121
Figure 4-4: Muscle depth loss over 14 days (N=17)
122 Mean muscle loss was 1.2% per day over 14 days on ICU. Losses over the initial 7 days were accelerated with a mean 1.4% daily muscle depth loss observed over the first week.
Table 4-5: Percentage muscle loss over 14 days (N=17)
Day 3 (n=17) Day 7 (n=17) Day 14 (n=17)
Mean (SD), 95% CI Mean (SD), 95% CI Mean (SD), 95% CI
Percentage loss -3.28 (10.82) -10.11 (11.5) -17.37 (11.28)
-8.84 to 2.29 -16.24 to -3.99 -23.17 to -11.57
Daily loss (%) 1.1% 1.4% 1.2%
123 4.3.4 Nutritional tipping point
Results are presented as median values for muscle depth loss, urinary urea, urinary 3-MH and CRP plotted against day of measurement on ICU to visually explore whether a nutritional tipping point could be identified in patients that remained on the ICU over 14 days. Nitrogen balance data remained negative during ICU stay. Afterwards multilevel modelling results are presented with predicted values for each variable to explore whether a nutritional tipping point could be identified. Lastly the new PINI equation was used to determine if a nutritional tipping point could be defined from this new equation.
Key finding
A nutritional tipping point is unlikely to exist during ICU stay due to a continuing inflammatory process, increasing protein breakdown and progressive muscle depth loss.
In Figure 4-5 median urinary urea (N=14) was plotted alongside muscle depth loss as measured by ultrasound (N=14) in patients that remained on ICU over 14 days. Results are presented as medians (IQR). Urinary urea was used as a surrogate marker for protein breakdown. Muscle depth loss increased over the 14 day period, with a 16% (-27.2 to -8) loss by day 14. Baseline measurement at day 1 was taken as 0% loss. On day 3 a median 1.6% (-12.2 to 2) loss was observed and on day 7 a median 8.3% (-19.3 to -1.4) loss.
Median urinary urea on day 1 of measurement was 377.8 (217.2 - 737.5) mmol/24h, on day 3 was 458 (231 - 596.4) mmol/24h, on day 7 was 493.3 (410.5 to 896) mmol/24h and on day 14 was 225 (145.8 - 701.5) mmol/24h. Median urinary urea peaked at day 7 and decreased by day 14, however values remained elevated during 14 days on ICU.
124 The increasing muscle depth loss and ongoing protein breakdown over 14 days depict an ongoing catabolic phase. This suggests that a nutritional tipping point is unlikely to exist during 14 days of critical illness.
In Figure 4-5 median urinary urea was plotted alongside median urinary 3- MH over time on ICU. Results for both urinary urea and 3-MH peaked at day 7 of measurement and both sets of results remained elevated during 14 days of measurement on ICU. This suggests that catabolism or protein breakdown persists over 14 days on ICU.
In Figure 4-5 median CRP (N=17) was plotted alongside muscle depth loss on ultrasound (N=17) over 14 days. Results are presented as median CRP and IQR. CRP on day 1 of measurement was 110 (50-253) mg/L, day 3 was 161 (90-288) mg/L, day 7 was 169 (27-298) mg/L and on day 14 was 50.6 (15.8-217.3) mg/L.
Values for CRP, although decreasing, have not returned to within a normal range within 14 days on ICU. Muscle depth loss has increased over this time as described. With an ongoing inflammatory process and increased muscle depth loss a nutritional tipping point is unlikely to exist within 14 days of measurement on ICU.
In Figure 4-5 median CRP (N=14) and median urinary urea (n=14) were plotted over 14 days. With an ongoing inflammatory process and persistent protein breakdown a nutritional tipping point is unlikely to exist within 14 days on ICU.
In Figure 4-6 urinary 3-MH (N=14) is plotted alongside median muscle depth loss (N=14) over 14 days on ICU. Median 3-MH values for day 1 were 369
125 (255-572) µmol/24h, on day 3 were 315 (266-539) µmol/24h, on day 7 were 349.5 (193-475) µmol/24h and on day 14 were 207 (65-594) µmol/24h.
3-Methylhistidine values remained elevated (normal range: 75-226µmol/24h) during 14 days on ICU suggesting there is ongoing muscle breakdown. Increasing muscle depth loss over 14 days in addition to raised 3-MH levels suggests that a nutritional tipping point is unlikely to exist within 14 days on ICU and that catabolism persists during this period.
Nitrogen balance remained negative during ICU stay confirming continuing protein breakdown. Median (IQR) nitrogen balance for day 1 was -10 (-14.8 to -3) g/d, day 3 was -6 (-15.8 to -1.8) g/day, day 7 was -9.6 (-19.1 to -3.3) g/day and day 14 results were -4.6 (-17.4 to -0.8) g/day. Nitrogen balance data are presented for 14 days on ICU, Figure 4-7.
126
Figure 4-5: Median urinary urea (n=14), muscle loss (n=17), 3-MH (n=14) and CRP (n=17) over 14 days on ICU
127
Figure 4-6: Median 3-MH (n=14) and muscle loss (n=14) over 14 days on ICU.
128
Figure (a) Nitrogen balance serial measurements
Figure (b) Nitrogen balance per day of measurement
Figure 4-7: Nitrogen balance serial measurements (a) and nitrogen balance values per day of measurement on ICU (b)
129 4.3.5 Nutritional tipping point - univariate analysis
Correlations were performed to assess the strength of association between muscle depth loss with age, maximum CRP, APACHE II score, SOFA score, nitrogen loss, 3-MH, 3-MH: creatinine ratio, mean daily energy intake and mean daily protein intake in patients who remained on the ICU over 7 days. Spearman Rank correlations for non-parametric data were performed, Figures 4-10 and 4-11.
Muscle depth loss was correlated with age to see if older age was associated with more muscle depth loss, a non-significant association was observed, Table 4-6.
The linear association between muscle depth loss and maximum CRP was assessed to see if higher CRP correlated with more muscle loss, a weak negative correlation was observed was seen, Table 4-6.
The linear association between muscle depth loss and APACHE II score was assessed to determine if higher APACHE II scores (sicker patients) were associated with higher muscle loss, a non-significant association was observed, Table 4-6.
Muscle depth change was plotted with the Sequential Organ Failure Assessment (SOFA score). SOFA scores are used on the ICU to track the patients’ status and severity of illness during their stay on ICU. Unfortunately admission SOFA scores only could be used as serial scores were not collected on a daily basis at all the hospitals. Linear association between muscle depth loss and SOFA was assessed to determine if sicker patients demonstrated higher muscle loss, a non-significant association was observed, Table 4-6.
The strength of association between muscle and nitrogen loss was assessed, a non-significant association was seen,Table 4-6.
The strength of association between muscle depth change and the urinary 3- MH: creatinine ratio was assessed. Urinary 3-MH: creatine ratio has been used as 3-MH measurements are thought to have larger interindividual
130 variation, concerning in particular muscle mass, than what the 3-MH: creatinine ratio might have (Lukaski 1981;Sjolin 1989). This ratio was however not used in results presented in this research (multilevel modelling) as one major disadvantage of using the 3-MH: creatinine ratio is that urinary creatinine excretion has been found to be increased during infection and trauma (Long et al. 1981;Sjolin 1989). Investigators studying protein catabolism via urinary 3-MH losses in the critically ill patient tend not to use the urinary creatinine ratio for this reason (Welle 1999). Nevertheless a non- significant association was observed, Table 4-6.
The strength of association between muscle depth change and energy intake was assessed to determine if lower muscle depth was associated with lower energy intake. The mean energy intake over 7 days in 45 subjects was 1119kcal/day, a non-significant association was found, Table 4-6.
Equally, the strength of association between protein intake and muscle depth change over 7 days was assessed to determine if lower protein intakes were associated with lower muscle depth. Mean protein intake over 7 days in 45 subjects was 50g/day, a non-significant association was found, Table 4-6.
The strength of association between nitrogen loss and 3-MH change was assessed. A strong positive association was observed, r=0.715, p=0.000272. (Figure 4-8)
131
Nitrogen loss and 3-MH change
Figure 4-8: Correlation between the change in 3-MH and nitrogen loss over 7 days (N=21) Apart from a clear strength of association between nitrogen loss and 3-MH excretion all other correlations were weak. No confounding factors could however be assessed with univariate analysis, correlation therefore provides limited information.
As CRP is regulated by Interleukin-6, univariate correlation was performed to determine the strength of relationship. A positive association was observed, r = 0.535, p<0.000 in n=192 data points, which was derived from all patients over all time points.
Figure 4-9: Spearman’s rank correlation between CRP and IL-6
132 Table 4-6: Correlation matrix for muscle depth loss
Spearman Muscle Age Maximum APACHE II SOFA Nitrogen 3-MH: Energy Protein depth loss CRP score score loss creatinine intake intake rank ratio correlation
Muscle 1.00 -0.04 -0.289 -0.124 -0.138 -0.025 0.230 -0.031 -0.074 depth loss
Significance - 0.8 0.054 0.442 0.395 0.917 0.358 0.837 0.629
N - 45 45 42 40 20 18 45 45
133
Age Maximum CRP
APACHE II score SOFA score
Figure 4-10: Correlation between percentage muscle loss and age (N=45), max CRP (N=45), APACHE II (N=42) and SOFA score (N=40)
134
Nitrogen Loss 3-MH: creatinine ratio
Energy intake Protein intake
Figure 4-11: Correlation between percentage muscle depth loss and nitrogen loss (N=20), 3-MH: creatinine ratio (N=18), energy intake (N=45) and protein intake (N=45)
135 4.3.6 Nutritional tipping point - multilevel modelling results
Multilevel modelling results confirm the findings that a nutritional tipping point is unlikely to exist during ICU stay. Predicted values for CRP, serum albumin, urinary urea, 3-MH and muscle depth indicate persistent protein breakdown, increasing muscle depth loss and a continuing inflammatory process during 20 days on ICU. Predicted values were calculated from multilevel models, these values represent the range of values one might expect to see for CRP, serum albumin, urinary urea, 3-MH and muscle depth when these markers were modelled against fixed variables (day of measurement, age, gender, BMI and ICU length of stay). Predicted values are presented as means (95% confidence intervals). Tables containing predicted values are presented in Appendix X, p247.
4.3.6.1 Predicted values for C-reactive protein
Although C-reactive protein values decrease over time they remain above the normal range (0.0-5.0 mg/L) during 20 days on ICU, reflecting an ongoing inflammatory response. CRP levels were predicted to be on average ~115 mg/L (95% CI: 95 - 136 mg/L) for the 1st day of measurement during ICU stay, which may fall to ~36 mg/L (95%CI: 16 - 65 mg/L) for the 20th day of measurement.
4.3.6.2 Predicted values for serum albumin
Serum albumin levels remained below the normal range (35-50g/L) during 20 days on ICU. Albumin levels were predicted to be ~20.6 g/L (95% CI: 19.2 - 22.1 g/L) on average for the 1st day of measurement on ICU, which may increase to ~21.4 g/L (18.7 - 24.3 g/L) on average for the 20th day of measurement.
136
Figure 4-12: Predicted values for CRP (mg/L) and serum albumin (g/L) with corresponding 95% confidence intervals according to day of measurement on ICU.
137
4.3.6.3 Predicted values for urinary urea
Predicted urinary urea levels increased during 20 days on ICU suggesting increasing protein loss during this period. Urinary urea levels were predicted to be on average ~262 mmol/24h (95% CI: 205 - 326 mmol/24h) for the 1st day of measurement on ICU, which may increase to ~327 mmol/24h (167- 541 mmol/24h) for the 20th day of measurement. Sicker patients will continue to deteriorate and display increasing protein loss however those patients that are improving should get better and protein loss should improve.
Predicted urinary urea excretion was converted to nitrogen and protein loss using the following equations, see Table 4-7:
Nitrogen loss (g/day) = (urinary urea x 0.028) + 2.2 (losses of nitrogen through faeces or sweat) (Deacon 2009).
Protein loss (g/day) = Nitrogen loss x 6.25.
Table 4-7: Predicted values of urinary urea, nitrogen loss and protein breakdown
Urinary urea Nitrogen loss Protein loss (mmol/24h) (g/day) (g/day)
Day 1 (95% CI) 262 9.54 59.6
(205 - 326) (7.94 – 11.33) (49.63 – 70.8)
Day 20 (95% CI) 327 11.36 71
(167 - 541) (6.88 – 17.34) (43 – 108.4)
Laboratory normal ranges for urinary urea were not provided. In the literature the following urine nitrogen ranges have been identified:
138
In healthy subjects: 11.68 - 17.16g nitrogen per 24h (Bingham and Cummings 1985). This was determined over 28 days whilst subjects continued to eat their usual diet.
In trauma patients: 11.4 - 21.8g nitrogen per 24h (Buonpane et al. 1989).
In healthy patients: 3.88 - 13.6g nitrogen per 24h and 1.8 - 6.39g nitrogen per 24h in septic patients (Long 1981).
There is clearly a lot of variation in reference ranges quoted, my cohort does not initially appear to display accelerated loss. However, daily protein loss may be nearly 60g protein on day 1 of measurement which may increase to 71g loss by day 20. It is clear that unless approximately 60-70g of protein (Table 4-7) was consumed, on average, these losses would not be counteracted. Mean daily protein intake for this cohort was 52.7 g/day, yet estimated mean daily protein requirement was 97g/day. Protein intake therefore only met 54% of their daily protein requirement.
4.3.6.4 Predicted values for 3-methylhistidine
3-Methylhistidine levels, although decreasing over time, remained above the normal range (75 - 226 µmol/24h) over 15 days on ICU suggesting ongoing protein breakdown. 3-Methylhistidine values were predicted to be on average ~336 µmol/24h (287 - 388 µmol/24h) on the 1st day of measurement in, for example, 52 year old patients and decreased to ~255 µmol/24h (174 - 351 µmol/24h) on the 15th day of the measurement, suggesting maximal muscle wasting occurs early on.
139
3-Methylhistidine (µmol/24h)
Figure 4-13: Predicted values for urinary urea (mmol/24h) and 3-MH (µmol/24h) with corresponding 95% confidence intervals according to the day of measurement on ICU
140
4.3.6.5 Predicted values for total muscle depth
Total muscle depth decreased over time during patients’ ICU stay. Starting muscle depth values were predicted to be on average ~7.1cm (6.2 - 8.0cm) on the 1st day of measurement in, for example, 52 year old male patients. This decreased to ~5.7cm (5.0 - 6.5cm) on the 15th day of the measurement. For 63 year old male patients predicted muscle depth was on average ~6.7cm (5.9 - 7.7cm) on the 1st day of measurement which decreased to ~5.7cm (4.9 - 6.5cm) on the 11th day of measurement.
Figure 4-14: Predicted values for total muscle depth (cm) in 52 year old male patients (L) and 63 year old male patients (R) per day of measurement (95% confidence intervals included).
141
4.3.7 The new prognostic inflammatory and nutritional index
The new prognostic inflammatory and nutritional index (new PINI) does not appear to provide any indication of a notional nutritional tipping point.
The new PINI, calculated as log (CRP) /Albumin was assessed over 14 days to determine whether the new PINI would provide any indication of a nutritional tipping point. This calculation was performed in N=45 patients that remained on intensive care for a minimum of seven days. Results are presented as median PINI (IQR). Higher PINI is associated with an inflammatory response.
Table 4-8: The new PINI measured over 14 days
PINI day 1 PINI day 3 PINI day 7 PINI day 14
N 45 45 45 17
Median 0.104 0.103 0.079 0.066
(IQR) (0.072-0.1445) (0.067-0.1425) (0.059-0.1235) (0.055-0.115)
The Friedman test was used as an alternative to repeated measures ANOVA for non-parametric data to detect change over time in PINI scores. Results indicate a non significant reduction in median PINI from days 1 to 14, 0.104 to 0.066, p=0.464. The Friedman test for 3 groups, PINI from days 1 to 7 showed a significant reduction in PINI from 0.104 on day 1 to 0.079 on day 7, p=0.0278.
New PINI score therefore showed a significant reduction between day 1 to day 7, but not between days 1 to day 14. The fall in PINI on day 7 of measurement relates to the decrease in observed CRP values.
Analysis was repeated for the 17 patients that remained on ICU for 14 days.
142
Table 4-9: The new PINI measured over 14 days in 17 patients
PINI day 1 PINI day 3 PINI day 7 PINI day 14
N 17 17 17 17
Median 0.102 0.098 0.109 0.066
(IQR) (0.071-0.119) (0.075-0.137) (0.066-0.137) (0.055-0.115)
New PINI in the 17 patients that remained on the ICU for 14 days did not change significantly over time, p=0.464. Change from day 1 to day 7 was not significant either, p=0.984, nor were changes significant between day 1 and day 3, p=0.552. Day 1 to day 3 change was determined by Wilcoxon matched-pairs signed rank test.
143 4.3.8 Inter-relationships between inflammatory and nutritional markers during critical illness
Multilevel modelling results are presented where CRP, serum albumin, urinary urea, 3-MH, muscle depth, nitrogen balance, IL-10 and IL-6 were modelled against fixed variables: day of measurement, age, gender, BMI and ICU length of stay.
Multilevel modelling revealed that each additional day of measurement on ICU was significantly associated with an expected decrease of CRP, muscle depth and interleukin-10 levels. With every year increase in a patient’s age levels of muscle depth and 3-methylhistidine were significantly reduced and the odds of having detectable IL-10 levels were significantly increased.
4.3.8.1 C-reactive protein
Results from multilevel modelling revealed that the day of measurement was a significant predictor of CRP levels. Each additional day of measurement was associated with an expected decrease in the square root of CRP by 0.24 units (95% CI: -0.36 to -0.13), see Table 4-10.
4.3.8.2 Serum albumin
Multilevel modelling results indicate that each additional day of measurement was associated with an expected increase in the square root of albumin concentration by 0.004 units (95% CI: -0.015 to 0.023), Table 4-10.
