BUCCAL PULSE OXIMETRY: ACCURACY AND PRECISION
A
DISSERTATION
Presented to the Faculty of
The University of Texas Health Science Center at San Antonio
Graduate School of Biomedical Sciences
in Partial Fulfillment
of the Requirements
for the Degree of
DOCTOR OF PHILOSOPHY IN NURSING
By
Patricia N. Meza, RN, MS, CCNS, CEN, TCRN
San Antonio, Texas
May 2017
BUCCAL PULSE OXIMETRY: ACCURACY AND PRECISION
Patricia N. Meza
APPROVED:
______Andrea E. Berndt, Ph.D., Supervising Professor
______Carrie Jo Braden, Ph.D., R.N.
______Elizabeth J. Bridges, Ph.D., R.N., CCNS
______Joseph Schmelz, Ph.D., R.N.
______Steven G. Venticinque, MD
May 1, 2017______Date
APPROVED:
______David S. Weiss, Ph.D. Dean, Graduate School of Biomedical Sciences
DEDICATION
I dedicate my dissertation to my wonderful, supportive family and especially my beautiful daughter, Kelsey Bradshaw. Kelsey, you have and continue to be my light and sense of purpose. I'm so very grateful that God entrusted me with the honor and greatest privilege of being your mom. You've persevered through hardships caused by my mobile career, long hours and deployments. No words will capture how much I love you and admire the young lady you've become. Dad, I know you're smiling from heaven, proud at what your brave decision to immigrate to this wonderful country has afforded for your children and our next family generations. Mom, your work ethic is unlike anyone else's and it has been my driving force throughout my life. You and Dad never let your limited formal education hold you back. You are splendid examples of what hard work can accomplish. Gary, you endured time apart during my deployments and when my dissertation work pulled me away from everything else. I’m so thankful for your patience, encouragement and especially for your love.
I also owe gratitude to the United States Air Force. This organization first took me in as a teenager trying to find a life path. The Air Force provided fabulous mentors, colleagues, life-long friends, unique, professional opportunities and an exciting career. Serving our military beneficiaries was a privilege; caring for our wounded warriors was my greatest honor.
Finally, I am grateful to my God. Through Him all things are possible. Despite all the challenges along this long, hard road, He gives life meaning and helped me regain perspective when I needed it most.
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ACKNOWLEDGEMENTS
I’m extremely grateful for the journey and life lessons as I went through this process.
I would like to acknowledge the faculty and staff who supported and guided me throughout this process.
Dr. Berndt – I am very grateful for your leadership and ongoing encouragement throughout this process. You have been a continuous positive driving force through every step of the way towards accomplishing my PhD.
Dr. Schmelz – I appreciate your guidance and pathophysiology expertise, especially throughout the early phases of my study. Thank you for bringing on the challenging questions early. You made it possible for me to expand my lens throughout my study’s work.
Dr. Braden – thank you for sharing your unbelievable wisdom and for helping to set the stage for my study. You helped re-instill the "wonderment" nurses need so that we may fuel our inquisitiveness and maximize our learning.
Dr. Venticinque – you are an excellent role model for collaborative work and have always advocated multidisciplinary approaches to clinical care and inquiry. While working with you in the Air Force, your leadership and approach with staff and patients inspired me to remain open to new science and always seek the best care options for our patients.
Dr. Bridges – your incredible work in Air Force critical care nursing will continue to impact military healthcare and care of the wounded warrior for years to come. Thank you for supporting my work and for being an inspiring role model.
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Dr. Gill – there were several times throughout my program when you re-injected energy, fun and, most importantly, common sense. I will always be very grateful for your support, especially during my most stressful moments.
Ms. Hasewinkle -- thank you so much for all your kind attention and help to make sure my administrative items were taken care of. Being out of the state and sometimes out of the country made it a bit tougher. I know you gave me more than my fair share of attention.
I appreciated all your help more than you know.
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BUCCAL PULSE OXIMETRY: ACCURACY AND PRECISION
Publication No. ______
Patricia N. Meza, Ph.D.
The University of Texas Health Science Center at San Antonio
Graduate School of Biomedical Sciences
Supervising Professor: Andrea E. Berndt, Ph.D.
Clinicians assess multiple biomarkers and patient signs/symptoms in clinical settings such as intensive care units, operating rooms, emergency departments, and patient transport.
Pulse oximetry monitoring remains a standard. There are instances in which traditional pulse
vi oximetry monitoring sites, such as the fingers, toes or earlobes, fail or access is infeasible. In these instances, clinicians seek alternative monitoring sites, such as the buccal area. Unstable patients may also challenge ability to acquire a pulse oximetry signal, possibly preventing reliable pulse oximetry values. Compared to fingers or toes, the buccal area is not as susceptible to local vasoconstriction effects. Therefore, the buccal area may enable measurement of pulse oximetry even in low flow states. Only a few studies have investigated accuracy and/or precision of buccal pulse oximetry relative to arterial blood oxygenation.
The purpose of this study was to establish limits of agreement between buccal pulse oximetry and arterial blood oxygenation and to compare the limits of agreement for buccal pulse oximetry and finger pulse oximetry relative to arterial blood oxygenation. The present study also examined the degree to which fraction of inspired oxygen, partial pressure of oxygen dissolved in arterial blood, minute ventilation, heart rate, mean arterial pressure, pulse oximetry waveform amplitude, presence of vasoactive medication intravenous infusions, body temperature, and skin phototype (pigment) influenced buccal and finger pulse oximetry
- arterial blood oxygenation bias.
A prospective clinical investigation approach was used and 136 participants were initially recruited in a large Level 1 trauma center. The final sample size was 98. Study inclusion criteria were: age 18 or greater, presence of an endotracheal or tracheostomy tube, and presence of mechanical ventilation. Buccal and finger pulse oximetry measurements were compared to the gold standard SaO2. Data were analyzed using Bland-Altman analyses,
Pearson Correlation, and independent-samples t-test.
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Results: The present study revealed limits of agreement between buccal pulse oximetry and arterial blood oxygenation were -13.73 to 9.87. Limits of agreement between buccal pulse oximetry and arterial blood oxygenation were dispersed more widely when compared to finger pulse oximetry and arterial blood oxygenation (-4.11 to 4.3), especially at arterial blood oxygenation values less than 90 percent. The present study also revealed that decreased mean arterial pressure and presence of vasopressor infusions independently were associated with more negative finger pulse oximetry- arterial blood oxygen saturation bias.
Lastly, participants with brown skin phototype had more positive finger pulse oximetry- arterial blood oxygen saturation bias.
Implications: As buccal pulse oximetry may vary by greater than 4 percent of arterial blood oxygenation, the present study indicated that buccal pulse oximetry is an imprecise measure of arterial blood oxygenation in mechanically-ventilated adults. Clinicians should be aware that decreased mean arterial pressure or presence of vasopressor infusions may result in finger pulse oximetry values that underestimate arterial blood oxygen saturation. Finally, clinicians should be aware that brown skin phototype may be associated with finger pulse oximetry values that overestimate arterial blood oxygen saturation.
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TABLE OF CONTENTS Page
Title ...... i
Approval ...... ii
Dedication ...... iii
Acknowledgements ...... iv
Abstract ...... vi
Table of Contents ...... ix
List of Tables ...... xv
List of Figures ...... xvii
I. INTRODUCTION ...... 1
A. General Problem Area ...... 1
B. Purpose and Aims of the Study ...... 3
C. Need for More Study ...... 4
D. Significance of the Present Study ...... 6
E. Guiding Framework: Oxygen Transport and Oxygenation ...... 7
1. Oxygen Transport ...... 7
2. Oxygenation ...... 8
F. Pulse Oximetry Technology ...... 10
G. Device Accuracy and Precision ...... 11
II. REVIEW OF THE LITERATURE ...... 16
A. Previous Research Leading to Buccal Pulse Oximetry ...... 17
B. Oxygen Transport...... 21
ix
1. Hypoxemia, Hypoxia, and Pulse Oximetry ...... 22
C. Oxygenation ...... 23
1. Partial Pressure of Oxygen and Fraction of Inspired Oxygen ...... 23
2. Hemoglobin Affinity for Oxygen ...... 24
3. Dyshemoglobins ...... 25
4. Cardiac Output ...... 26
5. Regional Blood Flow: Body Temperature, Vasoconstriction,
Pulse Amplitude, and Blood Flow Velocity ...... 29
6. Saturation, Desaturation, and Resaturation ...... 34
D. Pulse Oximetry Technology ...... 37
1. Transmittance Pulse Oximetry ...... 39
2. Reflectance Pulse Oximetry ...... 39
3. Clinical Intermix of Transmittance and Reflectance Pulse
Oximetry Technologies ...... 40
E. Factors Affecting Accuracy and Precision of Arterial Blood Gas and
Pulse Oximetry Measurement ...... 41
1. Participant ...... 41
2. Environment ...... 45
3. User …...... 47
4. Machine/Device ...... 48
III. METHODS...... 53
A. Study Design ...... 53
B. Setting ...... 53
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C. Sample ...... 53
1. Inclusion/Exclusion Criteria ...... 54
2. Sample Size ...... 55
3. Recruitment ...... 55
D. Instruments ...... 55
1. Pulse Oximeter Sensors ...... 56
2. NellcorTM N-600x Pulse Oximeters ...... 59
3. Arterial Lines and Non-Invasive Blood Pressure Cuffs ...... 62
4. Consent ...... 63
5. Data Collection Worksheet ...... 64
6. Skin Phototype Palette ...... 65
7. Arterial Blood Gas Supplies and Samples ...... 65
8. Arterial Blood Gas Analyzers ...... 66
9. Body Temperature Measurement Sources ...... 66
E. Procedure ...... 67
1. Pulse Oximeter Set Up and Preparation...... 67
2. Pulse Oximetry Sensor Preparation and Placement ...... 67
3. Mean Arterial Pressure and Heart Rate ...... 69
4. Pulse Oximetry Data ...... 70
5. Arterial Blood Gas Sampling and Processing ...... 70
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6. Fraction of Inspired Oxygen, Minute Ventilation, Presence of
Vasopressor Infusions, Partial Pressure of Oxygen Dissolved
in Arterial Blood, and Arterial Blood Oxygen Saturation ...... 71
7. Post Procedure ...... 71
F. Analyses of Specific Aims ...... 71
1. Aims 1 and 2: Level of Agreement Between Buccal Pulse
Oximetry and Finger Pulse Oximetry with Arterial Blood
Oxygen Saturation ...... 71
2. Aim 3: Potential Influencing Variables ...... 72
IV. RESULTS ...... 74
A. Findings ...... 74
1. Sample Size ...... 74
2. Preliminary Analyses ...... 76
3. Oxygen Saturation Data Homogeneity ...... 76
4. Participant Demographic Information ...... 77
5. Medical Diagnoses ...... 77
6. Airway, Arterial Blood Gas Sampling, and Vital Signs Sources ...... 80
7. Frequencies for Saturation Biomarkers, FiO2, VE, and
Waveform Amplitude ...... 82
8. Data Screening: Oxygen Transport and Oxygenation
Variables ...... 87
9. Buccal and Finger Pulse Oximetry Saturation ...... 99
10. Aim 1: Buccal Pulse Oximetry Accuracy and Precision ...... 110
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11. Aim 2: Comparison of Buccal and Finger Pulse Oximetry
Accuracy and Precision ...... 110
12. Aim 3: Buccal and Finger Pulse Oximetry and Potential
Influencing Variables ...... 114
V. DISCUSSION, IMPLICATIONS, AND RECOMMENDATIONS ...... 126
A. Summary and Discussion of Findings ...... 126
1. Buccal Pulse Oximetry Accuracy and Precision ...... 127
2. Aim 3: Examine the Degree to which FiO2, PaO2, VE, HR,
MAP, Waveform Amplitude, Presence of Vasopressor
Infusions, Body Temperature, and Skin Phototype Influence
Buccal and Finger Pulse Oximetry ...... 127
3. Correlation Findings for Independent Variables ...... 128
B. Implications for Nursing Education, Practice, and Research ...... 130
1. Implications for Nursing Education ...... 130
2. Implications for Nursing Clinical Practice...... 130
3. Implications for Nursing Research ...... 131
C. Limitations of the Study ...... 134
1. Sample Homogeneity ...... 134
2. Blood Oxygen Saturation Homogeneity ...... 134
3. Fraction of Inspired Oxygen Homogeneity...... 135
4. Mean Arterial Pressure ...... 135
5. Body Temperature ...... 136
6. Pulse Oximetry Sensor ...... 136
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7. Pulse Amplitude Blip Bar ...... 137
D. Strengths of the Study ...... 137
1. Pulse Oximetry Sensor Technology ...... 137
2. Pulse Oximetry Data Collection Times ...... 137
3. Investigation of Multiple Potentially Influencing Variables ...... 137
E. Summary ………...... 137
APPENDICES
A. Abbreviations ...... 139
B. IRB Approval ...... 141
C. IRB Approval - Change to Number of Participants, Adds Study Site,
2 ICUs and Sponsor, Updates Equipment ...... 143
D. IRB Approval - Change to Inclusion/Exclusion Criteria ...... 144
E. IRB Approval - Change to Inclusion Criteria, Data Collection Tool,
and Adds Information for ABG Analyzer ...... 145
F. Recruitment Poster ...... 146
G. Informed Consent - English ...... 147
H. Informed Consent - Spanish ...... 155
I. Data Collection Worksheet ...... 165
REFERENCES...... 166
VITA ...... 187
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LIST OF TABLES Page
Table 1 Limited Buccal Pulse Oximetry Literature: Evolution and
Summary ...... 18
Table 2 Participants Recruited and Disenrolled ...... 75
Table 3 Frequencies and Percentages of Arterial Blood Saturation and Pulse
Oximetry Saturation ...... 78
Table 4 Participant Demographic Characteristics, Medical Diagnoses, Nursing
Unit, and Presence of Vasopressors ...... 79
Table 5 Airway Types, Arterial Blood Gas Sampling Methods, Sites and
Times, and Vital Signs Sources ...... 81
Table 6 Frequencies: Participant Oxygenation Biomarker Values/Ranges ...... 83
Table 7 Descriptives: Oxygen/Saturation Calculations and Biomarkers ...... 84
Table 8 Frequencies: Participant Vital Signs and Pulse Oximetry Waveform
Amplitude ...... 85
Table 9 Descriptives: Minute Ventilation, Heart Rate, Mean Arterial
Pressure, and Body Temperature ...... 86
Table 10 Bland-Altman Bias and Limits of Agreement: Buccal and Finger
Pulse Oximetry Saturation and Arterial Blood Oxygen Saturation ...... 112
Table 11 Pearson Correlation: Arterial Blood Oxygen Saturation, Buccal and
Finger SpO2 - SaO2 Bias, Fraction of Inspired Oxygen, Minute
Ventilation, Partial Pressure of Oxygen, Heart Rate, Mean Arterial
Pressure, and Body Temperature ...... 115
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Table 12 Pearson Correlation: Buccal and Finger Pulse Oximetry Saturation -
Arterial Blood Oxygen Saturation Bias with Hemoglobin and
Hematocrit ...... 116
Table 13 Spearman Correlation: Buccal and Finger Pulse Oximetry Saturation -
Arterial Blood Oxygen Saturation Bias, Pulse Oximetry Waveform
Amplitude, Heart Rate, Mean Arterial Pressure, and Body Temperature ...... 118
Table 14 Vasopressor Infusions ...... 119
Table 15 Independent-Samples t-Test: Buccal and Finger Pulse Oximetry-
Arterial Blood Oxygen Saturation Bias with and without Vasopressor
Infusions ...... 121
Table 16 Spearman Correlation: Buccal and Finger Pulse Oximetry-Arterial
Blood Oxygen Saturation Bias and Presence of Vasopressor
Infusions ...... 122
Table 17 Spearman Correlation: Buccal and Finger Pulse Oxygen Saturation -
Arterial Blood Oxygen Saturation Bias with Skin Phototype ...... 123
Table 18 Independent-Samples t-Test: Buccal and Finger Pulse Oximetry-
Arterial Blood Oxygen Saturation Bias Based on Skin Phototype ...... 125
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LIST OF FIGURES Page
Figure 1 Conditions and Predictors Affecting Pulse Oximetry ...... 9
Figure 2 Pulse Oximetry Transmittance and Reflectance Technologies ...... 12
Figure 3 Blood Flow Distance and Desaturation/Resaturation Times ...... 13
Figure 4 Skin Phototype Palette ...... 49
Figure 5 Pulse Oximeter Sensor with Delineated Adhesive Removal Areas ...... 57
Figure 6 Pulse Oximeter Sensor Modified for Buccal Use ...... 58
Figure 7 Modified Pulse Oximeter Sensor Placement in the Buccal Area ...... 60
Figure 8 Pulse Oximeter Blip Display Screen ...... 61
Figure 9 Buccal and Finger Pulse Oximeter Placement ...... 68
Figure 10 Histogram: Arterial Blood Oxygen Saturation ...... 88
Figure 11 Histogram: Buccal Pulse Oximetry ...... 90
Figure 12 Boxplot: Buccal Pulse Oximetry – Arterial Blood Oxygen Saturation
Bias ...... 91
Figure 13 Scatterplot: Buccal Pulse Oximetry with Arterial Blood Oxygen
Saturation ...... 92
Figure 14 Scatterplot: Buccal Pulse Oximetry - Arterial Blood Oxygen Saturation
Bias with Arterial Blood Oxygen Saturation ...... 93
Figure 15 Scatterplot: Buccal Pulse Oximetry - Arterial Blood Oxygen Saturation
Bias with Fraction of Inspired Oxygen ...... 94
Figure 16 Scatterplot: Buccal Pulse Oximetry - Arterial Blood Oxygen Saturation
Bias with Minute Ventilation ...... 95
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Figure 17 Scatterplot: Buccal Pulse Oximetry - Arterial Blood Oxygen Saturation