Although the predicted values for serum albumin indicate that additional measurement days were associated with an increase in albumin concentration, serum albumin levels increased to ≈ 21.4 g/L only on the 20th day of measurement.
144 Table 4-10: Results from linear mixed modelling to assess the characteristics related to inflammatory and nutritional markers (square root transformations). Results for fixed variables are presented as b-coefficients (95% CI).
CRP (N=78) Albumin (N=78) Urinary Urea (N=60) 3-MH (N=60) Muscle depth (N=75)
coefficient (95% CI) coefficient (95% CI) coefficient (95% CI) coefficient (95% CI) coefficient (95% CI)
Fixed variable
(-0.36, - (-0.015, (-0.186, Days -0.24 0.004 0.100 -0.169 (-0.38, 0.04) -0.021 (-0.03, -0.02) 0.13) 0.023) 0.385)
Age, years -0.153 (-0.23, -0.07) -0.009 (-0.017, -0.0006)
Female gender -0.394 (-0.67, -0.12)
Shaded areas demonstrate significant interactions; CRP: C-reactive protein; 3-MH: 3-methylhistidine
145 4.3.8.3 Urinary urea
Results from multilevel modelling revealed that each additional day of measurement was associated with an expected increase in the square root of urinary urea of 0.100 units (95% CI: -0.18 to 0.385), Table 4-10.
Urinary urea levels increased with each additional day of measurement, this suggests that protein breakdown increased during ICU stay and that catabolism continued during this period. Predicted values suggest that urinary urea levels increased rather than decreased during 20 days on ICU, from ≈ 262 mmol/24h on day 1 of measurement to ≈ 327 mmol/24h on day 20 of measurement. Urinary urea levels may also be affected by dietary protein intake, however only 54% of patients’ daily protein requirement was met.
4.3.8.4 Urinary 3-methylhistidine
Results from multilevel modelling revealed that for each additional day of measurement the square root of 3-methylhistidine levels were expected to decrease by 0.169 units (95% CI: -0.38 to 0.04), controlling for age. Age was found to be a significant predictor of 3-MH levels. One year increase in patient’s age was associated with an expected decrease in the square root of 3-methylhistidine levels of 0.153 units (95% CI: -0.23 to -0.07) measured at the same day of measurement, Table 4-10.
For example, for a 24 year old patient 3-MH predicted levels were on average ~511 µmol/24h (95% CI: 381 to 660 µmol/24h) on day 1 of measurement, which may decrease to ~417 µmol/24h (95% CI: 277 to 584 µmol/24h) on the 9th day of measurement.
146
As age increased there was a decrease in the predicted values of 3-MH. 3- Methylhistidine may decrease from ~233 µmol/24h (95% CI: 184 to 288 µmol/24h) for a 72 yr-old on day 1 of measurement to ~166 µmol/24h (95% CI: 98 to 252 µmol/24h) on day 15 of measurement.
The 3-MH values decreased over time on ICU, however results remain above the normal range suggesting ongoing protein or muscle breakdown. Young patients demonstrate higher rates of protein or muscle breakdown than older subjects do, as measured by urinary 3-MH.
4.3.8.5 Total muscle depth
Day of measurement, age and gender were all found to be significant predictors of total muscle depth. For each additional day of measurement the square root of total muscle depth levels were expected to decrease by 0.021 units (95% CI: -0.03 to -0.02), controlling for age and gender. Age was found to be a significant predictor of total muscle depth. One year increase in a patients’ age was associated with an expected decrease in the square root of total muscle depth levels by 0.009 units (95%CI: -0.017 to -0.0006) measured at the same day and controlling for gender. Females were found to have lower levels of the square root of total muscle depth compared to males (b= -0.394, 95% CI: -0.67 to -0.12) controlling for day of measurement and age, Table 4- 10.
Females demonstrating lower total muscle depth compared with males of the same age are expected. On day 1 of measurement, total muscle depth was predicted to be 5.6 cm (95%CI: 4.4 - 7.0 cm) for a 44 year old female and 7.6 cm (95%CI: 6.6 - 8.8 cm) for a male patient of the same age. The muscle depth reduced to 4.8 cm (95% CI: 3.7 - 6.1 cm) for a 44 year old female participant on the 9th day of the measurement and reduced to 6.2 cm (95%CI: 5.3 - 7.2 cm) for a male participant of the same age on the 14th day of the measurement.
147 For a 52 year old female patient day 1 measurements were predicted to be 5.3cm (95%CI: 4.2 - 6.5cm) and 4.1cm (95%CI: 3.2 - 5.1cm) on day 14. A male patient of the same age have day 1 predicted measurements of 7.3cm (95%CI: 6.4 - 8.2cm) and on day 15 predicted depth of 5.7cm (95%CI: 5.0 - 6.5cm).
A 63 year old female may have predicted total muscle depth of 4.9cm (95%CI: 3.9 - 6.0cm) on day 1 which may reduce to 4cm (95%CI: 3.1 - 4.9cm) on day 11. A male of the same age may have a predicted day 1 muscle total depth of 6.7cm (95%CI: 5.9 - 7.7cm) which may reduce to 5.7cm (95%CI: 4.9 - 6.5cm) on day 11.
A 72 year old female may have predicted total muscle depth of 4.5cm (95%CI: 3.5 - 5.6cm) on day 1 of measurement which may reduce to 3.3cm (95%CI: 2.5 - 4.3cm) on day 15 of measurement. Male patients of the same age may have a predicted total muscle depth of 6.3cm (95%CI: 5.3 - 7.4cm) on day 1 which may reduce to 5.7cm (95%CI: 4.7 - 6.7cm) on day 7 of measurement.
See Figure 4-15 and Figure 4-16.
148
Figure 4-15: Predicted values for muscle depth loss (cm) with corresponding 95% confidence intervals selected for age (years) in female patients
149
Figure 4-16: Predicted values for muscle depth loss (cm) with corresponding 95% confidence intervals selected for age (years) in male patients
150 4.3.8.6 Nitrogen balance
For each additional day of measurement the median for nitrogen balance (PENG equation) was increased by 0.47 units (95% CI: -0.51 to 1.45), Table 4-11.
Median nitrogen balance (PENG equation) on day 1 of measurement was - 10g/day (IQR: -15 to -2.75 g/d). With each additional day of measurement the median nitrogen balance was predicted to increase suggesting that nitrogen balance (PENG) improved over time.
Similarly, for one day increment in the day of measurement the median for nitrogen balance (calculated by the Deacon equation) was increased by 0.18 units (95% CI: -0.31 to 0.68), Table 4-11.
Median nitrogen balance measurement (Deacon equation) on day 1 of measurement was -5.67g/day (IQR: -9.85 to 1.01 g/d). With each additional day of measurement the median nitrogen balance was predicted to increase by 0.18 units suggesting that nitrogen balance (Deacon), although negative, improved over time.
4.3.8.7 Interleukin-6
For each additional day of measurement the median interleukin-6 levels (pg/ml) decreased by 0.84 units (95% CI: -2.6 to 0.90). The lack of association remained when all other predictors were also tested in the model, Table 4-11.
151
Table 4-11: Results from median regression mixed models assessing the characteristics related to nitrogen balance (PENG and Deacon equation) and IL- 6. Results for the fixed variable are presented as a b-coefficient (95% Confidence Intervals).
Nitrogen balance Nitrogen balance Interleukin-6 (PENG equation) (Deacon equation)
N=75 N=58 N=58
coefficient (95% CI) coefficient (95% CI) coefficient (95% CI)
Fixed variable
Days 0.47 (-0.51, 1.45) 0.18 (-0.31, 0.68) -0.84 (-2.6, 0.90)
PENG: Parenteral & Enteral Nutrition Group; IL-6: interleukin-6.
152
4.3.8.8 Interleukin-10
The odds of having levels of interleukin-10 > 0 pg/ml tended to decrease over time. For each additional day of measurement a 5% lower odds (on average) of having IL-10 levels > 0 pg/ml (95% CI: 0.81 to 1.11) was expected. The odds of having detectable IL-10 levels are expected to be higher as age increases. One year increase in patients’ age was associated with 12% higher odds, on average, of having IL-10 levels > 0 pg/ml (95% CI: 1.02 to 1.23), taking into account the day of the measurement, Table 4-12.
More specifically, for the 1st day of measurement patients have on average ~53% lower odds of having interleukin-10 levels > 0 pg/ml (95% CI: 0.14 to 1.62) whilst on the 20th day of measurement it was on average ~84% less likely to have interleukin-10 levels > 0 pg/ml (95% CI: 0.01, 3.01), Figure 4- 17.
Figure 4-17: Predicted values for the odds of having interleukin-10 levels > 0 pg/ml with corresponding 95% confidence intervals, according to day of measurement.
153 Table 4-12: Results from multilevel binary modelling to assess the characteristics related with the odds of having interleukin-10 levels > 0 pg/ml (n=77). Results for the fixed variables are presented as exponentiated b-coefficients (95% Confidence Intervals).
Model 1.0
coefficient (95% CI)
Fixed variable
Days 0.95 (0.81, 1.11)
Age 1.12 (1.02, 1.23)
Shaded areas indicate significant interactions
154
4.3.8.9 Multilevel modelling with nitrogen balance
This analysis was conducted to evaluate the association between nitrogen balance (PENG and Deacon equations) and inflammatory and nutritional markers. The additional effect of fixed variables (day of measurement, age, gender, BMI and total days in ICU) was also considered.
Increased albumin, urinary urea and 3-MH levels were associated with decreased median nitrogen balance levels. Additional days of measurement of urinary urea were associated with increased nitrogen balance.
C-reactive protein was negatively associated with nitrogen balance (PENG) levels, Table 4-13. As the inflammatory process increased, nitrogen balance became more negative. C-reactive protein was however positively associated with nitrogen balance from the Deacon equation, Table 4-13. These differences likely reflect differences in the nitrogen balance equations.
An increase of 1 g/L in albumin levels was associated with an on average decrease of the median for nitrogen balance by 0.33 units (95% CI: -0.66 to - 0.008) in the PENG equation and by 0.29 units (95% CI: -0.52 to -0.05) in the Deacon equation, Table 4-13. The decrease refers to the median of nitrogen balance, therefore adjusted for ‘day of measurement’. Patients with longer ICU length of stay may have lower nitrogen balance, therefore median nitrogen balance tended to be negative. This change may also be due to a small number of patients with serum albumin levels increasing due to albumin administration. As albumin administration data were not captured accurately the exact number of patients receiving albumin is unknown.
Every 1mmol/24h increase in urinary urea was associated with an on average decrease of the median for nitrogen balance (PENG) levels by 0.03 units (95% CI: -0.03 to -0.02) and by 0.02 units (95% CI: -0.03 to -0.02) for the Deacon equation. In addition, the day of measurement was positively associated with the levels of nitrogen balance for both equations and remained significant when all other predictors were also present in the model, Table 4-13. Increased urninary urea was associated with decreased nitrogen
155
balance as protein breakdown persists. These results are expected as urinary urea is included in the nitrogen balance calculation.
Every 1 µmol/24h increase in 3-methylhistidine levels was associated with a decrease of the median for nitrogen balance for both the PENG end Deacon equations, Table 4-13. Increased 3-MH reflects muscle or protein breakdown, this may be associated with a decrease in nitrogen balance.
Table 4-13: Multilevel modelling associations with nitrogen balance
Nitrogen balance (PENG) Nitrogen balance ( Deacon)
An increase of: (95% CI) (95% CI)
1mg/L CRP ↓ 0.002 units ↑ 0.0002 units
(-0.04, 0.03) (-0.03, 0.03)
1g/L albumin ↓ 0.33 units * ↓ 0.29 units *
(-0.66, -0.008) (-0.52, -0.05)
1mmol/24h urinary ↓ 0.03 units * ↓ 0.02 units * urea (-0.03,-0.02) (-0.03, -0.02)
1 day of measurement ↑ 0.39 units * ↑ 0.42 units *
(0.04, 0.74) (0.10, 0.74)
1µmol/24h 3-MH ↓ 0.03 units * ↓ 0.02 units *
(-0.04, -0.02) (-0.03, -0.01)
* Significant associations
156 4.3.9 Additional Analysis
Additional analysis was performed based on the inclusion of ICU outcome (dead/alive), steroid use (yes/no) and nutritional adequacy:
Based on the Schwarz Bayesian Criterion (BIC) - a criterion which is used for model comparison taking into account the explanatory variables and sample size in each model - no other model provided a better fit than the models described previously for each biomarker where day of measurement, age, gender, BMI and ICU length of stay were modelled against each biomarker. The lowest BIC indicates the best fit for the model (indicated in grey boxes in Table 4-14). Analysis therefore confirmed that, according to BIC, inclusion of additional variables (ICU outcome, steroid use and nutritional adequacy) were not significant predictors to any of the measured outcomes. Previous selected models with day of measurement, age, gender, BMI and ICU length of stay retained the best fit.
157 Table 4-14: Biomarkers and best model fit for analysis including ICU outcome, steroid use and nutritional adequacy
df BIC
CRP
Day 4 1381
Day, Death, Steroid, %Energy, %protein 8 1412
Albumin
Day 6 379
Day, Death, Steroid, %Energy, %protein 10 429
Urinary urea
Day 4 855
Day, Death, Steroid, %Energy, %protein 8 874
3-MH
Days, Age 5 818
Day, Age, Death, Steroid, %Energy, %protein 9 830
Nitrogen Balance (PENG)
Day 5 924
Day, Death, Steroid, %Energy, %protein 9 933
Nitrogen Balance ( Deacon)
Day 4 893
Day, %Energy 8 879
Total Muscle Depth
Day, Age, Gender 8 19
Day, Age, Gender, Death, Steroid, %Energy, 12 36 %protein
Interleukin-10
Day 3 211
Day, Death, Steroid, %Energy, %protein 7 217
Interleukin-6
Day 4 2207
Day, Death, Steroid, %Energy, %protein 8 2224
Day: Day of measurement; Death: ICU outcome; Steroid: steroid use; % Energy: % energy intake; % protein: % protein intake; CRP: C-reactive protein; 3-MH: 3-methylhistidine
158 An exception was found for predicting nitrogen balance with the Deacon equation only. Results show that among all factors tested % energy intake was significantly related with the outcome, and its’ inclusion provided the best model. In particular, for 1% increase in energy intake the median for nitrogen balance is increased by 0.09 units (95% CI: 0.05, 0.13), controlling for the day of measurement.
Table 4-15: Nitrogen Balance (Deacon equation) modelled against day of measurement and % energy intake.
Nitrogen Balance
(Deacon)
coefficient (95% CI)
Fixed variable
Intercept -8.04 (-9.81, -6.27)
Day -0.44 (-0.90, 0.02)
% Energy intake 0.09 (0.05, 0.13)
159 4.3.10 Functional Outcomes
Katz Index
The Katz index scoring minimum is zero (very dependent) with a maximum score of six (independent). In this cohort of 78 patients the score improved from zero to one only in 6 (7.7%) patients. No further improvements were observed, thus according to the Katz score all patients remained highly dependent. The improvement from the score of zero to one was seen in the ‘continence’ category (complete self control over bladder and bowel movements).
Barthel Index
The Barthel index scoring total is from zero (highly dependent) to 100 (highly independent). In this cohort of 78 patients 11 (14%) improved from a score of zero to a score of five; 2 (2.5%) patients improved to a score of 10; 2 (2.5%) patients improved to a score of 15 and 3 (3.8%) patients improved to a score of 20. No improvement beyond 20 out of 100 was observed.
The categories in which improvements were seen were in ‘bladder’, ‘bowels’ and ‘transfers’. Bladder function improvements include scores improving from zero (incontinent or catheterised) to five (occasional accident) or to 10 (fully bladder continent). Bowel movement scores improved from zero (incontinent) to five (occasional accident) or to 10 (fully bowel continent). Transfer scores improved from zero (unable, no sitting balance) to five (major help required by one or two people, does have sitting balance). No improvements beyond these categories were observed.
160 4.3.11 Data for surrogate endpoints for future nutritional clinical trials
Mathematically results indicate that a 10 - 20% change in day 7 and day 14 values for CRP, urinary urea and 3-MH may potentially provide feasible sample size estimates for use in future clinical trials in addition to 5 - 20% change in day 7 and day 14 values for albumin and muscle depth. Many of these variables may however be vulnerable to confounding and would unlikely be used as primary endpoints in future nutritional studies. Change in trajectory between day 7 and day 14 variables did not provide feasible sample size estimates, these large sample size estimates imply that much more data would be required in order to accept small differences as statistically significant, however in clinical practice these would not be considered as significant differences, Table 4-16.
161 Table 4-16: Sample size determination from current data set
Estimated sample size for given effect size
Variable Mean (SD) 5% 10% 15% 20%
CRP, mg/L
Day 7 85 (8.55) 32 8 4 2
Day 14 57 (10.96) 140 30 14 8
Change 28 (9.8) 385 97 43 25
Albumin, g/L
Day 7 20.9 (0.61) 3 1 1 1
Day 14 21.1 (0.97) 7 2 1 1
Change 0.2 (0.81) 51497 12875 5722 3219
Urinary urea, mmol/24h
Day 7 282 (36.2) 52 13 6 4
Day 14 306 (64.6) 140 35 16 9
Change 24 (52) 14739 3685 1638 922
3-MH, µmol/24h
Day 7 274 (24.5) 26 7 3 2
Day 14 236 (40) 88 23 11 6
Change 38 (33.2) 2397 600 267 150
Muscle depth, cm
Day 1 6.5 (0.41) 13 4 2 1
Day 14 5.8 (0.39) 15 4 2 1
Change 0.7 (0.40) 1026 257 104 65
162 4.4 Discussion
A notional nutritional tipping point could not be identified during 20 days on ICU, markers point towards a continuing inflammatory response, negative nitrogen balance and progressive muscle depth loss. A conceptual diagram of this period on ICU has been proposed below:
Figure 4-18: Conceptual diagram of the proposed trajectories of pro- inflammatory responses and anabolic switch in the critically ill.
Multilevel modelling revealed that each additional day spent on ICU was significantly associated with an expected decrease of C-reactive protein, muscle depth and interleukin-10 levels. With every year increase in a patient’s age levels of muscle depth and 3-methylhistidine were significantly reduced and the odds of having detectable IL-10 levels were significantly increased.
The biomarkers used to estimate sample size were studied at a point in the patient’s recovery when data suggest that the cohort is catabolic, thus not at a time for nutritional intervention. Sample size estimates may provide some guidance as to the orders of magnitude needed for a late intervention study,
163 based on the biomarkers as endpoints, however estimates are not satisfactory and should be viewed with caution.
4.4.1 Screening and recruitment
Patient recruitment to a study requiring critically ill patients was one of the most demanding aspects of this research. Recruiting from three different hospitals provided the benefit of increasing patient numbers, however, cross- site recruitment required detailed planning, and required research nurses to assist with recruitment when I was on another site. Research nurses were not always available and were not dedicated to this single study, nor were they able to carry out the full data collection. Recruitment was improved when a part time research assistant helped with both patient recruitment and data collection. Training a research assistant to conduct muscle ultrasound greatly facilitated cross-site recruitment and data collection. Thus, this study shows that in order to maximise ICU patient recruitment and data collection at least one dedicated research assistant is required per two hospital sites.
4.4.2 Patient demographics
This cohort of 78 patients appears to be representative of any general intensive care unit, with mean age of 59 years, predominantly male patients (69%), mean APACHE II score of 21.6 and median ICU length of stay of 10 days. The study cohort was compared to national data from the Intensive Care National Audit and Research Centre (ICNARC) annual data for the same period as the study (https://www.icnarc.org/CMS, accessed 1 July 2013). Mean age from national figures was 60 years with predominantly male admissions, 56%, and median length of stay of around 2.2 days.
Median length of stay in my cohort appears longer than national figures however, patients with short length of stay of 1-2 days were excluded from this study. National figures additionally include routine post operative
164 admissions with shorter length of stay. A higher percentage of patients in my study cohort were male in comparison to national figures, 69% versus 55% respectively, this may be due to the majority of trauma and cardiac admissions in the study cohort being male. Ten (83%) trauma admissions in this cohort were male and twelve (86%) of cardiac admissions were male.
4.4.3 Muscle depth change on ICU
Patients on ICU lost a significant amount of muscle depth, 1.2% per day over 14 days. The finding confirms previous studies reporting muscle depth losses of between 1-2% per day in the critically ill (Campbell 1995;Gamrin 1997;Plank and Hill 2000b;Reid 2004). Higher rates of wasting were observed in nine multi-organ failure patients where daily rates of wasting were recorded between 2-9% per day, median 6% per day (Campbell 1995). My study followed the same protocol as Campbell et al and Reid et al, measuring muscle depth on ultrasound over the bicep, thigh and forearm and is the largest study to date in which serial ultrasound measurements were performed in critically ill patients. Reid et al found a median 1.6% muscle depth loss per day in their cohort of 50 ICU patients measured over 7 days, in my study cohort daily percentage muscle loss observed at 7 days was 1.4%. It appears that muscle wasting is at a maximum around the 7 day period and that, although muscle wasting still persists over the following 7 days, the rate of loss is reduced, which may perhaps be associated with recovery. The population included by Reid et al appear to have similar demographics to this study cohort with mean age of 56 years (19-79), 52% male patients, APACHE II score of 17 (2-43) as well as inclusion of medical, surgical and trauma (including head injured) patients.
It is worth noting that different techniques and measurement points are described in the ultrasound literature which may lead to variation of reported results. Some investigators measure muscle depth over three sites (Campbell 1995;Reid 2004) and others measure muscle depth on one limb only, usually on the quadriceps muscle (Gruther et al. 2008). Some use a variety of muscle
165 sites (Cartwright et al. 2013) and others use cross sectional area over the rectus femoris muscle rather than muscle depth (Bloch et al. 2013;Seymour 2009).
Campbell et al found that by combining measurements over the thigh, bicep and forearm this provided the best estimate of lean body mass as compared to skinfold measurements, three combined sites showed a correlation of r = 0.843. The muscle ultrasound and skinfold correlation were performed in 70 healthy subjects. Lean tissue mass was compared between ultrasound and DEXA scanning in 36 subjects (healthy volunteers and patients attending outpatient clinics). The three ultrasound sites demonstrated significant correlation with lean mass measured by DEXA scanning, biceps r=0.788, forearm r=0.842 and thigh r=0.796, p<0.001. In nine ICU patients with multi- organ failure muscle depth measurements were compared to arm circumference measurements over 15 days. Arm circumference measurements showed no consistent pattern of change over time, these measurements may have been confounded by oedema, whilst muscle depth measurements were negatively correlated with time (Campbell 1995).
Gruther et al measured muscle depth in both legs over the vastus intermedius and rectus femoris, on two occasions only. Significant negative correlations between muscle depth and length of stay on the ICU were described, (right thigh p=0.005 and left thigh p=0.004) however p-values only were reported in this study without correlation coefficients.
Cartwright et al assessed muscle thickness of tibialis anterior, rectus femoris, abductor digiti minimi, biceps and diaphragm muscle in 16 ICU patients over 14 days on at least four occasions. Gray-scale analysis from stored images were also performed, each pixel was assigned a value between 0 (black) and 255 (white) to allow calculation of means and standard deviations of the area of interest (Cartwright 2013). No significant reduction in muscle depth was observed, however gray-scale standard deviations reduced significantly for tibialis anterior (33.87 to 28.01, p=0.001) and rectus femoris (31.40 to 28.73, p=0.041).
166 Two studies report measurement of the rectus femoris cross sectional area, one in high risk elective cardiac surgical patients (Bloch 2013) and the other in 26 healthy volunteers and 30 COPD patients (Seymour 2009). Bloch et al reported a 3.5% daily loss in 23 patients measured over one week following cardiac surgery. Median length of stay on the ICU was short for this cohort, one day only (Bloch 2013). Seymour et al did not perform serial measurements, however ultrasound measurements between healthy subjects and COPD patients were compared. A significant reduction in rectus femoris cross sectional area in COPD patients was observed, mean 350mm2 versus 460mm2 in healthy subjects, p=0.001. Rectus femoris cross sectional area was also correlated to quadriceps strength testing in COPD patients. Quadriceps strength was assessed by isometric maximum voluntary contraction and by twitch tension, correlation coefficients were r=0.78 and r=0.69 respectively, p<0.001(Seymour 2009).
As corticosteroid administration is linked to muscle wasting (Hasselgren 2010) steroid administration was recorded in my study cohort. Twenty-three patients (29%) received steroids during their ICU stay of which three patients received Dexamethasone, 15 patients received Hydrocortisone and five Predisolone. (See additional analysis section 4.4.7 for steroid use and muscle depth loss.)
Limitations to the muscle depth ultrasound technique
Muscle ultrasound requires a lot of training and practice. Intra-rater and inter- rater reliability must be tested as appropriate. Very clear standard operating procedures are required to ensure that consistent measurements are taken. Procedures should include consistent positioning of the ultrasound probe on the limb, ensuring that underlying soft tissue is not compressed and using indelible ink to mark the midpoint of each limb to ensure day-by-day consistency of serial measurements. In addition, the operator should ensure that the patient’s foot is pointing upwards at 90° as small changes in the position of the foot could alter the depth measurement.
167 Strengths of the muscle depth ultrasound technique
A strength of this technique is that it has been validated in ICU patients in the presence of oedema. No other non-invasive techniques are readily available to perform serial measurements of muscle mass in the critically ill. It is safe, non-invasive and ultrasound equipment is readily available on most intensive care units nowadays. We have additionally demonstrated good intra-rater and inter-rater reliability with this technique. Depth measurement per se is easier and quicker to perform than the rectus femoris cross sectional area measurement, the latter technique requires a much larger ultrasound machine and transducer.
4.4.4 Nutritional tipping point
The main finding from this study is that a nutritional tipping point is unlikely to exist within 20 days on intensive care due to a continuing inflammatory process, increasing protein breakdown and progressive muscle depth loss.
This is the first study attempting to identify a notional nutritional tipping point, where anabolism starts to exceed catabolism, during a period of critical illness. Results demonstrate increasing muscle depth loss with urinary urea, 3-MH and CRP values remaining above the normal range and nitrogen balance remaining negative. These findings were confirmed by multilevel modelling results.
The clinical significance of this work is that additional nutritional strategies may have to be targeted after a period of critical illness. Whilst patients should continue to receive standard nutrition support during their ICU stay, any additional nutritional strategy may have to be targeted outside of the ICU environment. ‘Aggressive’ or additional nutritional strategies may not be assimilated whilst the process of catabolism is dominant. It appears that a metabolic switch, nutritional tipping point or anabolic phase is likely to take place outside of the ICU environment once the inflammatory process and acute phase response has resolved and protein catabolism has eased off.
168 Although nutritional strategies to promote anabolism may be best targeted outside of the ICU environment, strategies to reduce catabolism might be beneficial during the earlier stages of critical illness such as drug interventions with Propranolol and Oxandrolone.
The prognostic inflammatory and nutritional index (PINI) (Ingenbleek 1985) was the nearest concept to a tipping point study that could be identified however this index was used to stratify patients for risk of complication or death, rather than to identify a point of metabolic shift per se.
One similar study assessed the risk of adverse outcomes that may be modified by nutrition therapy in the ICU setting (Heyland et al. 2011b). This score was developed using prospective data from 597 ICU patients in which multivariate modelling was used to determine what variables should remain in the final scoring system. A conceptual model helped to identify variables associated with starvation, inflammation, nutritional status and clinical outcomes. These variables included age, APACHE II score, baseline SOFA score, number of comorbidities, days from hospital admission to ICU admission and IL-6, which were all significantly associated with 28-day mortality (all p<0.001). As the score increased, duration of mechanical ventilation and mortality rate increased. The authors report that this may help to identify critically ill patients most likely to benefit from aggressive nutrition therapy (Heyland 2011b). Data from my research however suggests patients may not benefit from ‘aggressive’ nutrition therapy during critical illness.
Negative nitrogen balance has been reported previously. Buonpane et al described in a cohort of 12 trauma ICU patients how nitrogen balance remained negative during ICU stay. At baseline a negative nitrogen balance was reported as -14g/day and by day 21 on ICU nitrogen balance was - 4g /day (Buonpane 1989).
Other groups describe ongoing protein breakdown during a period of critical illness. Plank et al described a 13-15% body protein loss observed in a group of ICU patients with severe sepsis and trauma (Plank 2000b), Streat et al describe a 12.5% body protein loss (mean 1.5kg, SE: 0.3kg, p=0.001) in septic ICU patients monitored over 10 days (Streat et al. 1987), Gamrin et al
169 described a 10% loss of protein content in muscle tissue over five days in surgical and trauma ICU patients (Gamrin 1997) and Monk et al describe a 1.62kg (16%) total body protein loss, (p<0.0002), of which 1.09kg (67%) was from skeletal muscle over 21 days. Total body protein was determined by gamma in vivo neutron activation analysis and skeletal muscle mass via DEXA scanning. The equipment used to determine these changes in body composition is not readily available in most institutions (Monk et al. 1996).
4.4.5 Univariate analysis - correlations
A weak correlationwas identified between muscle depth loss and maximum CRP only. No correlation was observed between the amount of muscle depth lost and the amount of calories consumed. This lack of association has been reported previously (Reid 2004). No correlation was observed either between protein intake and muscle depth loss.
There was a strong positive correlation between nitrogen loss and 3-MH. Approximately 80% of nitrogen loss is via urine with the remaining 20% via faeces or via the skin. Of the 80% nitrogen lost via urine (total urine nitrogen), approximately 80-90% will be from urinary urea (urine urea nitrogen). Approximately 1% of total urine nitrogen loss is via 3-MH. Nitrogen loss originates largely from body protein breakdown in critically ill patients. Once 3- methylhistidine is liberated during protein catabolism it cannot be re-utilised and appears in the urine. Therefore the strong correlation observed (Welle 1999).
A positive correlation was furthermore observed between CRP and IL-6, CRP is mainly produced by hepatocytes and regulated by IL-6 (Ho and Lipman 2009).
Univariate analysis provides limited information as potential confounding factors were not assessed. Although multiple regression models might be able to describe relationships between variables and muscle depth loss per se, multivariate multilevel models were chosen as a repeated measures design
170 was used in which there were multiple predictors of a single outcome with the aim of identifying a nutritional tipping point.
4.4.6 Multilevel modelling
Multilevel modelling revealed that each additional day spent on ICU was significantly associated with an expected decrease of C-reactive protein, muscle depth and interleukin-10 levels. With every year increase in a patient’s age expected levels of muscle depth and 3-methylhistidine were significantly reduced and the odds of having detectable IL-10 levels were significantly increased.
Day of measurement was a significant predictor of CRP levels, with each additional day of measurement CRP was expected to decrease. This reflects improvement in patients’ inflammatory status. Predicted values for CRP on the 20th day of measurement still remained above the normal range, therefore although patients may be improving over time the inflammatory process hadn’t completely resolved.
Each additional day of measurement was associated with an expected increase in serum albumin levels. This association was not statistically significant, however hypoalbuminemia over 20 days may be of clinical significance (Arif et al. 2002). Hypoalbuminemia may reflect an ongoing inflammatory response and albumin concentration is a measure of either the presence of disease or the severity thereof (Goldwasser and Feldman 1997). Serum albumin concentrations decrease with expansion of the vascular compartment. Increased vascular permeability in the acute phase of illness may cause increased protein flux and redistribution of albumin (Margarson and Soni 1998). Plasma concentrations of albumin and transferrin are initially decreased, hence being described as ‘negative acute phase proteins’. After this initial reduction in albumin synthesis an increase in overall hepatic protein synthesis is observed, including albumin synthesis (Margarson 1998). Albumin synthesis may therefore follow a biphasic pattern where the synthesis
171 is initially decreased followed by increased production (Mansoor et al. 1997). The hypoalbuminemia observed in my study cohort may also be a reflection of the long half life of albumin, around 20 days (Rothschild et al. 1972). Persistent hypoalbuminemia observed may be due to plasma albumin moving into the extravascular space (Mansoor 1997).
A limitation to data collection concerning serum albumin is that albumin administration was not clearly documented. Only a handful of patients were recorded as receiving human albumin solution 20% at 100ml twice daily, yet dramatic increases in serum albumin levels in three patients suggest that these data were not recorded accurately during data collection for this study. Albumin administration is however used infrequently within the intensive care units in which this study was performed.
Multilevel modelling results indicated that each additional day of measurement was associated with an expected increase in urinary urea levels. This association was not significant however raised urinary urea on the 20th day of measurement may be of clinical significance in particular as predicted 71 g protein loss per day. An increase in urinary urea during ICU stay reflects either overfeeding of protein or increased protein breakdown. As only 54% of the patients’ daily protein requirement was met, it seems that increasing levels of urinary urea reflect continuing protein breakdown during a period of critical illness. Urinary urea excretion is still commonly used to estimate protein breakdown (Welle 1999).
The breakdown of protein, especially from muscle, in the critically ill is in part an adaptive response. Protein breakdown provides amino acid substrates that are used for synthesis of acute phase proteins. These proteins are required for immune response, tissue regeneration, wound healing and replacement of lost blood during trauma and critical illness (Welle 1999). Protein breakdown occurs despite provision of nutrition support in the critically ill (Plank 2000b). Not providing any form of nutrition support during critical illness may however accelerate losses.
172 Studies assessing urine nitrogen excretion are mostly described in trauma patients. Buonpane et al describe mean nitrogen excretion of 18.2 (SD: 3.6) g/day (Buonpane 1989) which may reflect the enhanced protein breakdown experienced by trauma patients or the differences in equations used to determine nitrogen loss.
Few studies have assessed whether urinary urea measurements correlate with other methods of protein metabolism assessment. In children suffering from burn injury urine urea nitrogen correlated well with stable isotope determined protein balance (r2=0.77, p<0.001), although levels of agreement between the two methods were not satisfactory (Prelack et al. 1997).
Urinary collection in critically ill patients requiring continuous renal replacement therapy (CRRT) may be difficult. Serum and effluent (dialysate or ultrafiltrate) urea and creatinine concentrations were however determined for calculations of protein loss in general ICU patients (Leblanc et al. 1998), in septic multi-organ failure patients requiring CRRT (Frankenfield et al. 1994) and in trauma patients undergoing CRRT (Klein et al. 2002). In my cohort, patients on CRRT were excluded from urinary studies as the technique to determine nitrogen excretion from CRRT patients was not used in our institution.
Stable isotope techniques are however far more robust in describing protein metabolism, in particular as whole body protein turnover can be determined. Negative nitrogen balance from whole body protein breakdown was observed from stable isotope studies in head injured patients (Mansoor 1997) and in multi-organ failure patients (Arnold 1993). Increased whole body protein breakdown is described along with increased whole body protein synthesis, the negative balance occurs as the breakdown by far outweighs the synthesis. Reduced muscle protein synthesis has been observed post surgery (Petersson et al. 1994) and following head injury (Mansoor 1997).
173 Limitations to using urinary urea
Urinary study results are dependent on the 24h collection method, spillage or incomplete collections will cause inaccuracies. During this research there were inevitable cases of misplaced 24h urine collections, however clear instructions and documentation limited these occurrences. A further limitation is that our laboratory did not provide a quoted normal reference range for 24h urine urea, ideally a reference range should be derived from the analytical methodology. Furthermore, estimates of urine urea nitrogen may underestimate total urine nitrogen losses in patients with trauma and critical illness as losses via other routes, including wound losses, are likely underestimated (Dickerson 2005;Grimble 1988;Welle 1999). The different equations available in the literature to determine nitrogen excretion make interpretation of results difficult. These limitations should be considered with interpretation of the results of this study. Additionally, in this study patients requiring CRRT were excluded from urinary studies as described.