Bias with Partial Pressure of Oxygen Dissolved in Arterial Blood ...... 96
Figure 18 Scatterplot: Buccal Pulse Oximetry - Arterial Blood Oxygen Saturation
Bias with Heart Rate ...... 97
Figure 19 Scatterplot: Buccal Pulse Oximetry - Arterial Blood Oxygen Saturation
Bias with Mean Arterial Pressure ...... 98
Figure 20 Scatterplot: Buccal Pulse Oximetry - Arterial Blood Oxygen Saturation
Bias with Body Temperature ...... 99
Figure 21 Histogram: Finger Pulse Oximetry ...... 101
Figure 22 Boxplot: Finger Pulse Oximetry - Arterial Blood Oxygen Saturation
Bias ...... 102
Figure 23 Scatterplot: Finger Pulse Oximetry with Arterial Blood Oxygen
Saturation ...... 103
Figure 24 Scatterplot: Finger Pulse Oximetry - Arterial Blood Oxygen Saturation
Bias with Fraction of Inspired Oxygen ...... 104
Figure 25 Scatterplot: Finger Pulse Oximetry - Arterial Blood Oxygen Saturation
Bias with Partial Pressure of Oxygen Dissolved in Arterial Blood ...... 105
Figure 26 Scatterplot: Finger Pulse Oximetry - Arterial Blood Oxygen Saturation
Bias with Minute Ventilation ...... 106
Figure 27 Scatterplot: Finger Pulse Oximetry - Arterial Blood Oxygen Saturation
Bias with Heart Rate ...... 107
Figure 28 Scatterplot: Finger Pulse Oximetry - Arterial Blood Oxygen Saturation
Bias with Mean Arterial Pressure ...... 108
xviii
Figure 29 Scatterplot: Finger Pulse Oximetry - Arterial Blood Oxygen Saturation
Bias with Body Temperature ...... 109
Figure 30 Bland-Altman Plot: Buccal Pulse Oximetry and Arterial Blood
Oxygen Saturation ...... 111
Figure 31 Bland-Altman Plot: Finger Pulse Oximetry and Arterial Blood
Oxygen Saturation ...... 113
Figure 32 Buccal and Finger Pulse Oximetry Pulse Waveforms ...... 133
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I. INTRODUCTION
A. General Problem Area.
Clinicians employ many technologies to monitor and care for very ill patients. One such technology in intensive care units (ICUs) is continuous pulse oximetry (SpO2), which estimates arterial blood saturation with oxygen (Guyton & Hall, 2006d; Hodgson, Tuxen,
Holland, & Keating, 2009; Kober, Joldzo, Puskas, Gorove, & Hoerauf, 2003). SpO2 values are not used in isolation to make clinical decisions. Pulse oximetry can monitor trends and capture changes that are beneficial when caring for mechanically ventilated patients in hospitals or undergoing transport. The use of continuous pulse oximetry has extended to remote monitoring, such as with portable telemetry units (Bodin, 2003). SpO2 may help estimate global oxygen delivery (DO2), but will not reflect the amount of oxygen taken up by the tissues (VO2) and utilized by the tissues (Ahrens & Rutherford, 1993c, Leach &
Treacher, 2002). Partnered with end tidal carbon dioxide monitoring (ETCO2) or capnography, SpO2 may help clinicians to identify early drops in oxygen saturation (Jobes &
Nicolson, 1988; Nuhr et al., 2004; Paul, Mathoulin, & Whitehouse, 2013; Schutz, 2005).
This early warning system may also guide interventions such as hyper-oxygenation during patient repositioning, endotracheal suctioning, or transport for diagnostic studies.
Pulse oximetry may also substitute for other physiologic calculations. For example,
SpO2 may substitute for partial pressure of oxygen (PaO2) to estimate PaO2/fraction of inspired oxygen (FiO2) ratio, or PF ratio. Pulse oximetry/fraction of inspired oxygen (SF ratio) could be used as a substitute for PF ratio and marker of severity of lung disease in children. Lobete and colleagues (2013) found that SF ratio had a strong linear association with PF ratio, finding a PF ratio of 300 corresponding to an SF ratio of 296 (95% CI, 285-
1
308). Researchers surmised that SF served as an "adequate, noninvasive surrogate marker for PF ratio" for mechanically-ventilated, critically ill children (Lobete et al., 2013).
Similarly, Schmidt and colleagues found that SF and PF ratio had strong correlation in 3,767 adults (Schmidt, Gernand & Kakarala, 2015). They also determined that SF ratio could substitute for SF in an automated screening tool for acute respiratory distress syndrome
(Spearman rs = 0.72, p < .001).
Continuous pulse oximetry serves as an invaluable patient monitoring tool for critically injured patients and is employed in deployed settings, such as military field hospitals, and during air transport (W.C. Moore, personal communication, September 14,
2010). Critical Care Air Transport Teams (CCATTs) are 3-person military specialty teams capable of providing en route care for up to six critically ill patients. CCATTs ensure continuity and intensity of care. The success of these teams involves meticulous preplanning and vigilant care in the air (Ingalls et al., 2014). This preplanning and care includes setting up for continuous SpO2 monitoring to capture patient condition decline and identify the need to rapidly employ interventions.
War injuries such as burns to the face, neck, chest and extremities, massive soft tissue injuries, traumatic amputations, as well as presence of bandages and splinting devices may prevent access to traditional pulse oximetry monitoring sites (Fischer, 2010). Similarly, stateside critically ill patients may undergo declined physical conditions which may challenge or prevent pulse oximetry monitoring via traditional sites (Valdez-Lowe, Ghareeb,
& Artinian, 2009). In these instances, an alternate pulse oximetry monitoring site is needed.
Buccal site pulse oximetry lacks research-based, clinical application and, therefore, is considered an unconventional monitoring site for adult, critically ill patient populations.
2
Additionally, despite its frequent use, finger SpO2 is known to sometimes have questionable variability, such as with patients with chronic obstructive pulmonary disease
(Amalakanti, & Pentakota, 2016). It is possible that the buccal (cheek) area may retain blood flow in compromised states such as central hypotension whereas the finger sites may not
(Fernandez et al., 2007; Wan, Sun, Ristagno, Weil, & Tang, 2010). Therefore, the buccal site may be a feasible alternative when traditional pulse oximetry monitoring locations, such as the finger, ear lobe or toes, fail.
B. Purpose and Aims of the Study.
The purpose of this study is to: establish level of agreement (LOA) between buccal pulse oximetry (SpO2) and arterial blood oxygen saturation (SaO2) in mechanically ventilated adults, compare the LOA for buccal SpO2 and finger SpO2 relative to SaO2 in mechanically ventilated adults. The purpose of this study is also to investigate potential influencing variables to include: participant vital signs, physiological characteristics, and presence of vasoactive infusions influence buccal and finger SpO2 values in mechanically ventilated adults. These vital signs and physiological characteristics include: fraction of inspired oxygen (FiO2), partial pressure of oxygen dissolved in arterial blood (PaO2), minute ventilation (VE), heart rate (HR), mean arterial pressure (MAP), SpO2 waveform amplitude, presence of vasoactive medication intravenous infusions (vasoactive infusions), body temperature and skin phototype (pigment). A complete list of abbreviations used throughout this document is at Appendix A.
The specific aims of the present study were to:
1. Establish level of agreement (LOA) between buccal pulse oximetry (SpO2) and
arterial blood oxygen saturation (SaO2) in mechanically ventilated adults.
3
2. Compare the LOA for buccal SpO2 and finger SpO2 relative to SaO2 in
mechanically ventilated adults.
3. Examine the degree to which FiO2, PaO2, VE, HR, MAP, SpO2 waveform
amplitude, presence of vasoactive infusions body temperature and skin phototype
influence buccal and finger SpO2 in mechanically ventilated adults.
C. Need for More Study.
Optimal function of pulse oximetry requires a pulsating capillary bed measurable by the oximeter (Kyriacou, Moye, Choi, Langford, & Jones, 2001). Previous studies show the forehead is not as sensitive to vasoconstrictor responses and that blood flow may be better preserved over peripheral sites in cases of hypoperfusion (Awad et al., 2001; Nesseler et al.,
2012; Weesner, Walker, Shepherd & Patel, 1998). In some instances, the buccal area may be a better pulse oximetry monitoring site than the finger due to cerebral vasoprotection and preservation of blood flow to the head. In 1998, Weesner and colleagues performed a correlation study of reflectance buccal pulse oximetry on nine adult burn patients.
Researchers collected data during 21 surgical procedures and compared forehead reflectance pulse oximetry (RPO) values with traditional finger transmission pulse oximetry (TPO).
Specific results were not included in the study's published summary, however, Weesner and colleagues deemed the forehead and finger values differences as "nonsignificant" (Weesner et al., 1998).
In a 2010 rat study, Wan and colleagues (2010) induced hemorrhagic shock and found that the buccal mucosa and cerebral microvascular flow patterns were preserved. In adult patients with low cardiac indices, Fernandez and colleagues (2007) found that "during low cardiac output, the forehead sensor had better agreement with SaO2 than the finger
4 sensor." Researchers found bias + precision for finger was -1.16 + 1.62% and forehead was -
0.36 + 1.74%. Fernandez believed this was attributed to the forehead area arteries and capillaries lacking the vasoconstrictor response present in the fingers.
It may be possible that buccal capillary blood flow reflects tissue partial pressure of carbon dioxide (PCO2) and partial pressure of oxygen (PO2) changes much earlier than larger blood vessels. Fries and colleagues studied tissue PCO2 by inducing circulatory shock in male breeder Sprague-Dawley rats, discovering that buccal and gastric microvascular blood flow "closely related to the severity of tissue hypercarbia and therefore tissue hypoxia" (Fries et al., 2006). Researchers facilitated septic shock in the anesthetized, intubated animals by performing cecal ligation and puncture, allowing bowel contents to occupy the abdominal space. Researchers found blood flow decreased approximately 30 percent in smaller gastric and buccal vessels (<20µm) of these animals 4 hours before death, whereas blood flow to vessels > 20µm was only "minimally affected" and not until up to 2 hours before death (Fries et al., 2006).
Earlier blood flow and pulse oximetry value changes in buccal and microvascular blood flow would support faster clinician decision points and interventions, assisting better patient outcomes. Despite literature supporting preserved blood flow to the buccal area and earlier SpO2 changes, only four known studies investigated accuracy and/or precision of buccal pulse oximetry in adult patients.
Buccal pulse oximetry application in adults may have surfaced with limited formal study. Specific findings of literature review are presented in detail in Chapter 2. While researchers have studied the buccal site as a potential alternative pulse oximetry monitoring
5 site in adults, all studies were limited in scope and size (DeJong et al., 2011; Groudine, 1996;
2011; Gunter, 1989; Jobes & Nicolson, 1988; Rogers, 1997; Torres et al., 2005).
D. Significance of the Present Study.
The gold standard for assessing arterial blood oxygen saturation (SaO2) and partial pressure of oxygen (PaO2) is arterial blood gas (ABG) analysis. Anecdotal reports reveal that, in some cases of pulse oximetry failure, clinicians will opt for non-traditional sites such as the buccal corner of the mouth, the tongue or perform more frequent ABG analyses.
However, frequent ABG draws are not without consequence. When patients lack arterial lines, painful arterial punctures are required for ABG analysis. Further, the relationship between the number of blood draws, iatrogenic blood loss due to frequent blood draws and potential impact on length of stay in the ICU is always a concern (Alazia, Colavolpe, Botti,
Ramero, & Francois, 1996; Salem et al., 1991; Turek, Cerny, Parizkova, & Dostal, 2006).
Closed blood draw systems have reduced but not obliterated iatrogenic blood loss with blood sampling. Iatrogenic blood loss may facilitate anemia in critically ill patients (Turek et al.,
2006). Frequent arterial blood sampling is undesirable during military aeromedical transport missions. Processing ABGs in the non-traditional environment of a military cargo aircraft involves use of a point of care testing device and temperature-sensitive cartridges.
When pulse oximetry fails, clinicians lose a clinically useful tool that estimates arterial blood oxygen saturation and helps identify potential oxygenation problems early on
(Schutz, 2005). Loss of continuous pulse oximetry data can be concerning in the patient transport environment, where other clinical examination options for changing oxygenation status are limited. Examining patient skin and mucus membrane color for signs of oxygen deterioration, for example, is impossible in the back of an aircraft in low-light situations.
6
The buccal area may be a viable alternative monitoring site when traditional pulse oximetry monitoring sites fail.
Literature search revealed one case report, five letters to the editor and only three known studies on buccal oximetry in adults (De Jong et al., 2011; Groudine, 1998; Gunter,
1989; Jobes & Nicolson, 1988; Landon, Benumof, & O'Leary, 1992; O'Leary, Landon, &
Benumof, 1992; Rogers & Gan, 1997; Sosis, 1990; Torres et al., 2005). Only one of the three studies investigated accuracy of buccal TPO in the critically ill intensive care unit
(ICU) adult population (O'Leary et al., 1992). The present study will help critical care clinicians make informed clinical decisions and recognize limitations when selecting alternative pulse oximetry monitoring sites such as the buccal area.
E. Guiding Framework: Oxygen Transport and Oxygenation.
Oxygenation is defined as the "addition of oxygen to any chemical or physical system" (Hensyl & Felscher, 1987). Therefore, oxygen transport feeds into oxygenation.
Ordinarily, 97% of arterial oxygen is bound to hemoglobin. The remaining 3% of the oxygen is dissolved in plasma (Guyton & Hall, 2006e). Pulse oximetry (SpO2) is a measure of hemoglobin saturation with oxygen, which is a major component of oxygen delivery.
SpO2 estimates SaO2 by deriving the ratio of oxygenated hemoglobin to total hemoglobin.
This study focused on measurement of arterial blood oxygenation as it pertains to the addition of oxygen (bound) to arterial blood hemoglobin and reflected in SpO2. Integration of oxygenation and oxygen transport in relation to SpO2 is illustrated in Figure 1 and explained further below.
1. Oxygen Transport.
Oxygen transport is dependent on diffusion and blood flow (Guyton & Hall, 2006e).
7
Ahrens and Rutherford described four factors that affect oxygen transport as: partial pressure of oxygen (PaO2), hemoglobin, hemoglobin saturation with oxygen (SaO2) and cardiac output (Ahrens & Rutherford, 1993b). In relation to pulse oximetry, these factors are further divided into these subcategories: PaO2, hemoglobin affinity for oxygen, dyshemoglobins, regional blood flow, and saturation/de-saturation times. These factors will be explored in
Chapter 2.
2. Oxygenation.
In the absence of pulmonary pathology, alveolar PaO2 determines the amount of oxygen that enters the blood stream. In turn, PaO2 is affected by fraction of inspired oxygen
(FiO2). V/Q ratio expresses the relationship between alveolar ventilation (liters of gas/minute) and alveolar capillary blood flow (liters/minute) (Guyton & Hall, 2006d; Wilson
& Thompson, 1990). Various lung conditions and body positions, such as supine (flat) or high fowler (head of bed elevated can vary V/Q ratio. Impaired oxygen binding may impair oxygen transport (Haymond, Cariappa, Eby, & Scott, 2004). The oxyhemoglobin dissociation curve (OHDC) describes hemoglobin’s affinity for oxygen, and accounts for affecting factors (Ahrens & Rutherford, 1993d; Guyton & Hall, 2006d). These factors include temperature, carbon dioxide levels, pH and levels of diphosphoglycerate (2, 3-DPG), which decreases the oxygen affinity of hemoglobin and facilitates release of oxygen by the red blood cell. Impaired oxygen binding to hemoglobin can also occur in conditions of dysfunctional hemoglobin.
Oxygen transport and oxygenation was the guiding framework for this study
(Figure 1). Factors relative to oxygen transport and oxygenation included: oxygen saturation
8
Figure 1. Conditions and Predictors Affecting Pulse Oximetry. Integration of oxygen transport and oxygenation and effects on pulse oximetry. FiO2 influences PAO2 and PaO2, which then then influences saturation of arterial blood. Arterial blood oxygen saturation was investigated in this study. VE impacts acid/base balance and oxygen affinity to and binding with red blood cells. Vasoactive infusions stimulate the SNS, which then increases HR, cardiac output and MAP. SNS stimulation and body temperature may potentiate peripheral vasoconstriction, vessel diameter and subsequent capillary blood flow and pulse waveform amplitude. MAP, which may affect compensatory peripheral auto-regulation, such as vasoconstriction to shunt blood centrally, may also affect capillary blood flow. Skin phototype may influence accuracy of pulse oximetry. SpO2 = pulse oximetry; hgb = hemoglobin; PaO2 = partial pressure of arterial oxygen; O2 = oxygen; MAP = mean arterial pressure; PAO2 = alveolar oxygen concentration; 2, 3-DPG = 2, 3-Diphosphoglycerate; FiO2 = fraction of inspired oxygen; SNS = sympathetic nervous system.
9 of arterial blood saturation SaO2, fraction of inspired oxygen (FiO2), partial pressure of oxygen (PaO2), minute ventilation (VE), heart rate (HR), and mean arterial pressure (MAP).
FiO2 directly influences alveolar arterial oxygenation, PaO2 and oxygen saturation of arterial blood (SaO2). Minute ventilation impacts acid/base balance and oxygen affinity with hemoglobin. HR is a component of cardiac output and adequate transport of oxygenated blood. MAP, which affects compensatory peripheral auto-regulation, such as peripheral vasoconstriction to centrally shunt blood, and presence of vasoactive infusions, may impact peripheral vasoconstriction. Additionally, body temperature and SpO2 waveform amplitude were also studied. Body temperature influences local vasoconstriction/dilation, while SpO2 waveform amplitude represents SpO2 site capillary blood flow.
F. Pulse Oximetry Technology.
Adequate pulse strength, supported by adequate MAP, is needed for pulse oximeter sensors to retrieve an accurate plethysmograph waveform and oximetry value. Pulse oximetry (SpO2) measures the "saturation of hemoglobin based on light spectral differences between hemoglobin and oxyhemoglobin" using a light source device applied over a local capillary bed (Rutherford, 1993). SpO2 sensors have two main features--a light source and a light detector mechanism. The light source is comprised of two diodes, one of which emits infrared light while the other emits red light (Enekvist & Johansson, 2016; Nellcor, 2003a,
2003b, 2010; Rutherford, 1993; Sharma & Haber, 2002). Saturated oxyhemoglobin more readily absorbs infrared light and allows red light to pass through. Conversely, reduced oxyhemoglobin (deoxyhemoglobin) absorbs red light and allows infrared light to pass through (Nellcor, 2003a; Rutherford, 1993; Sharma & Haber, 2002).
There are two types of pulse oximetry technology--reflectance pulse oximetry (RPO)
10 between the light emitter and light detector (Figure 2). Traditional TPO monitoring sites include the fingers, toes and earlobes. In RPO, the light detectors lie adjacent, or around the light emitter. Therefore, RPO sensors tend to be placed flat against the selected monitoring site, such as the forehead. Theoretically, the further a pulse oximeter monitoring site is from the left ventricle (see figure 3), which ejects newly-oxygenated blood, the longer it will take to display a saturation reading change (De Jong et al., 2011; Hamber et al., 1999; MacLeod,
Cortinez, Keifer, Radu, & Somma, 2003).
G. Device Accuracy and Precision.
Accuracy is defined as the extent to which an instrument, in this case the device,
"measured the domain defined in the study" (Burns & Grove, 2001b). In the present study, accuracy was defined as how well buccal SpO2 agreed with SaO2. On the other hand, precision is the degree to which several measurements provide answers very close to each other or the consistency of "measurements made with physiological instruments" (Burns &
Grove, 2001b). Precision does not equal accuracy. An instrument or device may produce measurements which are very close to each other but consistently distant from the standard.
Device inaccuracy and imprecision are two potential sources of error when studying physiological data. Other potential sources of error when acquiring physiologic data can be grouped into five categories: environment, user, subject (participant), machine/device and interpretation (Burns & Grove, 2001b). These categories will be explored in more detail in
Chapters 2 and 3.