Strengths of using urinary urea
Urinary urea excretion is used as a surrogate marker for protein breakdown which provides a measure of catabolism. In the absence of more robust techniques, such as protein turnover, 24h urine urea provides information as to the overall nitrogen balance of a patient. Urine collections over a 24h period are much simpler to perform than stable isotope studies. The latter may take up to 6 hours to complete per patient, patients are required to be fasted for 8 hours and multiple staff might be required to conduct the studies in mechanically ventilated patients. An additional strength of urinary urea collections in this study is that serial measurements were performed in patients, this enabled assessment of change over time.
Predicted values for 3-MH reduced over time on ICU, however, results remained above the normal range reflecting ongoing protein breakdown. Age was found to be a significant predictor of 3-MH levels. One year increase in a patient’s age was associated with an expected decrease in 3-MH levels. This
174 suggests that older patients may have lower levels of 3-MH than younger patients have and that younger patients, by displaying higher levels of 3-MH, potentially demonstrate greater muscle or protein breakdown. This may be due to younger patients generally having larger muscle bulk than older patients do. It has previously been reported that ICU patients with ‘thicker’ muscle at the start of their ICU stay lost significantly more muscle depth than patients with ‘thinner’ muscles (Reid 2004). Reid et al studied 50 ICU patients, in which patients with thicker muscle lost 0.8cm (IQR: 0.2 to 2.3) in 7 days compared to 0.35cm (IQR: 0 to 1.1) lost in patients with thinner muscles, p=0.001 (Reid 2004). This observed difference with age and 3-MH levels may also be due to the different diagnostic categories represented in this study cohort. Most of the young patients represented in this cohort were from multiple trauma admissions. Trauma patients are known to exhibit accelerated protein losses, particularly in head injured patients, with reported losses of 1.62kg of body protein and 1.09kg skeletal muscle loss (Mansoor 1997;Monk 1996).
Urinary 3-methylhistidine is derived from the contractile myofibril proteins, actin and myosin that make up approximately one third of the total protein mass of the body. Studying myofibrillar breakdown via 3-MH analysis in urine is therefore a popular manner of studying protein metabolism (Welle 1999).
The urinary 3MH: creatine ratio was not used as creatinine excretion is increased during infection and trauma (Lukaski 1981;Sjolin 1989), variable in patients presenting with acute kidney injury during critical illness (Westhuyzen et al. 2003) and impaired with tubular injury, consequently the use of 3-MH: creatinine ratio is less commonly used the critically ill (Welle 1999).
Limitations to using 3-MH
A potential limitation of 3-MH is that meat intake affects 3-MH excretion, however all patients were exclusively fed via either enteral or parenteral nutrition in my study. Defining a normal range for 3-MH is complicated due to the influence of meat intake on 3-MH excretion levels. Lukaski et al defined a
175 normal range of between 150-226 µmol/24h in healthy men and 75-210 µmol/24 in healthy females on a meat-free diet (Lukaski 1981). Sjolin et al described a mean 215 µmol/24h (SD: 55) 3-MH excretion in men and a mean 150 µmol/24h (SD: 30) 3-MH excretion in women on a meat-free diet (Sjolin 1989).
A further limitation includes that muscle breakdown alone may not be the sole contributor to 3-MH excretion (Chinkes 2005). Actin is present in all cells, therefore 3-MH is not derived exclusively from muscle protein (Welle 1999). Additionally, as 3-MH is derived from actin and myosin, breakdown reflects that of all muscle which may include skeletal, smooth and cardiac muscle. However, using 3-MH as a surrogate measure for muscle breakdown in conjunction with other outcome measures to detect muscle or protein breakdown may reduce this limitation.
The manner in which 3-MH results are reported presents another potential limitation to interpretation of 3-MH data. 3-Methylhistidine may be reported as µmol per kg per 24h (µmol/kg/24h), as µmol 3-MH per mmol creatinine excretion (µmol/mmol creatinine) or as µmol per 24h (µmol/24h). Body weight varies greatly on ICU due to daily fluctuations in fluid balance, reporting of 3- MH per body weight was therefore not selected in this research. Additionally, body weight is usually estimated on ICU and rarely measured. The reporting via creatinine excretion was not selected either in this research as creatinine excretion may vary greatly in ICU patients, in particular in those presenting with acute kidney injury (Westhuyzen 2003).
Strengths of using 3-MH
Strengths of this outcome measure include the ease of use of performing 24h urine collections within intensive care in comparison to other methods used to determine protein breakdown. Additionally, 3-MH produced from protein breakdown cannot be recycled into use for protein synthesis. The only route of elimination of 3-MH from protein breakdown appears to be via urine,
176 consequently 3-MH provides useful index of myofibrillar breakdown (Welle 1999).
This technique remains popular in studies assessing muscle breakdown in the critically ill (Kuhls 2007;Mansoor 2007;Nissen 1996;Stein 1999;Voerman 1995) and can be used as a surrogate marker to assess muscle breakdown despite the known limitations.
Multilevel modelling with muscle depth loss revealed three significant interactions, for each additional day of measurements muscle depth was expected to decrease, controlling for age and gender. Age was a significant predictor, for every year increase in a patient’s age muscle depth was expected to decrease measured at the same day and controlling for gender. Females were found to have lower muscle depth compared to males, again controlling for day of measurement and age. Muscle depth decreasing over time during ICU stay has been discussed previously. Females presenting with lower muscle depth is expected. With increasing age muscle depth decreases, this again is expected, however this appears to be a conflicting finding to what the 3-MH results suggest. Multilevel modelling results suggested that an increase in age was associated with a decrease in 3-MH values. Lower 3-MH values may imply lower breakdown of muscle which may in turn be due to thinner muscles (Reid 2004). It is well recognised that aging is associated with loss of muscle mass otherwise known as sarcopenia (Doherty 2003).
It is worth considering the differences between these two outcome measures, 3-MH and muscle depth change via ultrasound. Although both may be used to reflect muscle breakdown, there are some distinct differences. The muscle depth measurement used in this study assesses change in muscle thickness over the bicep, thigh and forearm only. 3-MH as an index of actin and myosin breakdown reflects all muscle breakdown, which by definition includes skeletal, smooth and cardiac muscle. Additionally, actin is present in cells other than muscle (Welle 1999) which was discussed as a limitation in the use of 3-MH as a marker for muscle breakdown. Thus, older patients may have lower muscle depth over time on ICU where younger patients may display
177 higher rates of protein breakdown overall. This again may be related to the diagnosis differences between the older and younger patients in this cohort with the younger patients mostly representing a trauma population.
Multilevel modelling results for nitrogen balance revealed non-significant associations between day of measurement and nitrogen balance. Median nitrogen balance for both the PENG and Deacon equation increased with each additional day of measurement. Nitrogen balance therefore improved over time. Buonpane et al described how nitrogen balance improved over 28 days on ICU to 4g/day (Buonpane 1989).
Associations between CRP, albumin, urinary urea and 3-MH were additionally assessed with nitrogen balance. Increased albumin, urinary urea and 3-MH levels were associated with decreased median nitrogen balance levels. An additional day of measurement of urinary urea was associated with increased median nitrogen balance.
An increase in serum albumin levels was associated with a decrease in nitrogen balance for both the PENG and Deacon equations. This finding cannot be explained by the data collected for this research study. Patients that remain on ICU for longer may already have lower levels of nitrogen balance, therefore median nitrogen balance tended to be negative. This small change may additionally be due to a small number of patients with increasing levels of serum albumin due to albumin administration. Unfortunately data collection on albumin administration was incomplete in this study. Additional potential confounders for serum albumin levels, such as inflammatory status, were not assessed at the same time in the model.
An increase in urinary urea was associated with a decrease of the median for nitrogen balance levels for both the PENG and Deacon equations. This is expected as increased urinary urea levels reflect ongoing catabolism, which is demonstrated by negative nitrogen balance. Furthermore, urinary urea is used in the calculation to determine nitrogen balance.
An increase in 3-methylhistidine levels was associated with a decrease of the median for nitrogen balance for both the PENG and Deacon equations.
178 Increased 3-MH reflects protein breakdown: this is associated with decreased nitrogen balance.
CRP was negatively associated with nitrogen balance (PENG) levels, yet positively associated with nitrogen balance from the Deacon equation. The differences observed here reflect the inherent differences of these equations. Few hospitals have the facility to measure total urinary nitrogen directly. The use of equation may underestimate losses from the skin, wounds and gastrointestinal losses and may therefore underestimate total nitrogen losses in the critically ill (Dickerson 2005;Grimble 1988).
Multilevel modelling results for IL-6 showed a non-significant reduction in median IL-6 levels over time. This is expected as IL-6 levels decrease over time as patients improve. IL-6 is both a pro- and anti-inflammatory cytokine which promotes acute-phase protein synthesis (Callahan 2009).
Multilevel modelling analysis for IL-10 indicated that day of measurement was a significant predictor for IL-10 levels. The odds of having detectable IL-10 levels decreased over time. Results also indicated that the odds of having detectable IL-10 levels were expected to be higher as age increases, when day of the measurement was taken into account. Interleukin-10 production has been found to be higher from leukocytes in the elderly than from those in young subjects (Cakman et al. 1997). Interleukin-10 release by leukocytes from the elderly occurs faster and in higher concentrations than that released by leukocytes from young subjects (Rink et al. 1998).
179 4.4.7 Additional analysis: Inclusion of ICU outcome, steroid use and nutritional adequacy
Day of measurement remained a significant predictor of CRP levels, additional analysis including day of measurement, ICU outcome, steroid use and nutritional adequacy were not significant predictors of CRP levels.
For serum albumin levels, urinary urea and nitrogen balance (PENG equation), day of measurement was best associated with biomarker levels, not the additional analysis including day of measurement, ICU outcome, steroid use and nutritional adequacy.
Day of measurement and age remained the best predictors of 3-MH levels, with age being a significant predictor. Additional analysis including day of measurement, ICU outcome, steroid use and nutritional adequacy were not significant predictors.
Nitrogen balance (Deacon equation) was the only outcome measure where an increase in energy intake was significantly related to an increase in the median levels for nitrogen balance.
Day of measurement, age and gender remained significant predictors for total muscle depth. Inclusion of additional analysis consisting of day of measurement, age, gender, day of measurement, ICU outcome, steroid use and nutritional adequacy were not significant predictors of total muscle depth.
Day of measurement remained the best predictor for both IL-10 and IL-6 levels, not the additional analysis including day of measurement, ICU outcome, steroid use and nutritional adequacy.
The models shown in Table 4.14 (p158) represent relationships that were sought between biomarkers and variables, the variables include day of measurement on ICU, age, gender, BMI, ICU length of stay, ICU outcome, steroid use and nutritional adequacy. For example, does day of measurement on ICU relate to CRP levels? Previous analysis demonstrated a significant relationship existed between CRP and day of measurement, for each additional day of measurement on the ICU, the CRP value reduced, which
180 was associated with patients recovering. No significant relationship was identified when CRP or any of the other biomarkers, including total muscle depth, were modelled against ICU outcome, steroid use and nutritional adequacy. The only significant outcome was for nitrogen balance (Deacon equation), where an increase in energy intake was related to an increase in nitrogen balance levels.
These models were used to explore the inter-relationships between variables and biomarkers. The rationale for exploring these inter-relationships was firstly to see if a nutritional tipping point could be identified from overall inspection of the data and secondly to understand the relationships of biomarkers and variables such that it would inform the design of a future nutritional clinical trial in addition to the surrogate endpoint data.
Clinically, multilevel modelling results can therefore be summarised: for each additional day spent on ICU the levels of CRP, total muscle depth and IL-10 are expected to decrease. For every year increase in a patient’s age levels of total muscle depth and 3-MH will reduce and the odds of having detectable levels of IL-10 are expected to increase. An increase in energy intake is related to an increase in nitrogen balance levels from the Deacon equation.
It is perhaps surprising that steroid use and nutritional adequacy did not seem to be significant predictors of total muscle depth. A limitation to the steroid data is that the dose of steroids administered was not included in this analysis, only whether patients received steroids during their ICU admission or not. Future studies should explore whether any relationship exists between different doses of steroid administration and muscle wasting as high-dose steroid administration has been linked to muscle wasting (Hasselgren et al. 2010). Nutritional adequacy was defined as calories received versus prescribed and grams of protein received versus prescribed. Calorie prescription was not derived from indirect calorimetry but from predictive equations as assessed by the unit dietitian and/or the researcher which is a further limitation to current data. The data is furthermore limited by the fact that day 1 of study was not necessarily day 1 of ICU admission as patients were recruited within 72h of their ICU admission.
181 4.4.8 Prognostic Inflammatory and Nutritional Index
The ‘new PINI’ did not provide any additional suggestion of a nutritional tipping point, it has been used mostly to predict risk of infection or death. The PINI has been used rarely in the past within critical care, in trauma patients PINI reduced over time (Vehe et al. 1991). It has furthermore been used in burns patients as prognostic indicator for infection or death (Gottschlich et al. 1992) and in respiratory failure patients on ICU where the PINI formula was used as an independent predictor of the weaning trial outcome in mechanically ventilated patients (Schlossmacher et al. 2002). No study within critical care was identified where PINI was used to identify a nutritional tipping point per se.
4.4.9 Functional Outcomes
The Katz and Barthel indices did not appear to detect the potential subtle changes in function that ICU patients might display. Low scores could be due to patients genuinely lacking the ability to demonstrate improvement, or may be due to insensitivity of the tool to detect meaningful change in patients with lower levels of function, highlighting a ‘floor’ effect in both tools.
The Barthel index may be better suited to assess functional status in patients discharged from critical care (van der 2008). Reliability for both the Katz and Barthel indices has not yet been established in critical care (Black et al. 2001). More tools have recently been designed to detect functional recovery in the critically ill, these are discussed in Chapter 5 (Corner et al. 2013;Denehy et al. 2012;Zanni et al. 2010).
4.4.10 Surrogate endpoints for future nutritional clinical trials
Sample size estimates for CRP, serum albumin, urinary urea, 3-MH and muscle depth may mathematically appear as reasonable estimates from day 7
182 and day 14 results, however the clinical significance of these changes may not be large enough. The large sample sizes observed in the change variable mean that much more data would be required in order to accept very small differences as statistically significant, although in clinical practice these are unlikely to be of clinical significance.
Endpoints of any study should reflect the intervention studied and should not be vulnerable to confounding. If confounding factors could not be measured directly the study relies only on the process of randomisation to adjust for imbalances between groups. Small studies may be vulnerable to confounders that cannot be measured, therefore an increased sample size may be required to anticipate requirement for post hoc adjustment if there is accidental imbalance in the randomization.
C-reactive protein and albumin levels will be vulnerable to confounders as markers of an inflammatory state. Serum albumin is additionally not a sensitive nutritional marker due to the long half life of albumin, due to the large intra-vascular pool and due to albumin’s role as a negative acute phase protein. Although data for CRP, albumin, urinary urea and 3-MH were mathematically explored these data may be too vulnerable to confounders not related to the intervention. This means in practice that although these data may be collected in nutritional trials, they should not be used as primary endpoints to power nutritional studies.
Surrogate endpoints to detect rate of change of collected biomarkers may be feasible in some studies, however from the current data set this does not appear to be the case.
It can therefore be concluded that the biomarkers used to estimate sample size were studied at a point in the patient’s recovery when the data suggest that the cohort is catabolic, thus not at a time for nutritional intervention. Early pharmacological intervention, such as Oxandrolone or Propranolol, may be used in earlier stages as this type of intervention is designed to have some aggresive effect on the catabolic state of the patient. These sample size estimates therefore provide some guidance as to the orders of magnitude
183 needed for a late intervention study, based on the biomarkers as endpoints, however estimates are not satisfactory and should be viewed with caution.
Nutrition studies in the critically ill are often confounded by heterogeneous patient groups and by poorly selected outcome measures. Heterogeneous populations in studies in the critically ill may result in conflicting findings in studies assessing similar nutritional interventions (Vincent and Preiser 2013). Studying subsets of patients in which risk stratification can be performed is more likely to highlight the type of intervention that might benefit these specific patients, rather than studying a large cohort (Berger and Mechanick 2013). Nutritional markers per se may not be the best outcome measures to use in trials of nutrition support. When selecting outcome measures to assess efficacy of nutrition one should consider whether nutrition is expected to have an effect on mortality, ICU length of stay, rate of complication or indeed whether nutrition should be used as a surrogate marker for functional recovery (Vincent 2013). Broader outcome measures assessing health-related quality of life or physical function may need to be considered instead in the critically ill provided with nutritional interventions (Heyland 2013). Equally, the chosen endpoint should reflect the intervention studied.
4.5 Conclusion
A notional nutritional tipping point does not exist during a period of critical illness, patients demonstrate a continuing inflammatory response, increased protein breakdown and progressive muscle depth loss. Patients are unlikely to benefit from aggressive nutritional strategies during this time, however standard nutrition support should be continued during their ICU stay. Nutritional strategies that might promote anabolism are likely best targeted outside fo the ICU environment. Drug interventions aimed at reducing catabolism, such as Oxandrolone and Propranolol, may however be more effective in the earlier stages of critical illness. Multilevel modelling revealed various inter-relationships, each additional day spent on ICU was significantly
184 associated with an expected decrease of C-reactive protein, muscle depth and interleukin-10 levels. With every year increase in a patient’s age expected levels of muscle depth and 3-methylhistidine were significantly reduced and the odds of having detectable IL-10 levels were significantly increased. Sample size estimates calculated from the current data set provide some guidance as to the patient numbers required for a late intervention study, based on biomarkers as endpoints, however estimates are not satisfactory and should be viewed with caution.
185 Chapter 5. Essential Amino Acid Supplementation in Critically Ill Trauma Patients – A Feasibility Study
5.1 Introduction
In this chapter the feasibility of using a leucine-enriched essential amino acid supplement in mechanically ventilated trauma patients will be discussed. The methods used in this study were described in Chapter 2. Similar outcome measures were used as in the Nutritional Tipping Point study. Assessment included markers of inflammation and cytokines, urinary studies to assess muscle breakdown and assessment of muscle depth change on ultrasound. In this study protein turnover was assessed in trauma patients via stable isotope studies using a 1-13C leucine tracer.
This study was conducted at Kings College Hospital in London in the adult Medical and Surgical Intensive Care Units. Kings College Hospital is one of London’s major trauma centres. Critically ill trauma patients only were recruited to this feasibility study.
5.1.1Research question
Leucine-enriched essential amino acid (L-EAA) supplementation may attenuate muscle depth loss in mechanically ventilated trauma patients.
5.1.2 Research aims
1) To determine whether the trauma population represent a feasible group to study within critical care.
2) To determine whether the intervention, leucine-enriched essential amino acid supplementation, is feasible to study within critical care.
186 3) To assess whether the outcome measures and endpoints used in this study would be both practical and scientific for use in future nutritional clinical trials.
4) To develop a strategy that will minimise muscle mass decline during critical illness.
5.2 Methods
5.2.1 Study groups
Trauma patients were selected for this study on the basis that they were thought to provide a reasonable study population within critical care. They are generally younger patients with less comorbidity and were thought to stay on ICU for a reasonable length of time. Two groups were studied:
Control group (N=4): The control group received usual care where standard enteral feed was provided according to patient need.
Interventional group (N=4): The interventional group received standard enteral feed in addition to leucine-enriched essential amino acid supplementation administered five times per day via enteral feeding tubes.
5.2.2 Study outcome measures
Inflammatory markers C-reactive protein (CRP) (mg/L) and serum albumin levels (g/L) were obtained from the Kings College Hospital clinical laboratory. CRP and albumin levels were collected on days 1, 3, 7 and 14 of the study. Cytokines IL-6 (pg/ml) and IL-10 (pg/ml) were measured from plasma samples on days 1, 3, 7 and 14 of the study, see chapter 2, section 2.1, p64.
187 Muscle depth change (cm) was measured by muscle ultrasound, on alternate days in the first week of study and twice weekly thereafter, see chapter 2, section 2.9, p72.
Urinary studies for 3-methylhistidine (µmol/24h) and urinary urea (mmol/24h) were performed on days 1, 3, 7 and 14 of the study, see chapter 2, section 2.7, p69.
Nitrogen balance (g/day) was calculated as described in chapter 2, section 2.8, p70. Nitrogen intakes were estimated from artificial nutrition support (enteral nutrition only in this study) and the amino acid supplement provided to patients during their ICU stay.
Stable isotope studies were performed to determine protein turnover in trauma patients, see chapter 2, section 2.12, p80. These studies were planned to take place on days 1, 5 and 10 of the study period.
5.2.3 ICU data collection
Data collected included estimated weight (kg) and height (m) of patients to ascertain pre-admission body mass index (BMI, kg/m2), age (years) and gender. Daily energy (kcal) and protein (grams) intakes were recorded from observational charts. Estimated energy and protein requirements were calculated, based on recommended requirements for the critically ill, 25- 30kcal/kg/day for energy and 1.2g/kg/day for protein (Kreymann 2006;McClave 2009). Enteral feed tolerance was monitored by assessing gastric residual volumes (ml) and incidence of vomiting. Occurrence of diarrhoea was also recorded.
ICU demographics were recorded as descriptors of the type and severity of the injury: diagnosis, past medical history, Acute Physiology and Chronic Health Evaluation (APACHE) II scores, Injury Severity Score (ISS), Glasgow Coma Scale, ventilation status, use of renal replacement therapy, ICU length of stay (days), ICU outcome (dead or alive) and hospital length of stay.
188 Functional outcomes (Katz and Barthel indices) were recorded as well as the MRC strength assessment, see chapter 2, sections 2.10, p77 and 2.11, p78 respectively.
Other ICU data thought to potentially influence muscle integrity, or muscle depth measurements, were recorded including use of steroids during ICU stay, sedation use, insulin administration (units/24h), fluid balance (ml/24h) and physiotherapy (particularly mobility or rehabilitation therapy).
5.2.4 Screening and recruitment
Screening and recruitment took place from 1 May 2012 until 31 January 2013 on the surgical and medical critical care units at Kings College Hospital London, with combined bed capacity of 32 beds.
Inclusion criteria:
Trauma admissions (head or multiple trauma) expected to be ventilated for > 48h
Patients over 18 years of age
Patients entered into the study within 72h of their ICU admission.
Exclusion criteria:
Medical or non-trauma surgical ICU patients
Patients enrolled into another interventional clinical trial
If enteral feeding was contra-indicated
Congestive cardiac failure
End stage renal failure
189 Chronic obstructive pulmonary disease
Chronic liver disease (cirrhosis)
Patients not for active medical treatment
Extensive psychiatric illness precluding provision of informed consent.
5.2.5 Randomisation
A computer generated block randomisation schedule was provided by the trial statistician. The schedule numbers were placed into sealed, numbered envelopes by an independent research nurse not located at the study site. The sealed envelopes were placed into the trials folder. After assent was obtained a research nurse not involved in the study randomised the patients to either standard care or the L-EAA supplement.
5.2.6 Intervention: Leucine enriched essential amino acid supplementation
Leucine-enriched essential amino acid powder supplementation (Nutricia Ltd., Wiltshire, United Kingdom) was administered in 100ml solutions given five times per day via enteral feeding tubes, see chapter 2, section 2.13, p89. The composition of the supplement is included in Table 5-1. In Figure 5-1 the L- EAA supplement can be seen. Interventional patients received the L-EAA supplement in addition to their standard enteral feed. No placebo product was used during this study as the manufacturer was not able to provide a suitable product.
190 Table 5-1: Composition of L-EAA supplement
Amino Acid grams
Cystine 0.8
Histidine 0.8
Isoleucine 2.1
Leucine 9.5
Lysine 2.5
Methionine 0.8
Phenylalanine 1.2
Threonine 2.2
Tryptophan 0.6
Tyrosine 1.8
Valine 2.7
The 25g daily supplement was provided in 5 doses per day. The branched- chain amino acid ratio for valine: leucine: isoleucine of 1:5:1 was based on a previous study by Borsheim et al where the additional leucine was thought to promote muscle protein synthesis (Borsheim 2008).
191
Figure 5-1: Leucine-enriched Essential Amino Acid supplement showing the supplement as delivered (in white pots) and as dispensed for the patient (bottles)
(Photo L Wandrag)
5.2.7 Study plan
The study plan and data collection time points can be viewed in Figure 5-2 below. The L-EAA supplement was given daily until discharge from ICU or until enteral feeding tubes were removed. Stable isotope studies were planned for days 1, 5 and 10, however these studies invariably had to be advanced as patients’ length of stay on ICU were shorter than anticipated. Additionally, as three people were required to perform each study they could only be performed on days when two esearch nurses were on duty.
192 Day of Study
Screening & Consent 1 3 5 7 10 14
• CRP, Alb • CRP, Alb Stable • CRP, Alb Stable • CRP, Alb Isotope Isotope •IL-6, IL-10 •IL-6, IL- •IL-6, IL-10 study study • IL-6, IL-10 10 • 3-MH • 3-MH • 3-MH • 3-MH • Nitrogen • Nitrogen • Nitrogen balance Balance • Nitrogen Balance Balance • Katz & •Stable • Katz & Barthel Isotope Study • Katz Barthel &Barthel • Katz & Barthel
Study 2: Muscle ultrasound alternate days for week 1, twice weekly thereafter
Study 2: EAA supplement given daily via enteral feeding tubes up to ICU discharge
Figure 5-2: Study plan of essential amino acid supplementation study
5.2.8 Statistical Analysis
Two-sided tests were used to all hypotheses tested, and the significance level was set to 0.05. Descriptive statistics were performed using SPSS V.20 (IBM Corp. Released 2011. IBM SPSS Statistics for Windows, Version 20.0. Armonk, NY: IBM Corp.).
Results for the continuous variables are presented as median (1st-3rd quartile). Differences between the control and essential amino-acid groups were assessed with Mann-Whitney U-test.
193 5.3 Results
5.3.1 Screening and recruitment
During the period from 1 May 2012 until 31 January 2013, 324 patients were admitted to the surgical and medical intensive care units at Kings College Hospital London. The combined bed capacity for the two units was 32 beds. Of the 324 patients 84 patients (25.9%) were major trauma admissions into critical care and 8 patients (9.5%) were recruited to this feasibility study. Patient recruitment is illustrated in Figure 5-3.
N = 324 ICU patients screened
N = 240 non-trauma patients excluded
N = 84 major trauma ICU N = 76 excluded patients screened N=34 Extubated/Lev el 2 patients N=8 Other interv entional study N=6 No research staf f N=5 Poor prognosis N=5 Ref used N=5 Psy chiatric history N=2 No isotopes N=2 No NOK N=2 No NGT N = 8 patients randomised N=2 EN contra-indicated N=2 CCF N=1 Patient 15 y ears old N=1 Recent ICU admission N=1 Re-admission
N = 4 Control N = 4 L-EAA patients patients
Figure 5-3: Patient recruitment for the feasibility study
CCF: Congestive cardiac failure; EN: enteral nutrition; ICU: Intensive care unit; L-EAA: leucine-enriched essential amino acid supplement; NGT: nasogastric tube; NOK: next of kin.
194 5.3.2 Demographic data
Demographic data is summarised in Table 5-2.
Table 5-2: Demographic data of trauma ICU patient cohort
Control L-EAA
Demographic factors (n=4) (n=4) P value
Age (years)
Median (IQR) 59.5 (26.2, 57.7) 52.5 (32, 93) 0.66 a
Sex
Male n (%) 2 (50) 4 (100) 0.43 b
BMI (kg/m²)
Median, (IQR) 22.95 25.3 0.69 a
(20.13, 27.35) (21.50, 27.68)
APACHE II (0-71)
Median (IQR) 13.5 (13, 18.5) 15 (11.25, 21) 0.88 a
Injury Severity Score (0-75) 27 (16, 32.75) 41.5 (24.5, 48.75) 0.20 a Median (IQR)
Diagnosis n (%)
Multiple trauma (no TBI) 1 (12.5) 1 (12.5)
TBI only 1 (12.5) 1 (12.5)
Trauma including TBI 2 (25) 2 (25)
ICU LOS (days)
Median (IQR) 4.5 (2.5, 5.75) 9.5 (8.0, 13.25) 0.03 a
Hospital LOS (days)
Median (IQR) 14.5 (4.5, 27.50) 14 (9.25, 26.25) 0.69 a a Mann-Whitney U test, b Fischer’s exact test, L-EAA:Leucine-enriched essential amino acid; IQR: Interquartile range; BMI: Body Mass Index; APACHE II: Acute Physiology and Chronic Health Evaluation II; TBI: Traumatic brain injury; ICU: Intensive Care Unit; LOS: Length of stay.
195 Six (75%) of the patients suffered traumatic brain injuries. Two patients presented with polytrauma without traumatic brain injuries.
There were no significant differences between the two groups apart from ICU length of stay. Patients in the L-EAA group appear sicker with higher APACHE II and injury severity scores than the control patient group. Although these differences did not reach significance this may be due to the small sample size. In the control group, one patient was discharged to his referring hospital after 4 days on ICU. Another patient died after 5 days on ICU, the third patient was transferred to the HDU after 2 days in ICU and the fourth control patient was transferred to HDU after 6 days on ICU.
5.3.3 L-EAA supplement received
The L-EAA doses received are presented in Table 5-3. The majority of omissions were due to unavoidable clinical reasons.
196 Table 5-3: L-EAA doses received and reasons for omissions
Participant Doses received Percentage Reasons for number received omisions
P401 0 out of 5 0% No NGT, discharge to HDU
P403 45 out of 53 85% Vomiting
NBM for stable isotope study
P404 7 out of 19 37% Vomiting, NBM for isotope study, procedures, RIP
P407 39 out of 47 83% NBM for isotope study, 2 x doses misplaced on ward
(NGT: nasogastric tube; HDU: high dependency unit; NBM: nil by mouth; RIP: rest in peace)
5.3.4 Outcome measures
Outcome measures could not be compared directly between groups due to the small sample size and short length of stay, however feasibility of data collection for the whole cohort is described.
C-reactive protein data were collected on 16 (100%) occasions, data for the whole cohort over 14 days ranged from 1 - 312 mg/L. Serum albumin data were collected on 16 (100%) occasions, data ranged from 22 - 39 g/L. One patient received albumin administration. Interleukin-6 data were collected on 15 (94%) occasions, data ranged from 9 -141 pg/ml. Interleukin-10 data were collected on 15 (94%) occasions, data ranged from 0 - 2100 pg/ml. Urinary urea, 3-MH and nitrogen balance data were collected on 13 (81%) occasions, three urine collections could not be performed as patients were transferred out of ICU. No urine samples were misplaced in this study. Urinary urea data ranged from 51 - 382 mmol/24h; 3-MH from 144 - 660 µmol/24h and nitrogen balance from -10.6 to 7.4 g/day (PENG eqation) and -6.2 to 11.2 g/day
197 (Deacon equation). Serial muscle ultrasound was only performed in 4 (50%) patients due to short length of stay, data ranged from 4.43 - 11.56cm.
The Katz index remained zero for all patients throughout the study, the Barthel index score changed from zero on one occasion as one patient sat out in a chair, however major help was required to position this patient. Manual strength testing could not be performed in this cohort as patients were not able to respond appropriately to the screening questions.
Stable isotope studies were performed on 5 (31%) occasions. Patients remained in a negative whole body protein balance, data ranged from - 3.9 to -2.1 g/day. Data collection for this outcome measure was limited due to short ICU length of stay and as two research nurses were required each time to assist me with stable isotope studies.
5.3.5 Leucine kinetics
In Table 5-5 leucine kinetic data are presented from stable isotope studies performed in trauma patients. These data were compared to leucine kinetic data from Mansoor et al for both a trauma ICU and healthy subject cohort (Mansoor 2007). A medical ICU (Whyte 2010) and a cardiac surgical ICU cohort (M.B. Whyte, personal communication) have additionally been included for comparison.
Leucine rate of appearance seems highest in my cohort, suggesting that these patients may have exhibited the highest rates of whole body protein breakdown, followed by the Mansoor et al trauma cohort and then the Whyte et al’s medical ICU cohort. Interestingly, the healthy control subjects and the Whyte surgical ICU patients displayed similar levels of Leu Ra, suggesting that the cardiac surgical patients were not catabolic. Concerning whole body protein synthesis as assessed by NOLD, this was highest in the Mansoor et al trauma cohort followed by my patient cohort, then healthy controls and lastly medical ICU patients. These results are interesting as this suggests that both trauma cohorts exhibited higher whole body protein synthesis rates than
198 healthy subjects. In the Mansoor et al cohort the mean SAPS II score was 42 (SE: 3) out of 160 (Mansoor 2007). Mean injury severity score in the Kings College Hospital cohort of 8 trauma patients was 32 (SD: 12.5) out of 75.
The overall leucine kinetic balance appeared most negative in my cohort, followed by the medical ICU cohort and then Mansoor et al’s trauma cohort. My trauma patients demonstrated a 34% higher whole body protein breakdown than synthesis, the medical ICU cohort showed a 32% higher rate of whole body protein breakdown than synthesis, Mansoor et al’s cohort exhibited a 8.5% higher whole body protein breakdown than synthesis and healthy controls showed a 6.5% lower breakdown than whole body protein synthesis.
These data should be interpreted with caution however as I was unable to perform stable isotopes in a healthy cohort due to time constraints, therefore although it appears as though the patients in my trauma cohort demonstrated the most negative leucine balance this cannot be concluded without confirmation of leucine turnover in a healthy cohort.
199 Table 5-4: Leucine kinetics comparator data
Leu Ra NOLD Leu balance
(µmol/kg/min) (µmol/kg/min) (µmol/kg/h)
Trauma ICU
N=5 3.22 2.13 -65.4
Median (IQR) (3.18-3.31) (1.74-2.37) (-93.33 to -51.46) (Wandrag, unpublished)
Trauma ICU
N=12 2.62 2.40 -13.2
Mean (SE) (0.24) (0.21) (-1.8)
(Mansoor 2007)
Healthy Control N=8 1.69 1.80 6.6 Mean (SE) (0.11) (0.07) (-0.04) (Mansoor 2007)
Medical ICU
N=10 2.40 1.63 -46.2
Mean (SE) (0.22) (0.17) (-3)
(Whyte 2010)
Cardiac Surgery ICU
N=8 1.69 NR NR Mean (SE) (0.07) (M.B. Whyte, unpublished)
Leu Ra: leucine rate of appearance, an index of whole body protein breakdown; NOLD: non- oxidative leucine disposal, an index of whole body protein synthesis. Leu balance is NOLD minus Leu Ra. IQR: interquartile range; NR: not reported; SE: standard error.
200 5.4 Discussion
5.4.1 Recruitment
Recruitment for this type of study in critical care was time consuming and resource intensive. Of the 84 major trauma admissions to ICU eight patients were recruited to this study. This number was lower than anticipated, 9.5% of screened patients were recruited. The next of kin of five potential study patients refused assent, mostly as they were too distressed to make a decision. Length of stay on the ICU was the only significant difference observed between the two groups with the L-EAA group displaying a longer length of ICU stay. The L-EAA group appeared sicker as demonstrated by higher ISS and APACHE II scores, albeit a non-significant difference was observed between groups. Even though patients were randomised according to a computer generated schedule, the small sample size did not allow for groups to be balanced.
5.4.2 Determining whether the trauma population represent a feasible patient population to study
This study identified that the trauma population is a more complicated group to study than originally anticipated. Trauma patients’ length of stay in ICU may not be long enough in the major trauma centre, therefore as a source of level 3 patients, trauma patients may not be sufficient. Annual data show patients had a mean length of stay of 6.6 days or median stay of 3.9 days (Medtrack database, Kings College Hospital). Patients are often repatriated to their local or referring hospital making the actual time for research studies shorter than anticipated.