Different environmental characteristics, such as climate and ambient temperature, may influence a device's ability to accurately measure physiological data. Without trained and scripted controls, variance in user technique may also alter results. Both factors, and
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Figure 2. Pulse Oximetry Reflectance and Transmittance Technologies. Pulse oximeter sensor technology types: transmittance and reflectance pulse oximetry sensor light detector/ emitter locations (adapted from Kugelman, Wasserman, Mor et al., 2004)
12
Figure 3. Blood Flow Distance and Desaturation/Resaturation Times. Effects of distance from left ventricle on desaturation/re-saturation times as reflected in pulse oximetry.
13 transmittance (or transmission) pulse oximetry (TPO). The RPO and TPO technologies primarily differ in how light emitter and light detectors are positioned in relation to the arterial bed (Rutherford, 1993b). TPO pulse oximetry requires a pulsating bed in however, may be controlled both by regulated temperature, such as within a hospital setting and through well-defined user protocols and procedures. Patient characteristics, such as skin phototype, and elements that impact regional blood flow, such as MAP, body temperature or presence of vasoactive infusions, may also impact device accuracy and precision.
Aside from patient characteristics, device technology may also impact precision.
Ahrens (2006) examined potential differences in device precision across three top brands of pulse oximetry devices. Three finger pulse oximetry sensors were simultaneously placed on digits of the participants' same hand and each sensor attached to oximeters manufactured by
Nellcor, Masimo and Philips. Using Bland-Altman analyses, researchers found bias and precision as follows: Nellcor (0.18 + 2.25), Masimo (0.31 + 1.98) and Philips (0.19 + 2.58)
(Ahrens, 2006). The researcher determined that the NellcorTM OxiMax N-600 was the most precise of the three devices, finding a statistically significant decrease in precision (p =
0.008) in both the Philips and Masimo oximeters (Ahrens, 2006).
Nellcor researchers also studied device accuracy and precision on 12 healthy volunteers. Researchers applied stepwise exposure to reduced oxygen mixtures and conducted over 300 arterial blood draws. Researchers found that mean bias was 0.1 between the MAX-N adhesive finger sensor with the OxiMax N-600 pulse oximeter compared to arterial blood samples with arterial blood saturation levels between 70 – 100% (Nellcor,
2005). Precision was 1.8 and the root-mean-square of the differences (ARMS) between finger
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SpO2 and SaO2 was also 1.8 (Nellcor, 2005). Researchers found that over 60% of the N-600 monitor and MAX-N sensor readings were within +2% of SaO2 (Nellcor, 2005, 2010).
15
II. REVIEW OF THE LITERATURE
Despite sparse published literature on buccal pulse oximetry, there are anecdotal reports from stateside intensive care units (ICUs) and from the deployed environment of buccal pulse oximetry use when traditional sites fail. Clinicians try alternative pulse oximetry monitoring sites when conditions such as when peripheral vasoconstriction or central hypotension challenge the use of traditional sites. The buccal area is one of those alternative sites.
One early case report mentioned using the tongue as an alternate pulse oximetry monitoring site for pediatric patients (Jobes & Nicolson, 1988). Jobes and Nicolson asserted only that the buccal pulse oximetry appeared to function "normally." Five subsequent letters to the editor illustrate evolution to using the buccal area as an alternative pulse oximetry monitoring site. These letters to the editor only presented sensor application techniques and interventions for infection control, but offered no research or analyses.
Only three known studies have investigated buccal pulse oximetry (SpO2). However, these studies revealed conflicting findings on the accuracy and precision of buccal pulse oximetry. One study found buccal SpO2 agreeing more closely to SaO2 than finger SpO2
(Landon et al., 1992; O'Leary et al., 1992). Another study found that precision was poor in both buccal and finger SpO2 (Torres et al., 2005). Torres and colleagues found that the buccal site demonstrated wider bias than the finger monitoring site. De Jong and colleagues found buccal SpO2 imprecise and inaccurate at saturations less than 90% (De Jong et al.,
2011). These studies will be described in detail later in this chapter. Notably absent from these few studies were influencing variables for pulse oximetry values to include: respiratory volume, hypotension, hypothermia, presence of vasopressor infusions and skin phototype.
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A literature review was accomplished using Ovid to access MEDLINE and CINAHL using key words: buccal oximetry, pulse oximetry, oximetry, oxygen saturation, and plethysmography. Secondary references were also retrieved using key words: pulse oximetry accuracy, precision, dyshemoglobinemia, dyshemoglobin, arterial blood gas and plethysmographic waveform. The purpose of the literature review was to identify past research in buccal pulse oximetry, investigate relevant oxygen transport and delivery issues, examine pulse oximetry technology, and identify factors affecting pulse oximetry device accuracy and precision. This chapter will begin by outline known buccal pulse oximetry studies and describe critical factors that affect oxygen transport and delivery. Additionally, this chapter will explain the major functional differences between the two types of peripheral pulse oximetry technologies. This chapter will review factors which may affect measurement of the gold standard, SaO2. Lastly, using the categories: user, participant, machine/device and interpretation, this chapter will also explain factors which may influence device accuracy and precision.
A. Previous Research Leading to Buccal Pulse Oximetry.
Research is sparse surrounding buccal pulse oximetry. The following is a brief chronological synopsis of the known buccal pulse oximetry case reports and research
(summary of articles from 1988 to 2011 at Table 1). Jobes and Nicolson (1988) published case reports on use of a modified finger pulse oximetry sensor on the tongue. They sought a reliable alternate sensor site in cases of hypothermia, decreased cardiac output, and increased systemic vascular resistance--conditions common during cardiopulmonary bypass. Two of their case reports were on infants and one was on a 15-year-old child. They cited that, in all three cases, the tongue pulse oximeter continued to function "normally" and reflected
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Table 1
Limited Buccal Pulse Oximetry Literature: Evolution and Summary
Author, Year Title Article Type/Approach General Finding Jobes & Monitoring of arterial Case report. Modified Pulse oximeter functioned Nicolson, hemoglobin oxygen disposable SpO2 sensors "normally", and reflected SaO2 1988 saturation using a for use on the tongues of values (no analyses) tongue sensor 3 pediatric patients Gunter, 1989 A buccal sensor for Letter to the editor Difficulty applying large sensors measuring arterial on the tongue of infants, sensors oxygen saturation slipped off tongue. Proposed modifying a disposable finger sensor for use in the buccal area instead Sosis, 1990 Use of an Ohmeda Letter to the editor Suggested using an ear clip-on ear oximetry probe reusable sensor in the buccal for "buccal" oximetry area Landon et al., Buccal pulse 1992 oximetry: an accurate Study. Compared buccal Modified disposable finger alternative to the and finger SpO2 with sensors for buccal use. Found finger probe SaO2 in 23 adults buccal SpO2 values were higher O'Leary et Buccal pulse undergoing general than finger SpO2 values and al., 1992 oximeter is more anesthesia and 10 adults buccal SpO2 agreed more closely accurate than finger in the ICU with SaO2 than finger SpO2 pulse oximeter in measuring oxygen saturation Groudine, Cost-effective buccal Letter to editor For cost effectiveness, proposed 1996 oximetry using a reusable finger SpO2 sensor in the buccal area and placing a vinyl glove over the sensor for infection control Rogers & Pulse oximeter probe Letter to the editor Proposed using the thumb Gan, 1997 sheath for buccal use portion of disposal glove to cover a reusable finger sensor for the buccal area Groudine, Informed use of Letter to editor (in Cautioned that use of a glove 1997 buccal oximetry response to Rogers & over a reusable sensor, as Gan, 1997) recommended 2 years prior, could impact device accuracy Torres et al., Distal extremity vs. Study. Compared Found SpO2 precision poor 2005 buccal pulse oximetry reusable finger sensors in regardless of site; buccal had after cardiac surgery the buccal area with wider bias than finger SpO2 in disposable sensors on children undergoing digits of 25 children cardiopulmonary bypass De Jong et Accuracy and Study. Compared buccal Buccal SpO2 was inaccurate and al., 2011 precision of buccal SpO2 to SaO2 and finger imprecise at saturations less than pulse oximetry SpO2 in 53 healthy adults 90%
18 arterial blood oximetry values. No specific data or analyses were provided. Jobes and
Nicolson noted that the tongue sensor pulse oximetry plethysmograph (POP) waveform revealed a more dramatic baseline variation than the peripheral POP waveforms. This phenomenon will be discussed later in chapter 5.
In a 1989 letter to the editor, Gunter described difficulty with applying the techniques proposed by Jobes for tongue SpO2 monitoring (Gunter, 1989). Gunter found that the tiny infant anatomy challenged ability to keep the large sensors from slipping off the tongue.
Gunter then described positioning a modified a finger pulse oximeter into the corner (buccal region) of the patient's mouth instead (Gunter, 1989). Gunter also stated that the modified digit sensor seemed to "offer reliable monitoring" during arterial vasoconstriction or low cardiac output. Once again, no data or analyses was provided.
In a 1990 letter to the editor, Sosis suggested using a non-disposable, ear clip-on
SpO2 sensor for the buccal area instead of modified, disposable finger sensor as proposed by
Gunter (Sosis, 1990; Gunter, 1989). With no data or analyses presented, Sosis stated the buccal area "worked well in situations" where other sites failed.
In 1992, Landon et al. and O'Leary et al. compared buccal SpO2 to digital SpO2 and
SaO2 on 23 adult patients in the operating room and 10 adult patients in the ICU.
Researchers modified disposable finger sensors for use in the buccal area. Bland-Altman analyses revealed that, during periods of hemodynamic instability, buccal SpO2 values were closer to SaO2 (mean difference -.1, SEM 0.3, LOA 2.7 to -2.9) than finger SpO2 values
(mean difference -0.8, SEM 0.3, LOA 2.4 to 3.9).
It appears that early buccal pulse oximetry and cost containment efforts then produced infection control concerns. In a 1996 letter to the editor, as a cost-containment,
19 hygienic buccal pulse oximetry approach, Groudine proposed placing a vinyl glove over a non-disposable finger sensor (Groudine, 1996). Like Groudine, in 1997, Rogers and Gan encased a portion of a non-disposable finger sensor with the thumb cut-off from a disposable vinyl glove prior to inserting into the patient's corner of the mouth. One year later, in response to Rogers and Gan (1997), Groudine submitted a letter to the editor cautioning that the vinyl glove modification could impact device accuracy (Groudine, 1998).
In 2005, Torres and colleagues compared pulse oximetry using non-disposable clip- on sensors placed in the buccal area to pulse oximetry from sensors placed on digits. In 25 pediatric patients (16.4 + 25.5 months) with congenital heart disease who were undergoing cardiopulmonary bypass in the operating room, researchers analyzed 64 paired measures, finding buccal SpO2 - SaO2 bias 2.6 + 8.7% (p = .76) and digit SpO2 - SaO2 bias 2.2 + 5.2%
(p = .76). Although buccal SpO2 - SaO2 bias was larger than digit SpO2 - SaO2 bias, Torres and colleagues (2005) determined that neither method was precise in this patient population.
More recently, De Jong and colleagues (2011) induced hypoxemia in 53 healthy adult volunteers and compared buccal SpO2, with finger SpO2, and SaO2. Researchers applied a non-disposable pulse oximetry sensor to participants' finger and a non-disposable NellcorTM
VetSat (veterinarian) pulse oximeter to the buccal corner of participants' mouth. De Jong and colleagues found that buccal SpO2 was inaccurate and imprecise at saturations less than 90%.
At induced hypoxemia to 90% (n = 44), Bland-Altman analyses revealed buccal SpO2 - SaO2 mean bias at .3% (95% CI, lower LOA -5.8% to -3.2%, upper LOA 3.8% to 6.4%). At induced hypoxemia to 80% (n = 39), buccal SpO2 - SaO2 mean bias was 2.4% (95% CI, lower LOA -3.2% to -.7%, upper LOA 5.4% to 7.9%). At induced hypoxemia to 70% (n =
36), mean bias was 2.6% (95% CI, lower LOA -4.9% to 1.4%, upper LOA 6.7% to 10.2%).
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Researchers did not analyze finger SpO2 with SaO2, preventing ability to compare finger and buccal site LOA with SaO2.
B. Oxygen Transport.
As illustrated in Figure 1, oxygen transport directly impacts oxygenation. Four elements important to oxygen transport and oxygenation are partial pressure of oxygen
(PO2), hemoglobin, hemoglobin saturation with oxygen (SaO2) and cardiac output (Ahrens &
Rutherford, 1993b; Guyton & Hall, 2006e). As oxygen molecules are inhaled, they travel through primary, secondary and tertiary airways, entering the pulmonary bloodstream through the alveoli/alveolar capillary exchange. As the oxygen molecules enter the pulmonary capillary blood supply, each hemoglobin molecule can bind with up to four oxygen molecules (Guyton & Hall, 2006d; Rutherford, 1993). The newly oxygenated hemoglobin molecules then travel through the pulmonary venous system, and are expelled via the left atrium and ventricle into systemic circulation. The percentage of arterial hemoglobin that is saturated with oxygen may be estimated via pulse oximetry (SpO2).
Oxyhemoglobin is the chemical compound of hemoglobin and oxygen (Dickson, 1995) which then circulates and facilitates oxygen availability to tissues throughout the body
(Guyton & Hall, 2006e).
Pulse oximetry will not replace the gold standard arterial blood gas, especially when arterial blood oxygen content is of concern and the patient condition deteriorates. However, as a convenient, non-invasive modality, pulse oximetry can help identify important trends that can guide early clinician action. The value of pulse oximetry is the continuous and dynamic information it provides clinicians. Arterial blood gas analysis will also most likely be performed when pulse oximetry values are of concern. Pulse oximetry remains a
21 fundamental surveillance tool in many clinical settings and serves as an early warning system for impending or actual oxygenation status changes.
1. Hypoxemia, Hypoxia and Pulse Oximetry.
Pulse oximetry monitoring sites may respond differently to hypoxia and hypothermic conditions. The terms hypoxemia and hypoxia are sometimes used interchangeably in the literature when referring to abnormally low levels of oxygen saturation. Hypoxemia refers to abnormally low concentration of oxygen in arterial blood while hypoxia refers to a deficiency in the amount of oxygen reaching tissues or failure of oxygenation at the tissue level” (Hensyl & Felscher, 1987; Samuel & Franklin, 2008). Hypoxemia has been reported in the literature as impacting pulse oximetry accuracy, particularly at saturation levels below
80%. However, potential inaccuracies of oximetry at lower saturation levels have low clinical significance interventions and additional surveillance methods are typically added for more unstable patients.
a. Induced hypoxemia. In 1986, Sendak and colleagues conducted a canine study to investigate pulse oximetry accuracy during periods of severe desaturation
(Sendak, Harris, & Donham, 1986). Using a NellcorTM N-100C sensor applied to the tongues of intubated, anesthetized, and mechanically ventilated dogs, FiO2 was alternated from 3% to
100%. After 2-minute stabilization periods, tongue SpO2 values, which ranged from 8% to
99%, were compared to SaO2. Linear regression analyses revealed that the tongue pulse oximetry values closely reflected SaO2 (y = .97x + 6.93; r = 0.98) (Sendak et al., 1986).
b. Comparison of pulse oximeter performance. Similarly interested in potential effects of hypoxia and pulse oximetry responses, researchers studied the response of seven pulse oximeters during induced hypoxia (Trivedi, Ghouri, Lai, Shah, & Barker, 1997).
22
Researchers fitted eight healthy adult volunteers (19 to 37 years old) with a rubber mouthpiece that attached to an anesthesia breathing circuit. Hypoxia was induced by administering a varying oxygen/nitrogen gas mixture concentration. Participants were allowed to desaturate down to levels "below 80%" as reflected by the NellcorTM pulse oximeter. Trivedi and colleagues found that all seven pulse oximeters were within 2% to 3% in error when compared to SaO2 and with room air. However, researchers also found that
TM SpO2 - SaO2 bias increased as SaO2 values decreased to 75% or lower. The Nellcor N-200 finger sensor had a mean error of -6.7 + 7.5%. All seven pulse oximeters were inaccurate at
SaO2 nearing 75% (Trivedi et al., 1997).
c. Performance of NellcorTM Pulse Oximeter in Induced Hypoxemia.
In 2005, Nellcor researchers investigated pulse oximetry response and accuracy during induced hypoxemia on 12 adult volunteers. Researchers exposed volunteers to a hypoxic air/nitrogen mixture, collecting over 300 arterial blood samples within SaO2 ranging from
53.3% to 99.5%. Researchers found that the NellcorTM OxiMax N-600 sensor, when combined with the OxiMax-N pulse oximeter, demonstrated a mean bias of 0.1 and root- mean-square of the differences (ARMS) for SaO2 values between 70% to 100%. Researchers also found that the same pulse oximeter/sensor combination demonstrated mean bias of 0.7 and ARMS of 2.6 for SaO2 range of 60% to 80% (Nellcor, 2005). These were the same model of sensor and pulse oximeter used in the present study.
C. Oxygenation.
1. Partial Pressure of Oxygen and Fraction of Inspired Oxygen.
Oxygen molecules diffuse from areas of higher concentration to areas of lower concentration (Bridges & Schmelz, 2009; Guyton & Hall, 2006d). As oxygen partial
23 pressure is greater in the alveoli than in the pulmonary capillary, this gradient eases oxygen diffusion into the pulmonary capillary (Guyton & Hall, 2006d, 2006e). Therefore, the partial pressure of oxygen in the alveoli is a strong determining factor in the saturation level of hemoglobin with oxygen (Ahrens, 1993a). The amount of oxygen dissolved in arterial blood is partial pressure of oxygen, or PaO2 (Guyton & Hall, 2006d). Normal ambient air contains approximately 21% oxygen. In critically ill, mechanically-ventilated patients, inhaled oxygen concentration is manipulated based on the clinical needs of the patient by adjusting the fraction of inspired oxygen (FiO2). Higher inspired O2 levels (FiO2 in intubated, mechanically-ventilated patients) displace alveolar nitrogen, increase alveolar oxygen concentration and raise alveolar partial pressure of oxygen levels (Ahrens, 1993a).
2. Hemoglobin Affinity for Oxygen.
Several factors and conditions such as body temperature, carbon dioxide levels, pH and levels of 2, 3-DPG, a phosphate compound that serves as an “oxygen cleaver,” may influence the ease of oxygen binding to a hemoglobin molecule (Ahrens, 1993d, MacDonald,
1977). Changes in oxygen's affinity to hemoglobin are described as OHDC shifts to the right or left. A shift of the OHDC to the right indicates that oxygen is less tightly bound to hemoglobin. Chemical changes and physiological conditions that will cause oxygen to be more loosely bound (less affinity) to hemoglobin are prompted by hyperthermia, acidosis, hypercarbia and increased 2, 3-DPG levels (Ahrens & Rutherford, 1993b; Guyton & Hall,
2006d). Alterations in VE can also affect acid/base balance and, therefore, hemoglobin/oxygen affinity. On the other hand, a shift of the OHDC to the left indicates that oxygen is more tightly bound to hemoglobin. Chemical changes that increase oxygen
24 affinity to hemoglobin include alkalosis, hypocarbia, hypothermia and lower 2, 3-DPG levels
(Guyton & Hall, 2006d; Rutherford, 1993).