Intensivists at Kings College Hospital were surprised at the short length of ICU stay for these patients, during the planning phases of this project ICU length of stay was thought to be reasonable enough to follow patients up for 14 days.
201 This illustrates that future project design should be supported by ICU admission and demographic data.
Patient recruitment to critical care research is clearly difficult, even in the context of a large potential pool of patients to recruit from. Additionally, due to the young age of many of the trauma admissions and the difficulty relatives experienced with the circumstances of the trauma, the next of kin were often too distressed to provide assent to the study. Furthermore, a lot of activity revolves around trauma patients, they have frequent scans, procedures and multiple operations in comparison to other ICU patient groups. This may limit the available time to administer interventions and to study outcome measures.
However, one major advantage of studying the trauma population is that they are generally younger and present with less comorbidity, thus making the impact of the critical care admission clearer to study. Furthermore, the day of trauma is usually known, providing a clearer model to study.
This type of dietary intervention may be feasible in the trauma population, however future studies should potentially focus on following the patient across the trauma network, from the Major Trauma Centre in the acute stages through to hospital rehabilitation in the network. Results from the nutritional tipping point study point towards the very acute stages of critical illness not being the best stage to target nutritional strategies, therefore rehabilitation strategies are likely to benefit patients more as they are followed through the trauma network.
5.4.3 Determining whether the intervention is feasible to study within trauma critical care
The L-EAA supplement was practical to administer to mechanically ventilated trauma patients via their enteral feeding tubes. The reasons for omissions of doses were for the most part unavoidable. The proportion of patients receiving the supplement may have been modest in this cohort, however lessons were learnt about the ongoing education required for dietary interventional studies.
202 The intake of any dietary intervention is likely to increase once a project and the intervention is fully embedded on the unit. In a busy trauma ICU feasibility of providing this supplement to severely ill trauma patients has been demonstrated, however it is dependent on intensive monitoring and having a dedicated research assistant to oversee the delivery of the supplement to the patient. It is questionable whether this intervention could be routinely provided with sufficient consistency, should it be shown to be effective.
5.4.4 Feasibility of the outcome measures and endpoints used in this study
Inflammatory markers and cytokine data were feasible to collect on most occasions. Urinary studies were not performed on three occasions due to patients transferring out of ICU and one patient requiring renal replacement therapy. Although urine collections were feasible on most occasions in this study cohort, missing collections could be an issue in larger studies and limitations of this outcome measures should be considered, see chapter 4, section 4.4.6, p174. No samples were misplaced during this study.
Serial ultrasound data in this study was limited due to the short duration of stay in particularly the control patient group, serial measurements were only performed in four patients. The use of muscle ultrasound is discussed in more detail in chapter 4, section 4.4.3, p167, however would only be useful in this type of study if patients could be followed serially for longer.
The currently available tools to detect functional change are not suitable for a trauma ICU population. The Katz and Barthel indices demonstrated substantial floor effects. Strength assessment via the MRC sum score was not feasible in a predominantly head injured trauma population. This type of testing, where patient cooperation is required, is unlikely to be of use in a trauma population suffering from traumatic brain injury.
203 I was unable to perform manual strength testing in this cohort. Patients were assessed once sedation was stopped and prior to discharge from the ICU and none of the patients were able to answer appropriately to the initial screening questions required in order to perform manual strength testing. This was mainly due to slow neurological recovery post traumatic brain injury.
De Jonghe et al found that 111 (54%) ICU patients studied had to be excluded from muscle strength assessment, 85 patients died before awakening, in two patients strength testing was omitted and 24 patients were discharged before they were awake enough to respond appropriately (De 2002). In a later cohort De Jonghe et al were able to perform strength testing in 99% of the patients included in their study (De 2007). The majority of these patients were medical ICU patients admitted with pneumonia or exacerbation of COPD.
In a general medical ICU cohort Ali et al found that 38 (22%) patients were unable to perform the MRC Sum Score assessment due to persistent coma or inattentiveness (Ali et al. 2008b). In a mixed medical and surgical ICU cohort Hough et al found that 101 (75%) patients had to be excluded from manual strength testing, mainly due to persistent coma, delirium or not being alert enough (Hough 2011). Hermans et al found that out of a provisional cohort of 735 ICU patients considered for neuromuscular analysis 257 (35%) were never awake enough to be assessed (Hermans 2012). This ICU cohort consisted mainly of cardiac surgery patients, approximately 60%.
The Katz index total score remained zero throughout when patients were assessed. The Barthel index score changed from zero on one occasion only as a patient sat out in a chair with major assistance. This patient remained cognitively impaired and the MRC Sum score assessment was not performed either in this case. Neither of these two indices appear to be sensitive enough to detect minor potential improvements in function, such as those we might see within a trauma critical care context.
A variety of functional assessment tools for specific use in the critically ill have been developed in recent years, which may be of use in future research, however reliability and validity will need to be established. The functional tools
204 that could potentially be used in an ICU population include the functional independent measure (FIM) (Dodds et al. 1993), the functional status score in ICU (FSS-ICU) (Zanni 2010), the physical functional independence test (PFIT) (Skinner et al. 2009) and the Chelsea functional test (CPax) (Corner 2013).
The FIM is used for general disability assessment with grading performed on 18 motor and cognitive tasks, scores range from 18-126. This tool has mostly been used in stroke patients, higher scores indicate higher levels of function (Dodds 1993). This tool has not yet been validated in the ICU population (Corner 2013).
The FSS-ICU utilises aspects of the FIM assessment tool, however only two of the functional tasks are appropriate in the ICU context. Three additional tasks relevant to the ICU environment were therefore included, the five sections in total are graded on a 7-point scoring system. As with the FIM, higher scores indicate higher levels of function. Categories include three areas where patients have not stood up yet: rolling, supine-to-sit transfer and unsupported sitting along with two further categories: sit-to-stand transfer and ambulation (Zanni 2010).
The PFIT test was designed for ICU patients in a centre where patients were weaning from tracheostomies, therefore perhaps not being entirely appropriate for the acutely ill ICU patient. Tests relate to cardiovascular fitness as opposed to actual assessment of physical function (Skinner 2009).
The CPax test was developed in London and it scores 10 components of physical function that would be most commonly assessed in critically ill patients. These include respiratory function (relating to ventilator support or spontaneous breathing), cough, moving within the bed, supine to sitting movement on the edge of the bed, dynamic sitting, standing balance, sit to stand movement, transfer from bed to chair, stepping and grip strength. Each component is graded from 0-5 giving a total score of 50. This tool was tested in 33 patients, inter-rater reliability for this tool was strong with κ= 0.988 (95%CI 0.791 to 1.000) (Corner 2013). This novel tool seems comprehensive in assessing the small incremental changes required in ICU patients. Further reliability and validity testing will be required in the critically ill.
205 Protein turnover studies, although robust and dynamic in assessing whole body protein breakdown and synthesis, are very time consuming and intricate to perform in mechanically ventilated patients. This type of outcome measure would only be feasible in future studies performed in subsets of patients or in sub-studies in units where research staff are plentiful, due to the labour intensity required to conduct these studies in ventilated patients.
Trauma patients in this current study seemed to have higher levels of leucine rate of appearance (Leu Ra), an index of whole body protein breakdown, than the Mansoor et al cohort displayed. Similarly to my trauma cohort, the Mansoor et al cohort consisted of a majority of traumatic brain injured patients. It is difficult to draw a direct comparison between these two cohorts as the acute physiology scores and injury scores were not equivalent. Additionally, a limitation to the injury severity score is that the severity of a head injury is not reflected by score, scoring is performed by the anatomical region injured however severity cannot be scored (Baker et al. 1974). The trauma cohorts exhibited the highest rates of whole body protein breakdown, with perhaps surprisingly high rates of whole body protein synthesis in comparison to a healthy population. Breakdown appeared highest in my cohort with protein synthesis highest in the Mansoor et al cohort. Ideally a healthy cohort should have been recruited to enable a direct comparison between my trauma cohort and a healthy population, which is a limitation to this current study, however this was unfortunately not feasible at the time of conducting this research. These data should however be interpreted with caution as I was unable to perform stable isotopes in a healthy cohort due to time constraints, therefore although it appears as though the patients in my trauma cohort demonstrated the most negative leucine balance this cannot be concluded without confirmation of leucine turnover in a healthy cohort.
206 One of the interesting findings from the protein turnover results is that whole body protein synthesis was higher in both trauma cohorts than the healthy subjects. Mansoor et al previously demonstrated that whole body protein synthesis was increased 48h after traumatic brain injury. This was thought to be due to the synthesis of acute phase proteins (Mansoor 1997). Birkhahn et al studied four trauma patients and five healthy subjects and concluded that whole body protein breakdown increased by 79% in the trauma subjects in comparison to healthy controls, protein synthesis was also increased above normal subjects at 37% (Birkhahn et al. 1981). In a group of patients studied with multi-organ failure whole body protein breakdown and synthesis increased concurrently, this was twice as high as rates measured in healthy control subjects (Arnold 1993).
Enhanced whole body protein synthesis may not be evident in all ICU patients, Whyte et al’s medical ICU cohort clearly exhibited lower rates than healthy control subjects (Whyte 2010). Klaude et al describe in an ICU cohort with sepsis and septic shock that patients demonstrated normal protein synthesis rates and increased protein breakdown of up to 160% in comparison to healthy control subjects (Klaude 2012).
5.4.5 Developing a strategy that will minimise muscle mass decline in critical illness
Due to the small sample size recruited the effect of this particular supplement on minimising muscle mass decline could not be assessed directly.
5.5 Conclusion
The feasibility of administering an amino acid supplement to trauma patients on ICU has been demonstrated, however the trauma population may be more complicated to study than anticipated. Future researchers should carefully consider whether trauma patients represent the best cohort to study within
207 critical care. Outcome measures used in this research, such as the stable isotope studies, were resource intensive and should only be considered in sub-studies in institutions with sufficient research staff. The trauma population in this study seemed to exhibit higher rates of whole body protein breakdown than other ICU patients. Whole body protein synthesis appeared higher than healthy control subjects and medical ICU patients. Protein turnover results were however limited by not studying a healthy cohort at the same time as the trauma patients and results should therefore be interpreted with caution.
208 Chapter 6. Conclusion and future work
Muscle wasting and weakness can lead to increased morbidity and ICU length of stay. Recovery for survivors of critical illness should be measured in months to years rather than days to weeks. Muscle breakdown during critical illness is a progressive and multi-factorial process, and is in part an adaptive response. Amino acid substrates are used for synthesis of acute phase proteins and used in immune response, for tissue regeneration and wound healing during trauma and critical illness. Researchers studying interventions aimed at ameliorating muscle loss will have to consider that the optimum timing of interventions should be identified for strategies to be effective.
The aim of this work was to understand the relationship between inflammatory and nutritional markers of critically ill patients such that a notional nutritional tipping point could be identified and a strategy to minimise muscle mass decline could be developed to facilitate recovery. Timing interventions in the knowledge of a nutritional tipping point would assist in optimising patient recovery.
An observational study was performed to identify a notional nutritional tipping point, where anabolism starts to exceed catabolism. The inter-relationships between inflammatory and nutritional markers during critical illness were additionally assessed in this study. Current data were mathematically explored to identify plausible surrogate endpoints for use in future nutritional clinical trials.
A feasibility study was conducted to identify whether the trauma population presented a viable group to study within critical care, given that these patients are usually younger and healthier than other patient groups and were thought to have a reasonable length of stay on ICU. The second aim was to determine how feasible the use of a leucine-enriched essential amino acid supplement might be as a future nutritional strategy aimed at minimising muscle loss. A variety of outcome measures relating to muscle depth, muscle breakdown, protein turnover and nutritional and inflammatory markers were explored to
209 determine how practical and scientifically sound these outcome measures would be for inclusion in a future larger nutritional clinical trial.
The observational study showed that ICU patients lost a significant amount of muscle depth during their ICU stay. This study was conducted in 78 critically ill patients at three hospital sites in London. A notional nutritional tipping point could not be identified during 20 days on ICU with markers pointing towards a continued inflammatory response, negative nitrogen balance and progressive muscle depth loss observed. This study identified that nutritional strategies to optimise recovery might be best targeted outside of an ICU environment, once patients are in a recovery phase. This finding does not imply that patients should not receive their usual nutrition support during ICU stay, it only suggests that ‘aggressive’ or additional nutritional strategies may not be effective whilst the process of catabolism remains dominant.
Multilevel modelling of variables revealed that with each additional day of measurement C-reactive protein, muscle depth and interleukin-10 levels decreased significantly. Older patients tended to have lower muscle depth and 3-methylhistidine levels. 3-Methylhistidine reflects myofibrillar breakdown from all muscle, including skeletal, smooth and cardiac muscle. The lower 3- methylhistidine levels may be due to lower protein breakdown in thinner muscle of older patients and therefore higher 3-methylhistidine levels in young patients may reflect higher rates of overall protein breakdown. This again may be due to younger patients in this cohort predominantly being trauma patients, whom have previously been shown to demonstrate accelerated rates of protein breakdown. Older patients were also found to have higher odds of having detectable levels of interleukin-10, an anti-inflammatory cytokine, than younger patients. The production of interleukin-10 has previously been found to be higher from leukocytes in the elderly than from those in the young.
The biomarkers used to estimate sample size were studied at a point in the patient’s recovery when the data suggest that patients remained catabolic, therefore not at an appropriate time for nutritional intervention. Although the sample size estimates provide some guidance as to the orders of magnitude needed for a late intervention study, when biomarkers are used as endpoints,
210 however estimates are not satisfactory and should be viewed with caution. Biomarker data collected in this research: CRP, albumin, urinary urea and 3- methylhistidine may additionally be influenced by confounding factors, although these data could be collected in future nutrition studies in critically ill patients they should not be used as primary endpoints.
The second study identified that trauma critically ill patients presented a much more complicated population to study than anticipated. Length of stay was shorter than expected due to early repatriation to referring hospitals. Trauma patients are often occupied with procedures and operations and future researchers should therefore carefully consider these issues when planning interventions and data collection time points. Administering an essential amino acid supplement to critically ill trauma patients is feasible, however it is questionable whether this intervention could be routinely provided with sufficient consistency, should it be shown to be effective. Many of the outcome measures used in the feasibility study were too resource intensive to be used in large future nutritional studies, in particular performing stable isotope studies in mechanically ventilated patients. Stable isotope studies could only reasonably be incorporated in sub-studies in units where research staff are plentiful. Assessment of functional status via the Katz and Barthel indices did not appear to be sensitive enough to depict potential minor improvements in function in the critically ill and manual strength testing is not recommended in cohorts with predominant traumatic brain injury either. This study also assessed protein turnover in a small trauma cohort. Trauma patients remained in a negative whole body protein balance and appeared to exhibit higher rates of whole body protein breakdown than medical ICU or surgical ICU patients do in comparison to the literature. Equally, whole body protein synthesis was higher than that observed in medical ICU patients and healthy subject data from the literature, these results should however be interpreted with caution as a healthy control cohort were not studied at the same time as the trauma cohort. The efficacy of a leucine- enriched essential amino acid supplement could not be assessed in this study due to small sample size.
211 Future research in this field could focus on either aiming to reduce catabolism during an ICU admission or by promoting anabolism after a period of critical illness. Potential strategies to minimise catabolism include drug interventions which have shown promise in reducing catabolism and promoting lean body mass gain. Although these drug interventions, Propranolol and Oxandrolone, have been studied mostly in patients suffering from burn injury, these interventions could be tested in other ICU cohorts to determine if the process of catabolism could be turned down earlier during an ICU stay. Other strategies that might reduce catabolism during critical illness include incorporation of electrical muscle stimulus and early exercise. Pilot studies have shown initial promise in electrical muscle stimulus for instance, however large prospective randomised controlled trials would be required to ascertain whether these interventions could feasibly reduce muscle wasting and improve physical function in ICU patients.
Outcome measures should be carefully selected to reflect the intervention studied. If urinary studies are conducted urinary nitrogen should be directly measured. If lean mass is assessed this should ideally be done via CT, such as has been demonstrated in trauma patients undergoing abdominal CT to ascertain lean body mass (Casaer et al. 2013). Muscle depth or muscle volume assessment via ultrasonography is practical and feasible when no other techniques are available within critical care however the limitations of these techniques should be clearly understood. Exploratory studies into causes of muscle wasting should be investigated further, in particular assessing the relationship between muscle wasting and the dose of steroid administration. Muscle biopsies could additionally be performed to determine if muscle protein breakdown and synthesis pathways are altered due to the chosen intervention.
In order to promote anabolism future nutritional research should focus on targeting additional nutritional interventions outside of the ICU environment to facilitate recovery. Leucine-enriched essential amino acid supplementation could be studied in addition to adequate intake of energy and protein.
212 Exercise or electrical muscle stimulus as well as the use of Oxandrolone and/or Propranolol may promote anabolism and warrants further investigation in other ICU cohorts. Appropriate functional and strength assessments should be identified in this population for use in future studies. The efficacy of a leucine-enriched essential amino acid supplement could additionally be tested in survivors of critical illness along with resistance exercise to assess whether functional recovery can be improved with this intervention. A specialised nutrition care pathway for ICU rehabilitation could be formulated in future, incorporating nutritional interventions shown to be effective in addition to appropriate measures of functional recovery.