3. Dyshemoglobins.
In certain conditions, oxygen may be abundant, but unable to bind with dysfunctional hemoglobin. Dysfunctional hemoglobin conditions may result when substances compete with oxygen in binding to hemoglobin (Rutherford, 1993; Sharma & Haber, 2002). Specific examples of dysfunctional hemoglobins are methemoglobin, carboxyhemoglobin and sulfhemoglobin (Rutherford, 1993; Vierheller, 1993).
Current pulse oximeter technology cannot accurately discern levels of oxygen saturation in the presence of carboxyhemoglobinemia and methemoglobinemia. Pulse oximetry values may falsely read near normal despite actual, extreme hypoxemia (Haymond et al., 2004). Methemoglobin equally absorbs both types of light (660 nm and 940 nm) emitted by the pulse oximeter. Pulse oximeter sensor light wavelengths will be detailed later in this chapter.
a. Methemoglobinemia. Methemoglobinemia may be congenital or acquired and occurs when the deoxygenated hemoglobin molecule is "oxidized from the ferrous to ferric state" (Benz, 2001; Sharma & Haber, 2002). Acquired methemoglobinemia could occur because of exposure to nitrates, nitrites and sulfones (Shihana, Dissanayake,
Buckley, & Dawson, 2010; Sin & Shafran, 1996). Methemoglobin cannot transport oxygen and may also cause inaccurate pulse oximetry readings (Sharma & Haber, 2002). At higher than normal methemoglobin levels, the pulse oximeter tends to overestimate hemoglobin saturation (Sharma & Haber, 2002).
25
b. Carboxyhemoglobin. Carboxyhemoglobin results when carbon monoxide binds to hemoglobin instead of oxygen. Carboxyhemoglobin decreases circulating oxygen values and may result in false high SpO2 values despite actual markedly low oxygen- carrying capacity (Feiner et al., 2013; Rutherford, 1993). Carboxyhemoglobin also renders oximetry inaccurate as the pulse oximeter cannot differentiate carboxyhemoglobin from oxyhemoglobin (Barker & Badal, 2008; Haymond et al., 2004; Sharma & Haber, 2002;
Weinberger & Drazen, 2001).
c. Sulfhemoglobin. Sulfhemoglobin is less reported in the literature and is deemed rare possibly due to the "difficulty in inducing sulfhemoglobinemia in vivo
(Aravindhan & Chisholm, 2000). Although listed as a dyshemoglobin condition and potentially caused by oxidation of hemoglobin induced by certain drugs and chemicals, it is unclear whether sulfhemoglobin truly impacts pulse oximetry operation or accuracy
(Aravindhan & Chisholm, 2000).
4. Cardiac Output.
Cardiac output is the volume of blood delivered through the systemic circulation. It is expressed in volume (milliliters) over time (per minute) and considers heart rate, peripheral vascular resistance (also termed systemic vascular resistance), and stroke volume (Darovic,
Graham, & Pranulis, 2002). The normal cardiac output range is between 4 – 8 liters per minute. Peripheral vascular resistance is the “average resistance to blood flow throughout the entire systemic circulation (Darovic, 2002). Stroke volume is normally about 70 mL and is the volume of blood ejected per beat by the left ventricle. More precisely, stroke volume is the difference between the ventricular end-diastolic volume and end-systolic volume
(Hazinski, 2002).
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Other parameters related to cardiac output and used to assess adequacy of perfusion include mean arterial pressure (MAP) and blood flow. MAP is estimated by dividing the sum of systolic blood pressure and two times diastolic blood pressure by three. MAP is expressed in millimeters of mercury (mm/Hg) and is an average of arterial pressures measured over a period (Guyton & Hall, 2006b). Blood flow is the quantity of blood flowing within a specific period past a specific point and may be expressed in liters or milliliters per minute. Blood flow through a vessel is illustrated by Ohm's law, which states that flow is equal to pressure difference (or gradient) divided by vascular resistance (Guyton & Hall,
2006c).
a. Hypovolemia. Hypovolemia can result in decreased blood pressure because of lower circulating intravascular volume. Lower blood pressure levels may lead to decreased peripheral pulses and, potentially, pulse oximetry failure. In a study of 18 adult patients (59 + 18 years) undergoing cardiopulmonary bypass, researchers compared performance of two distinct brands of pulse oximeter devices (Irita, Kai, Akiyoshi, Tanaka,
& Takahashi, 2003). Researchers found that one pulse oximeter device failed at a MAP of
47 + 12 in combination with core (bladder) temperatures of 34.2 + 2.5°C. The second pulse oximeter failed at a MAP of 49 + 5 when combined with bladder temperatures of 36.1 +
1.6°C. Researchers also found that one pulse oximetry device failed more frequently in patients who had undergone preoperative diuretic therapy and were suspected as being hypovolemic (Irita et al., 2003).
In 2000, Vicenzi, Gombotz, Krenn, Dorn, and Rehak compared transesophageal and finger SpO2 values in 40 critically ill adults. Bland-Altman analysis revealed that finger pulse oximetry underestimated SaO2 (median -2.1%, -1.8 and -2.4; 95% upper/lower
27 confidence limits), when compared to transesophageal SpO2 (median -0.2%, 0.2 and -0.1;
95% upper/lower confidence limits). Finger SpO2 also demonstrated slightly more
"dependence" on arterial blood pressure than transesophageal pulse oximetry.
b. Mean arterial pressure and peripheral pulse pressure. Pulse oximetry function depends on ability of the equipment to distinguish between oxygenated and deoxygenated hemoglobin (Rutherford, 1993b). As previously explained, the oximeter must distinguish good pulse amplitude for the device to differentiate between the oxygenated arteriole capillary bed and venule capillary blood flow. Low flow states can occur in instances of increased peripheral vasoconstriction caused by conditions such as decreased cardiac output, hypotension, hypothermia, stress response and presence of vasopressor infusions (Agashe, Coakley, & Mannheimer, 2006; Cannesson et al., 2008; O'Leary et al.,
1992; Sheridan, Prelack, Petras, Szyfelbein, & Tompkins, 1995; Torres et al., 2005).
Peripheral monitoring sites more vulnerable to vasoconstriction, such as fingers and toes, could manifest more evident pulse amplitude changes.
Nevertheless, other researchers found that even very low peripheral blood flow rates could still produce measurable pulse oximetry (Lawson, Norley, Korbon, Loeb, & Ellis,
1987). They studied the sensitivity of pulse oximetry detection of pulsatile blood flow using a laser Doppler flow probe to measure peripheral blood flow. Specifically, they increased tourniquet occlusion to the arms of adult volunteers until the oximeter device lost pulse sensing. In some cases, peripheral blood flow rates as low as 4 – 8.6% from baseline still enabled measurement of pulse oximetry. These results conflict with the dominant theory that decreased pulse amplitude negatively effects accurate pulse oximetry measurement and monitoring. The ability to retain an adequate pulse oximetry waveform despite low flow
28 indicates there are other contributing factors affecting pulse oximetry (Lawson et al., 1987).
Therefore, MAP, as it influences pressure gradient, blood flow and possibly pulse amplitude, was included in the present study.
Torres and colleagues (2003) compared pulse oximetry accuracy from the buccal area and finger in 25 children with congenital heart disease who were undergoing cardiopulmonary bypass (CPB). Researchers found a significant difference in absolute buccal SpO2 – SaO2 (5.3 + 7.3%) and absolute digit SpO2 – SaO2 (3.4 + 4.4%) (p = 0.002).
Researchers concluded that, in pediatrics undergoing CPB, pulse oximetry precision was poor in both finger and buccal monitoring sites (Torres et al., 2003). These findings may have occurred because of loss of pulsatile blood flow and inability of the oximeter to distinguish between venous and arterial capillary flow. However, newer CPB technology enables pulsatile pump modes. Although not the primary focus of their research, Simons and colleagues (2010) found that a low-resistant, high-compliant CPB oxygenator provided better pulse conduction than a high-resistant, low-compliant oxygenator. Buccal pulse oximetry in adult CPB patients would be a potential area for future research.
5. Regional Blood Flow: Body Temperature, Vasoconstriction, Pulse
Amplitude, and Blood Flow Velocity.
Pulsatile blood flow at the SpO2 monitoring site, which depends in part on cardiac output, allows the pulse oximetry sensor to distinguish between arteriole and venule capillary blood flow (Haldane, 2002; Rutherford, 1993). This distinction is key to function and accuracy of the pulse oximeter. Many physiologic conditions may affect local blood flow to peripheral pulse oximetry monitoring sites and may affect pulse oximetry values. These physiologic conditions include vasoconstriction potentiated by vasopressor infusions, in
29 response to hypothermia or shock states, or presence of peripheral vascular disease or hypotension (Darovic & Simonelli, 2002). These conditions are common in critically ill patients and may present simultaneously, challenging accurate peripheral pulse oximetry.
Actual pulse oximetry desaturation/re-saturation time differences, the specifics of blood flow and peripheral travel time require investigation of microvascular flow and are beyond the scope of this study. However, pulse oximetry plethysmographic (POP) waveform amplitude, in part affected by capillary pulse pressure, was investigated in the present study.
a. Vasoconstriction. In stress response and presence of vasopressor infusions, blood will shunt from the less vital skin and viscera to the skeletal muscles, brain, and heart. Stimulation of the sympathetic nervous system (SNS) as in the stress response or vasopressor infusions can potentiate peripheral vasoconstriction. The SNS can potentiate alpha 1 stimulation, resulting in arterial vasoconstriction (Moser, Riegel, Paul, Lennie, &
Kirkwood, 2009). Activation of the renin-angiotensin-aldosterone system (RAAS), such as in heart failure or hypotension because of hemorrhage, can also prompt angiotensin II-driven arteriole vasoconstriction (Moser et al., 2009).