213 References
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237 Appendices
238 Appendix I: Data Collection Sheet
Data Collection Sheet Day 1
Study: ______Date: ______
Researcher: ______Day of study: ______
Unit: CXH/HH/SMH (circle) Weight:…………..kg BMI:
Patient: PMH :
Speciality: (circle) Diagnosis: Medical Surgical APACHE II: Neurosurgery Organ Failure : ≤ 2 organs or ≥ 3 (circle) Mixed trauma Medications: Insulin: Steroids Sedation: Units per 24h: Drug: Fentanyl Midazolam Dose: Propofol NMBA: Cardiac: Albumin Thiopentone: Noradrenaline: OTHER: Adrenaline: GCS: Vasopressin: Dobutamine: Activity: Ventilation: Physiotherapy: Bedbound immobile ETT Yes Bedbound moving Setting: Duration: Sitting up in bed No Sitting out Comment: CVVHDF removal volume: Duration:
Nutrition: NG/PN/Oral Kcal per 24h: Protein per 24h
Vomiting: GRV per 24h: (ml) Diarrhoea (>3 Yes/No episodes/24h) Y/N Volume: Bristol:
239 Inflammatory Markers: WCC: CRP: (mg/dl) Albumin: (mg/dl) TNF-alpha:
IL-1beta: IL-6: IL-10:
REE: fiO2: VCO2: Kcal/24h: flow rate: VO2: Vent settings:
Nitrogen Balance:
Urinary urea per 24h: U - Creat:
Urinary urea: S - Urea:
3MH: S - Creat:
Stable Isotope: CO2:
KIC:
Protein Turnover:
Muscle US: Fluid Balance (last 24h):
1 2 3 Average
Bicep
Forearm
Thigh_half
Thigh_12cm
Muscle US: Total (cm): % loss:
EAA supplement Volume received: Comments: Volume given per 24h
Katz Index Score 0 – 6
(0 = Highly Dependent 6 = Highly Independent) Barthel Index Score 0 – 100
(0 = Highly Dependent
100 = Highly Independent)
240 Appendix II: Barthel Index
241 Appendix III: Katz Index
242 Appendix IV: MRC Sum Score
Date: Patient:
Patient has to be 100% cooperative. Ask: 1. Do you know where you are? 2. Can you stick your tongue out? 3. Can you squeeze my fingers?
‘Yes’ to all before commencing.
ASSESSMENT: Awakening/discharge (circle)
Shoulder Abduction R …… L ……
Elbow Flexion R …... L …...
Wrist extension R …… L ……
Hip Flexion R …... L ……
Knee Extension R …… L ……
Ankle Dorsi flexion R …… L ……
0 = No visible contraction 1 = Visible muscle contraction, no limb movement 2 = Active movement, not against gravity 3 = Active movement, against gravity 4 = Active movement against gravity and resistance 5 = Active movement against full resistance
Maximum score = 60 Minimum score = 0, quadriplegia ICUAW: < 48
243 Appendix V: Muscle ultrasound data collection sheet Muscle Ultrasound Participant No: CXH / HH/ SMH / KCH
Date B 1 B 2 B 3 Mean F 1 F 2 F 3 Mean T1/2_ 1 T1/2_ 2 T1/2_3 Mean T_12cm T_12cm T_12cm Mean Total
Document on general data collection sheet:
Time measurement taken, 24h Fluid balance, position of leg (90° angle on bed)
244 Appendix VI: IL-10 human duoset protocol
1. Capture antibody was diluted to the working concentration in PBS without the carrier protein in a clean reservoir. A 96-well microplate was immediately coated with 100 µL per well of the diluted capture antibody using a multi-channelled pipette. The microplate was sealed with paraffin and incubated overnight at room temperature.
2. Following the incubation period, the capture antibody was aspirated from the microplate and each well washed with 400ul of Tween® wash buffer. This process was repeated for a total of three times, ensuring complete aspiration of Tween® wash buffer between washes. After the last wash the remaining wash buffer was removed by aspirating and inverting the plate and blotting this against clean paper towels until the microplate was dry.
3. After the wash, 300 µL of reagent diluent was added to each well using a multi-channelled pipette. The microplate was sealed with paraffin and incubated at room temperature on the rocker for a minimum of 1 hour.
4. The process of aspirating and washing with Tween® wash buffer was repeated again three times. The microplate was inverted after the third wash and blot dried against clean paper towels.
5. Patient samples were removed from the -80C freezer and allowed to thaw at room temperature for a minimum of 1 hour. Serial dilutions of the standards were prepared immediately prior to washing of the plates. 100 µL of thawed patient samples or the serial dilution of the standards was added in duplicate into the microplate, using a clean pipette tip each time. The microplate was covered with paraffin and incubated on the rocker at room temperature for 2 hours.
6. The process of aspirating and washing with Tween® wash buffer was repeated again three times. The microplate was inverted after the third wash and blot dried against clean paper towels.
7. After washing, 100 µL of the prepared detection antibody was added into each well. The microplate was covered with paraffin and incubated on the rocker at room temperature for 2 hours.
245 8. The process of aspirating and washing with Tween® wash buffer was repeated again three times. The microplate was inverted after the third wash and blot dried against clean paper towels.
9. After washing, 100 µL of the working dilution of streptavidin-HRP was added to each well. The microplate was covered with paraffin and incubated for 20 minutes at room temperature away from direct light in a cupboard.
10. The process of aspirating and washing with Tween® wash buffer was repeated again three times. The microplate was inverted after the third wash and blot dried against clean paper towels.
11. After washing, 6ml of colour reagent A (H2O2) was added to 6ml of colour reagent B (tetramethylbenzidine) in a reservoir and mixed well. 100 µL of the substrate solution was added immediately to each well using the multi-channelled pipette. The microplate was incubated for 20 minutes at room temperature away from direct light in a cupboard.
12. 50 µL of stop solution (2 N H2SO4) was added to each well of the microplate, using the multi- channel pipette. The microplate was gently tapped to mix the solutions together.
13. The optical density of each well was immediately determined, using a microplate reader set to 450 nm.
A similar protocol was followed per manufacturer’s instructions for IL-6 ELISA.
246 Appendix VII: The distribution of patients’ BMI and length of stay on ICU
Figure a: Body mass index (kg/m²) distribution for patients.
Figure b: Distribution of patients’ length of stay on ICU (days).
247 Appendix VIII: Spread of patient data illustrated by trajectory plots
Figure 1: Trajectories of C-reactive protein (mg/L) for each patient during ICU stay
Figure 2: Trajectories of serum albumin (g/L) for each patient during ICU stay
248
Figure 3: Trajectories of urinary urea (mmol/24h) for each patient during ICU stay
Figure 4: Trajectories of 3-methylhistidine (µmol/24h) for each patient during ICU stay
249
Figure 5: Trajectories of nitrogen balance (g/day), PENG equation, for each patient during ICU stay
Figure 6: Trajectories for nitrogen balance (g/d), Deacon equation, per stay on ICU
250
Figure 7: Trajectories of total muscle depth (cm) for each patient during ICU stay
251 Appendix IX: Multilevel modelling Example results from multilevel modelling to assess the characteristics related with the levels of C-reactive protein (square root transformation) (n=78). Results for the fixed effects are presented as b-coefficients (95% Confidence Intervals). M1.0 M1.1 M1.2 M1.3 M1.4 M1.5
coefficient (95% CI) coefficient (95% CI) coefficient (95% CI) coefficient (95% CI) coefficient (95% CI) coefficient (95% CI)
Fixed effects
Intercept 10.95 (9.96, 11.93) 11.06 (10.06, 12.06) 11.80 (8.29, 15.31) 11.86 (8.41, 15.32) 13.12 (8.20, 18.04) 12.94 (8.02, 17.86)
Days -0.24 (-0.36, -0.13) -0.29 (-0.43, -0.15) -0.25 (-0.36, -0.13) -0.24 (-0.35, -0.13) -0.24 (-0.35, -0.13) -0.24 (-0.36, -0.13)
Age, yrs -0.01 (-0.07, 0.04) -0.01 (-0.06, 0.05) -0.01 (-0.06, 0.05) -0.01 (-0.06, 0.05)
Female gender -1.42 (-3.30, 0.46) -1.43 (-3.31, 0.44) -1.38 (-3.25, 0.49)
Body mass index, kg/m2 -0.05 (-0.18, 0.09) -0.05 (-0.19, 0.08)
Days in ICU 0.02 (-0.03, 0.06)
Random effects
variance (sd) variance (sd) variance (sd) variance (sd) variance (sd) variance (sd)
Subject 11.85 (3.44) 12.91 (3.59) 11.77 (3.43) 11.31 (3.36) 11.20 (3.35) 11.09 (3.33)
Days 0.08 (0.28)
Residual 10.22 (3.20) 8.86 (2.98) 10.23 (3.20) 10.24 (3.20) 10.24 (3.20) 10.25 (3.20)
Variance partition
Subject 0.54 0.59 0.54 0.52 0.52 0.52
Days 0.004
252
Model fit statistics
BIC 1381.00 1387.00 1385.80 1389.10 1394.10 1399.00 logLikelihood -679.30 -676.90 -679.20 -678.12 -677.87 -677.58
Deviance from M1.0 4.77 df=2 0.25 df=1 2.40 df=2 2.90 df=3 3.47 df=4
Deviance from M1.1
Deviance from M1.2 0.20 df=1 2.66 df=2 3.24 df=3
Deviance from M1.3 0.49 df=1 1.08 df=2
Deviance from M1.4 0.58 df=1
253 Appendix X: Predicted values from multilevel modelling Table 1: Predicted values for C-reactive protein levels (mg/L) along with their corresponding 95% Confidence intervals, according to the day the measurement was performed. Day of Predicted 95% Confidence measurement value Interval
1 115 (95, 136)
2 109 (91, 129)
3 104 (87, 123)
4 99 (83, 118)
5 95 (78, 113)
6 90 (74, 108)
7 85 (69, 104)
8 81 (64, 100)
9 77 (59, 96)
10 72 (55, 92)
11 68 (50, 89)
12 64 (46, 86)
13 60 (41, 83)
14 57 (37, 80)
15 53 (33, 78)
16 50 (29, 75)
20 37 (16, 65)
Table 2: Predicted values for Albumin levels (g/L) along with their corresponding 95% Confidence intervals, according to the day the measurement was performed. Day of Predicted 95% Confidence measurement value Interval
1 20.6 (19.2, 22.1)
2 20.7 (19.4, 22.0)
3 20.7 (19.5, 22.0)
4 20.8 (19.6, 22.0)
5 20.8 (19.6, 22.0)
254
6 20.8 (19.7, 22.0)
7 20.9 (19.7, 22.1)
8 20.9 (19.7, 22.2)
9 20.9 (19.6, 22.3)
10 21.0 (19.6, 22.4)
11 21.0 (19.5, 22.6)
12 21.1 (19.5, 22.7)
13 21.1 (19.4, 22.9)
14 21.1 (19.3, 23.1)
15 21.2 (19.2, 23.3)
16 21.2 (19.1, 23.4)
20 21.4 (18.7, 24.3)
Table 3: Predicted values for Urinary Urea levels (mmol/24h) along with their corresponding 95% Confidence intervals, according to the day the measurement was performed. Day of Predicted 95% Confidence measurement value Interval
1 262 (205, 326)
2 266 (210, 328)
3 269 (214, 330)
4 272 (216, 335)
5 276 (217, 341)
6 279 (217, 349)
7 282 (216, 358)
8 286 (214, 368)
9 289 (211, 379)
10 292 (208, 391)
11 296 (204, 404)
12 299 (201, 417)
13 303 (197, 431)
14 306 (193, 446)
15 310 (189, 460)
255 16 313 (184, 476)
20 327 (167, 541)
Table 4: Predicted values for 3-methylhistidine (umol/24h) along with their corresponding 95% Confidence intervals, according to patients’ age (years) and the day the measurement was performed. Day of measurement Age Predicted value 95% Confidence Interval
1 24 511 (381, 660)
1 27 491 (372, 625)
1 28 484 (369, 614)
1 30 470 (363, 592)
1 30 470 (363, 592)
1 32 457 (357, 571)
1 34 444 (350, 549)
1 36 431 (344, 529)
1 37 425 (341, 519)
1 39 413 (334, 499)
1 40 406 (331, 490)
1 41 400 (328, 480)
1 43 388 (321, 462)
1 44 382 (317, 453)
1 44 382 (317, 453)
1 46 370 (310, 436)
1 47 364 (307, 427)
1 47 364 (307, 427)
1 49 353 (299, 411)
1 51 341 (291, 396)
1 52 336 (287, 388)
1 52 336 (287, 388)
1 53 330 (283, 381)
1 53 330 (283, 381)
1 54 325 (279, 374)
256 1 54 325 (279, 374)
1 55 319 (274, 368)
1 55 319 (274, 368)
1 55 319 (274, 368)
1 56 314 (270, 361)
1 56 314 (270, 361)
1 57 308 (265, 355)
1 57 308 (265, 355)
1 57 308 (265, 355)
1 58 303 (260, 349)
1 59 298 (255, 344)
1 59 298 (255, 344)
1 59 298 (255, 344)
1 61 287 (245, 333)
1 61 287 (245, 333)
1 61 287 (245, 333)
1 62 282 (240, 328)
1 62 282 (240, 328)
1 63 277 (234, 323)
1 63 277 (234, 323)
1 63 277 (234, 323)
1 64 272 (229, 319)
1 64 272 (229, 319)
1 66 262 (218, 310)
1 66 262 (218, 310)
1 67 257 (212, 306)
1 67 257 (212, 306)
1 67 257 (212, 306)
1 67 257 (212, 306)
1 70 242 (195, 295)
1 71 238 (190, 291)
1 72 233 (184, 288)
257 1 72 233 (184, 288)
1 72 233 (184, 288)
1 73 228 (179, 284)
1 73 228 (179, 284)
1 73 228 (179, 284)
1 73 228 (179, 284)
1 75 219 (168, 278)
1 75 219 (168, 278)
1 76 215 (162, 275)
1 76 215 (162, 275)
1 77 210 (157, 272)
1 78 206 (151, 269)
1 80 197 (141, 263)
1 80 197 (141, 263)
1 81 193 (136, 260)
1 82 189 (131, 257)
1 84 180 (121, 252)
1 85 176 (116, 249)
1 85 176 (116, 249)
1 89 160 (97, 239)
2 53 324 (279, 373)
2 58 297 (256, 341)
2 62 276 (236, 320)
2 66 256 (214, 302)
2 67 252 (209, 298)
2 71 233 (186, 284)
2 72 228 (181, 281)
2 73 223 (175, 277)
2 73 223 (175, 277)
2 82 184 (127, 251)
2 84 176 (118, 246)
2 89 156 (94, 233)
258 3 24 496 (370, 641)
3 27 476 (361, 607)
3 28 469 (358, 596)
3 30 456 (352, 574)
3 34 430 (340, 532)
3 36 418 (333, 511)
3 39 399 (324, 482)
3 40 393 (321, 473)
3 43 375 (311, 445)
3 44 369 (308, 436)
3 46 357 (301, 419)
3 47 352 (297, 411)
3 49 340 (290, 395)
3 52 324 (278, 372)
3 53 318 (274, 365)
3 54 313 (270, 358)
3 55 307 (266, 352)
3 55 307 (266, 352)
3 56 302 (261, 346)
3 57 297 (257, 340)
3 57 297 (257, 340)
3 59 286 (247, 328)
3 59 286 (247, 328)
3 61 276 (237, 318)
3 61 276 (237, 318)
3 63 266 (226, 309)
3 63 266 (226, 309)
3 63 266 (226, 309)
3 64 261 (221, 304)
3 64 261 (221, 304)
3 66 251 (210, 296)
3 67 246 (204, 292)
259 3 67 246 (204, 292)
3 67 246 (204, 292)
3 72 223 (177, 274)
3 72 223 (177, 274)
3 73 218 (171, 271)
3 73 218 (171, 271)
3 75 209 (160, 265)
3 76 205 (155, 262)
3 76 205 (155, 262)
3 77 201 (149, 259)
3 78 196 (144, 256)
3 80 188 (134, 251)
3 81 184 (129, 248)
3 85 167 (109, 238)
4 30 449 (345, 566)
4 32 436 (339, 545)
4 39 392 (318, 475)
4 44 363 (301, 429)
4 47 345 (291, 404)
4 51 323 (276, 373)
4 52 317 (272, 366)
4 54 307 (264, 353)
4 55 301 (260, 346)
4 55 301 (260, 346)
4 56 296 (255, 340)
4 59 280 (241, 323)
4 59 280 (241, 323)
4 61 270 (231, 312)
4 62 265 (226, 308)
4 72 218 (172, 269)
4 73 213 (166, 266)
4 73 213 (166, 266)
260 4 80 183 (130, 246)
4 84 167 (110, 235)
4 85 163 (106, 233)
5 30 442 (338, 559)
5 32 429 (332, 538)
5 53 306 (261, 355)
5 59 275 (235, 318)
5 72 213 (166, 265)
5 73 208 (161, 262)
5 77 191 (140, 250)
5 78 187 (135, 247)
5 89 144 (84, 219)
6 28 447 (337, 574)
6 59 269 (227, 315)
6 61 259 (217, 305)
6 61 259 (217, 305)
6 66 235 (192, 283)
7 24 466 (341, 611)
7 40 367 (292, 450)
7 46 332 (272, 399)
7 47 327 (268, 391)
7 51 305 (253, 362)
7 52 300 (249, 355)
7 53 294 (245, 348)
7 54 289 (241, 342)
7 54 289 (241, 342)
7 55 284 (237, 336)
7 57 274 (228, 324)
7 58 269 (223, 318)
7 59 264 (219, 313)
7 59 264 (219, 313)
7 61 254 (209, 303)
261 7 63 244 (200, 293)
7 63 244 (200, 293)
7 72 203 (154, 259)
7 73 199 (149, 256)
7 73 199 (149, 256)
7 73 199 (149, 256)
7 77 182 (129, 243)
7 80 170 (115, 235)
7 81 166 (111, 232)
7 85 150 (93, 222)
7 85 150 (93, 222)
8 30 421 (314, 542)
8 32 408 (308, 522)
8 44 337 (270, 413)
8 49 310 (252, 374)
8 52 294 (240, 353)
8 55 278 (228, 334)
8 57 268 (219, 322)
8 67 220 (172, 275)
8 78 173 (119, 238)
9 24 452 (324, 601)
9 44 331 (260, 410)
9 59 253 (201, 310)
9 59 253 (201, 310)
9 73 189 (136, 252)
10 32 395 (290, 515)
10 47 309 (240, 386)
10 52 282 (221, 351)
10 54 272 (213, 338)
10 57 257 (201, 321)
10 73 185 (129, 250)
10 85 138 (79, 214)
262 11 44 319 (241, 408)
11 51 282 (216, 357)
11 63 224 (166, 290)
11 63 224 (166, 290)
12 59 237 (174, 310)
13 85 127 (64, 210)
14 24 417 (277, 584)
14 28 392 (265, 544)
14 32 368 (252, 506)
14 40 323 (226, 437)
14 44 301 (212, 406)
14 52 260 (183, 351)
14 53 255 (179, 345)
14 54 250 (175, 339)
14 55 246 (172, 333)
14 57 236 (164, 321)
14 77 151 (87, 234)
14 80 140 (76, 224)
14 85 123 (59, 209)
15 52 255 (174, 351)
15 59 222 (147, 311)
15 72 166 (98, 252)
16 73 158 (88, 249)
20 40 287 (168, 438)
Table 5: Predicted values for total muscle depth (cm) along with their corresponding 95% Confidence intervals, according to patients’ age (years), sex (male/ female) and the day the measurement was performed. Day of measurement Age Sex Predicted value 95% Confidence Interval
1 67 Male 6.6 (5.7, 7.5)
1 64 Male 6.7 (5.8, 7.6)
263 1 30 Male 8.3 (6.8, 10.1)
1 72 Female 4.5 (3.5, 5.6)
1 67 Female 4.7 (3.7, 5.8)
1 67 Male 6.6 (5.7, 7.5)
1 73 Male 6.3 (5.3, 7.4)
1 59 Female 5.0 (4.0, 6.1)
1 56 Male 7.1 (6.2, 8.0)
1 63 Female 4.9 (3.9, 6.0)
1 54 Female 5.2 (4.1, 6.4)
1 51 Male 7.3 (6.4, 8.3)
1 84 Female 4.1 (3.0, 5.3)
1 43 Female 5.7 (4.4, 7.1)
1 39 Male 7.9 (6.7, 9.2)
1 37 Male 8.0 (6.7, 9.4)
1 58 Male 7.0 (6.1, 7.9)
1 46 Male 7.5 (6.5, 8.6)
1 67 Female 4.7 (3.7, 5.8)
1 66 Male 6.6 (5.7, 7.6)
1 62 Female 4.9 (3.9, 6.0)
1 70 Female 4.6 (3.6, 5.7)
1 59 Female 5.0 (4.0, 6.1)
1 73 Male 6.3 (5.3, 7.4)
1 76 Female 4.4 (3.3, 5.5)
1 44 Female 5.6 (4.4, 7.0)
1 57 Male 7.0 (6.2, 7.9)
1 73 Male 6.3 (5.3, 7.4)
1 52 Male 7.3 (6.4, 8.2)
1 80 Male 6.0 (4.8, 7.3)
1 89 Male 5.6 (4.3, 7.2)
1 55 Male 7.1 (6.2, 8.0)
1 82 Male 5.9 (4.7, 7.2)
1 61 Female 4.9 (3.9, 6.0)
264 1 76 Male 6.2 (5.1, 7.3)
1 61 Male 6.8 (6.0, 7.8)
1 53 Male 7.2 (6.3, 8.2)
1 57 Female 5.1 (4.1, 6.2)
1 49 Male 7.4 (6.4, 8.4)
1 62 Male 6.8 (5.9, 7.7)
1 75 Male 6.2 (5.2, 7.4)
1 55 Male 7.1 (6.2, 8.0)
1 66 Male 6.6 (5.7, 7.6)
1 28 Male 8.4 (6.8, 10.3)
1 63 Female 4.9 (3.9, 6.0)
1 81 Male 6.0 (4.8, 7.3)
1 63 Male 6.7 (5.9, 7.7)
1 44 Male 7.6 (6.6, 8.8)
1 56 Male 7.1 (6.2, 8.0)
1 57 Male 7.0 (6.2, 7.9)
1 55 Female 5.2 (4.1, 6.3)
1 41 Male 7.8 (6.6, 9.0)
1 71 Male 6.4 (5.4, 7.4)
1 52 Female 5.3 (4.2, 6.5)
1 54 Male 7.2 (6.3, 8.1)
1 85 Female 4.0 (2.9, 5.3)
1 85 Male 5.8 (4.5, 7.2)
1 64 Male 6.7 (5.8, 7.6)
1 75 Female 4.4 (3.4, 5.5)
1 78 Male 6.1 (5.0, 7.3)
1 61 Male 6.8 (6.0, 7.8)
1 80 Female 4.2 (3.2, 5.4)
1 27 Male 8.5 (6.8, 10.4)
1 53 Male 7.2 (6.3, 8.2)
1 24 Female 6.5 (4.6, 8.7)
1 40 Male 7.8 (6.6, 9.1)
265 1 73 Female 4.5 (3.5, 5.6)
1 72 Male 6.3 (5.4, 7.4)
1 36 Male 8.0 (6.7, 9.5)
1 77 Male 6.1 (5.0, 7.3)
1 59 Male 6.9 (6.1, 7.8)
1 47 Male 7.5 (6.5, 8.6)
1 34 Male 8.1 (6.7, 9.7)
1 32 Male 8.2 (6.8, 9.9)
1 30 Male 8.3 (6.8, 10.1)
1 47 Male 7.5 (6.5, 8.6)
1 72 Male 6.3 (5.4, 7.4)
2 73 Female 4.4 (3.4, 5.5)
2 84 Female 4.0 (2.9, 5.2)
2 58 Male 6.9 (6.0, 7.8)
2 67 Female 4.6 (3.6, 5.7)
2 66 Male 6.5 (5.6, 7.4)
2 62 Female 4.8 (3.8, 5.9)
2 73 Male 6.2 (5.2, 7.3)
2 89 Male 5.5 (4.2, 7.0)
2 82 Male 5.8 (4.6, 7.1)
2 53 Male 7.1 (6.2, 8.0)
2 71 Male 6.3 (5.3, 7.3)
2 72 Male 6.2 (5.3, 7.3)
3 67 Male 6.3 (5.5, 7.3)
3 64 Male 6.5 (5.6, 7.4)
3 72 Female 4.3 (3.4, 5.4)
3 67 Female 4.5 (3.6, 5.6)
3 67 Male 6.3 (5.5, 7.3)
3 56 Male 6.8 (6.0, 7.7)
3 73 Female 4.3 (3.3, 5.4)
3 63 Female 4.7 (3.7, 5.7)
3 54 Female 5.0 (4.0, 6.2)
266 3 43 Female 5.5 (4.2, 6.9)
3 39 Male 7.7 (6.5, 8.9)
3 46 Male 7.3 (6.3, 8.4)
3 59 Female 4.8 (3.8, 5.9)
3 76 Female 4.2 (3.2, 5.3)
3 44 Female 5.4 (4.2, 6.8)
3 57 Male 6.8 (6.0, 7.7)
3 73 Male 6.1 (5.1, 7.1)
3 52 Male 7.0 (6.2, 8.0)
3 55 Male 6.9 (6.1, 7.8)
3 61 Female 4.7 (3.8, 5.8)
3 76 Male 6.0 (4.9, 7.1)
3 57 Female 4.9 (3.9, 6.0)
3 49 Male 7.2 (6.3, 8.2)
3 66 Male 6.4 (5.5, 7.3)
3 28 Male 8.2 (6.6, 10.0)
3 63 Female 4.7 (3.7, 5.7)
3 81 Male 5.7 (4.6, 7.0)
3 63 Male 6.5 (5.7, 7.4)
3 55 Female 5.0 (4.0, 6.1)
3 85 Male 5.6 (4.3, 7.0)
3 64 Male 6.5 (5.6, 7.4)
3 75 Female 4.2 (3.2, 5.3)
3 78 Male 5.9 (4.8, 7.1)
3 61 Male 6.6 (5.8, 7.5)
3 80 Female 4.0 (3.0, 5.2)
3 27 Male 8.2 (6.6, 10.1)
3 53 Male 7.0 (6.1, 7.9)
3 24 Female 6.3 (4.4, 8.4)
3 40 Male 7.6 (6.4, 8.9)
3 72 Male 6.1 (5.2, 7.2)
3 36 Male 7.8 (6.5, 9.2)
267 3 77 Male 5.9 (4.9, 7.1)
3 59 Male 6.7 (5.9, 7.6)
3 47 Male 7.3 (6.3, 8.3)
3 34 Male 7.9 (6.5, 9.4)
3 30 Male 8.1 (6.6, 9.8)
4 30 Male 8.0 (6.5, 9.6)
4 73 Male 6.0 (5.0, 7.0)
4 59 Female 4.7 (3.8, 5.8)
4 51 Male 7.0 (6.1, 7.9)
4 84 Female 3.8 (2.8, 5.0)
4 39 Male 7.5 (6.4, 8.8)
4 62 Female 4.6 (3.7, 5.7)
4 59 Female 4.7 (3.8, 5.8)
4 73 Male 6.0 (5.0, 7.0)
4 80 Male 5.7 (4.6, 6.9)
4 55 Male 6.8 (6.0, 7.7)
4 61 Male 6.5 (5.7, 7.4)
4 55 Male 6.8 (6.0, 7.7)
4 44 Male 7.3 (6.3, 8.4)
4 56 Male 6.7 (5.9, 7.6)
4 52 Female 5.0 (4.0, 6.2)
4 54 Male 6.8 (6.0, 7.7)
4 85 Female 3.8 (2.7, 5.0)
4 32 Male 7.9 (6.5, 9.4)
4 47 Male 7.2 (6.2, 8.2)
4 72 Male 6.0 (5.1, 7.0)
5 59 Female 4.6 (3.7, 5.7)
5 89 Male 5.2 (3.9, 6.7)
5 53 Male 6.8 (5.9, 7.6)
5 78 Male 5.7 (4.6, 6.8)
5 73 Female 4.1 (3.2, 5.2)
5 72 Male 5.9 (5.0, 6.9)
268 5 77 Male 5.7 (4.7, 6.8)
5 32 Male 7.8 (6.4, 9.3)
5 30 Male 7.9 (6.4, 9.5)
6 59 Female 4.5 (3.6, 5.6)
6 61 Female 4.5 (3.6, 5.5)
6 61 Male 6.3 (5.5, 7.1)
6 66 Male 6.1 (5.3, 7.0)
6 28 Male 7.8 (6.3, 9.5)
7 72 Female 4.0 (3.1, 5.0)
7 73 Male 5.7 (4.7, 6.7)
7 59 Female 4.5 (3.5, 5.5)
7 54 Female 4.6 (3.7, 5.7)
7 51 Male 6.6 (5.8, 7.5)
7 58 Male 6.3 (5.6, 7.1)
7 46 Male 6.9 (5.9, 7.9)
7 73 Male 5.7 (4.7, 6.7)
7 80 Male 5.4 (4.3, 6.6)
7 61 Female 4.4 (3.5, 5.4)
7 53 Male 6.5 (5.7, 7.4)
7 57 Female 4.5 (3.6, 5.6)
7 63 Female 4.3 (3.4, 5.3)
7 81 Male 5.3 (4.3, 6.6)
7 63 Male 6.1 (5.3, 6.9)
7 55 Female 4.6 (3.6, 5.7)
7 52 Female 4.7 (3.7, 5.8)
7 54 Male 6.5 (5.7, 7.3)
7 85 Female 3.5 (2.5, 4.7)
7 85 Male 5.2 (4.0, 6.5)
7 24 Female 5.9 (4.1, 7.9)
7 40 Male 7.1 (6.0, 8.3)
7 73 Female 4.0 (3.0, 5.0)
7 77 Male 5.5 (4.5, 6.6)
269 7 59 Male 6.3 (5.5, 7.1)
7 47 Male 6.8 (5.9, 7.8)
8 67 Male 5.8 (5.0, 6.7)
8 44 Female 4.9 (3.8, 6.2)
8 57 Male 6.3 (5.5, 7.1)
8 52 Male 6.5 (5.7, 7.3)
8 55 Male 6.3 (5.6, 7.2)
8 49 Male 6.6 (5.8, 7.5)
8 78 Male 5.4 (4.4, 6.5)
8 32 Male 7.4 (6.1, 8.9)
8 30 Male 7.5 (6.1, 9.1)
9 73 Female 3.8 (2.9, 4.8)
9 59 Female 4.3 (3.4, 5.3)
9 44 Female 4.8 (3.7, 6.1)
9 24 Female 5.6 (3.9, 7.7)
9 59 Male 6.1 (5.3, 6.9)
10 54 Female 4.4 (3.4, 5.4)
10 52 Male 6.3 (5.5, 7.1)
10 57 Female 4.3 (3.4, 5.3)
10 85 Male 4.9 (3.8, 6.2)
10 73 Female 3.7 (2.8, 4.7)
10 47 Male 6.5 (5.6, 7.4)
10 32 Male 7.2 (5.8, 8.6)
11 51 Male 6.2 (5.4, 7.0)
11 63 Female 4.0 (3.1, 4.9)
11 63 Male 5.7 (4.9, 6.5)
11 44 Male 6.5 (5.6, 7.5)
12 59 Female 4.0 (3.2, 5.0)
13 85 Female 3.1 (2.1, 4.2)
14 80 Male 4.7 (3.7, 5.8)
14 53 Male 5.8 (5.1, 6.6)
14 57 Female 3.9 (3.1, 4.9)
270 14 28 Male 6.9 (5.5, 8.5)
14 44 Male 6.2 (5.3, 7.2)
14 55 Female 4.0 (3.1, 5.0)
14 52 Female 4.1 (3.2, 5.1)
14 54 Male 5.8 (5.0, 6.5)
14 85 Male 4.5 (3.4, 5.8)
14 24 Female 5.2 (3.5, 7.1)
14 40 Male 6.4 (5.3, 7.5)
14 77 Male 4.8 (3.9, 5.9)
14 32 Male 6.7 (5.4, 8.1)
15 72 Female 3.3 (2.5, 4.3)
15 59 Female 3.8 (2.9, 4.7)
15 52 Male 5.7 (5.0, 6.5)
16 73 Female 3.2 (2.4, 4.1)
20 40 Male 5.7 (4.8, 6.8)
Table 6: Predicted values for the odds of having Interleukin-10 levels > 0 pg/ml along with their corresponding 95% Confidence intervals, according to the day the measurement was performed. Day of Predicted 95% Confidence measurement value Interval
1 0.47 (0.14, 1.62)
2 0.45 (0.14, 1.48)
3 0.42 (0.13, 1.37)
4 0.40 (0.12, 1.31)
5 0.38 (0.11, 1.27)
6 0.36 (0.10, 1.25)
7 0.34 (0.09, 1.26)
8 0.32 (0.08, 1.29)
9 0.30 (0.07, 1.34)
10 0.29 (0.06, 1.41)
11 0.27 (0.05, 1.49)
12 0.26 (0.04, 1.58)
271 13 0.24 (0.03, 1.69)
14 0.23 (0.03, 1.82)
15 0.22 (0.02, 1.97)
16 0.21 (0.02, 2.13)
20 0.16 (0.01, 3.01)
272 Appendix XI: KIC Standard Curve KIC Standard Curve on Mass Spectrometry
273 Appendix XII: Example of Patient KIC Curve Patient Example KIC on Mass Spectrometry
274 13 Appendix XIII: Example curves for CO2 breath and α-KIC samples
13CO2 breath samples P403_D1
0.025 0.02
0.015
APE 0.01 0.005 0 0 30 60 90 120 150 180 Sample time (mins)
P_404P_404
0.280
0.270 0.260 0.250 0.240 0.230 0.220 0.210 peak area ratio 260.2/259.2peak 0.200 -30 0 30 60 90 120 150 180 sample time (mins)
275 Appendix XIV: Permissions to republish third party documents WOLTERS KLUWER HEALTH LICENSE TERMS AND CONDITIONS Oct 08, 2012
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License Number 3004180381337 License date Oct 08, 2012 Licensed content publisher Wolters Kluwer Health Licensed content publication Current Opinion in Clinical Nutrition and Metabolic Care Licensed content title Hypoxia and muscle maintenance regulation: implications for chronic respiratory disease. Licensed content author Theije, Chiel; Costes, Frederic; Langen, Ramon; Pison, Christophe; Gosker, Harry Licensed content date Jan 1, 2011 Volume Number 14 Issue Number 6 Type of Use Dissertation/Thesis Requestor type Individual Title of your thesis / dissertation An investigation into inflammatory and nutritional markers during critical illness to identify an indicator of anabolism and to explore a method of attenuating muscle mass loss. Expected completion date Aug 2013 Estimated size(pages) 350 Billing Type Invoice
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277 Appendix XV: Ethics Approval
278
279
280 Appendix XVI: Presentations Invited International Presentations
The Malaysian Dietetic Association Scientific Conference, Kuala Lumpur (July 2012).
Plenary Lecture: Optimal provision of enteral nutrition in the ICU. Meet the expert session: Current controversies in ICU nutrition research.
Invited National and Regional Presentations
Centre for Inherited Metabolic Disorders Research Group, Guys & St Thomas’ Hospital NHS Foundation Trust (February 2013): Inflammatory and nutritional changes during critical illness with a method of attenuating muscle loss.
The Intensive Care Society’s State of the Art Meeting, London (December 2012): Experience of being an NIHR Clinical Academic Fellow.
Kings College Hospital Critical Care Research Group (November 2012): Inflammatory and nutritional changes during critical illness.
Oxford Nutrition Group, University of Oxford (September 2012): Inflammatory and nutritional changes during critical illness with a method of attenuating muscle loss.
British Association for Parenteral and Enteral Nutrition Annual Conference, UK (2010). How to Succeed in Research: A Fellow’s Perspective.
British Dietetic Association Research Symposium (2010). How to Succeed in Research: A Fellow’s Perspective.
Abstract
Does Body Mass Index influence muscle wasting following critical illness?
E Segaran, L Wandrag, M Stotz, M Hickson. European Society of Intensive Care
Medicine (2012).
281