The most commonly administered vasopressor infusions, such as phenylephrine, epinephrine, vasopressin and norepinephrine will also exhibit action via mechanisms to include alpha-adrenergic stimulation and some beta effects (Darovic & Simonelli, 2002). In both stress response and vasopressor infusions, peripheral vasoconstriction is the primary mechanism for the blood shunting, which will then decrease peripheral pulse pressure
(Darovic & Simonelli, 2002). The decreased pulse pressure may impact the pulse oximetry plethysmographic waveform and possibly affect pulse oximetry ability to capture readings.
30
b. Body temperature and vasoconstriction. Hypothermia may promote peripheral vasoconstriction, which may decrease peripheral pulse amplitude (Agashe et al.,
2006). Tremper and colleagues (1985) studied the accuracy of oximetry in relation to core body temperature, heart rate, blood pressure and cardiac output using 383 data sets collected from 53 surgical ICU patients. The pulse oximeter read "low perfusion" (LP) in 9 of 15 patients determined to be hypothermic (T < 35 C°). In the 57 data sets in which the oximeter read "LP," 16% had a temperature of less than 35 C°. Researchers concluded that pulse oximeters may not be able to detect a signal adequate to reflect oximetry measurement in cases of hypothermia and abnormal systemic vascular resistance (Tremper et al., 1985).
In certain conditions, such as hypothermia, more central pulse oximetry monitoring sites may retain accuracy over peripheral sites. As described earlier in this chapter,
MacLeod, Cortinez and colleagues reported that digit oximeters detected hypoxic episodes more slowly than forehead and ear oximeters (Cortinez, MacLeod, Wright, Cameron, &
Moretti, 2003; MacLeod et al., 2003). Researchers exposed seven adult male volunteers to a cooling mattress for 30 minutes and delivered a hypoxic challenge with FiO2 of 11% for 3 minutes. Researchers found that the forehead pulse oximeter sensor registered desaturation faster (median 34 seconds, range 17 – 65 seconds) than ear (mean 58 seconds, range 27 - 80 seconds), concluding that monitoring pulse oximetry via fingers might be inappropriate during mild hypothermia (MacLeod et al., 2003).
c. Body temperature, pulse amplitude, and vasoconstriction.
Cortinez and colleagues (2003) compared the amplitude of waveforms from the forehead, earlobe and fingers of the seven adult male volunteers undergoing induced hypothermia.
Volunteers were asked to lie on a cooling mattress set at 14° C while they received a
31 combination of a 2-liter infusion of cooled intravenous fluid and a nitroglycerin infusion to stimulate vasodilation. Researchers measured plethysmograph signals, discovering that signal strength from the forehead was less affected by induced hypothermia when compared to that of the earlobe or fingers. Researchers also found that, although all three sites
(forehead, earlobe, finger) revealed a decrease in wave amplitude after induced hypothermia with the cooling blanket, the decrease in the earlobe was statistically significant (pulse amplitude at start 0.75 + 0.25; amplitude at end 0.51 + 0.22). The NellcorTM finger sensor amplitude at start was 44.71 + 35.69 (P < 0.05); amplitude at end 8.86 + 3.08. The
Masimo® finger sensor amplitude at start was 5.47 + 5.49 (P < 0.05) and amplitude at end was 1.09 + 0.60 (P < 0.05) (Cortinez et al., 2003). The stronger forehead pulse amplitude during induced hypothermia could support a hypothesis that facial circulation is less susceptible to hypothermia-induced vasoactive changes. If the buccal circulation responds in similar fashion, the buccal area could be a favorable site for pulse oximetry monitoring in cases of hypothermia.
Peripheral pulse oximetry sites, such as the finger, are more vulnerable to peripheral vasoconstriction driven by cold-induced alpha adrenergic response (Awad et al., 2001). In a study of 12 healthy volunteers, Awad and colleagues monitored changes in ear and finger
POP signals while immersing the participant's contralateral hand in ice water. The ear POP signal displayed minimal change, decreasing by 2% + 10% (p = .38). On the other hand, the finger POP signal amplitude decreased by 48% + 19% (p < 0.0001). Citing finger cutaneous vessel wall innervation by α-adrenoceptors, Awad and colleagues (2001) found that ear POP signals did not change as dramatically as finger POP signals under the effects of cold. Others have proposed that the buccal arteries may not be as susceptible to vasoconstriction response
32 to cold stimuli as in the periphery (Agashe et al., 2006; Blaylock et al., 2008).
d. Body temperature and blood flow velocity. Blood flow velocity is dynamic; however, blood flow transit time is related to distance from the heart and vessel length/diameter. Cranial SpO2 monitoring sites, such as the buccal area or earlobe, have shorter transit length from the left ventricle compared to fingers or toes. This could potentially offer more rapid detection of oxygen saturation changes. In 13 healthy participants, Hamber and colleagues (1999) compared hypoxemia detection time for the ear, hand and foot SpO2 sites. Researchers induced hypoxemia and found a mean lag time of 6 seconds in finger desaturation values when compared to the ear (p < 0.05). They also found that foot SpO2 changes lagged a mean of 63 seconds compared to the ear (p < 0.05). More recently, De Jong and colleagues (2011) found a mean lag time of 21 seconds in finger resaturation values when compared to the buccal area.
MacLeod and colleagues (2003) compared speed of saturation value changes in ear, finger and forehead SpO2 sites. They induced hypoxemia and mild hypothermia in 7 adult male volunteers. Hypoxemia, defined as an abnormally low concentration of oxygen in the blood, was induced by administering FiO2 of 11% to participants. Unfortunately, the researchers did not provide hypothermia parameters for their study.
MacLeod and colleagues (2003) referenced previous work that found that an ear oximeter reflected a change in arterial oxygen saturation within 6 seconds, while a finger sensor could take up to 24 seconds longer. They exposed participants to a cooling mattress for 30 minutes, then delivered a 3-minute hypoxic challenge with FiO2 of 11%. Researchers found that the forehead pulse oximeter sensor demonstrated desaturation faster (median 34 seconds, range 17 – 65 seconds) than ear (mean 58 seconds, range 27-80 seconds). The
33
NellcorTM OxiMax finger sensor demonstrated desaturation to 95% at a mean of 95 seconds
(range 72 - 180), while the Massimo finger sensor registered desaturation at mean 96 seconds
(range 83 - 189 seconds). Re-saturation occurred at a 14 mean of seconds (range 13 – 28 seconds) for the forehead sensor, mean of 14 seconds (range 11 - 29 seconds) for the ear sensor, mean of 64 seconds (range 50 - 86 seconds) for the NellcorTM finger sensor and mean of 76 seconds (range 46 - 89 seconds) for the Massimo finger sensor (MacLeod et al., 2003).
However, they did not compare pulse oximetry values to arterial blood oxygen saturation
(SaO2).
6. Saturation, Desaturation and Resaturation.
In addition to blood flow rates, other factors affect saturation and desaturation times at different pulse oximetry monitoring sites. Reynolds and colleagues (1993) studied desaturation and re-saturation time differences in 22 children. They compared changes in pulse oximetry values obtained in two groups (ages 12 years + 17 months) from either the combination of cheek, finger and toe or cheek tongue and toe. Researchers were interested in investigating time differences to achieve desaturation to 4% below baseline SpO2 and re- saturation time to 4% above the nadir. Desaturation time was measured beginning from the point at which apnea was induced by interruption of ventilation in anesthetized children with congenital heart disease. Researchers found that central pulse oximetry monitoring sites
(cheek and tongue) reflected desaturation times that were more than twice as fast as peripheral (finger and toe) sites. Desaturation as reflected by the cheek sensor occurred at 24
+ 12 seconds. Finger sensors reflected desaturation at 56 + 43 seconds and the toe sensor reflected desaturation at 58 + 28 seconds (Reynolds et al., 1993).
34
Hypoxemia may impose physiologic changes, such as peripheral vasoconstriction that could impact speed in recognizing desaturation. Severinghaus and Naifeh (1987) studied the effects of profound hypoxemia in adult volunteers comparing eight distinct brands of pulse oximeters. Sensors were applied to the fingers of healthy adults (18 to 64 years old). After inducing stepwise hypoxic plateaus of 40% to 70%, researchers compared ear and finger pulse oximetry values to SaO2. Researchers found longer sensor response times for induced desaturation than resaturation. Researchers attributed "increased blood flow when hypoxic blood reaches the tissue." The NellcorTM N-100 finger pulse oximetry sensor, an earlier generation of the sensor used for the present study, was one of the eight sensors investigated by Severinghaus and Naifeh, demonstrating a mean error of -6.6 +10.8 (Severinghaus &
Naifeh, 1987).
Re-saturation time was measured from the time of institution of mechanical ventilation and reflected a much faster re-saturation time at the cheek (12 + 8 seconds). Re- saturation was reflected at 40 + 36 seconds for the finger and 48 + 25 second at the toe sensors. Researchers also found no difference between cheek and tongue sites regarding desaturation (20 + 10 seconds versus 21 + 9 seconds) or re-saturation (10 + 6 seconds versus
7 + 3 seconds) (Reynolds et al., 1993). The benefits of potential earlier detection of hypoxemia as in with cheek and tongue sites cannot be understated.
As previously discussed, De Jong and colleagues (2011) found an average delay of
21.8 + 9.5 seconds in finger pulse oximetry resaturation to 80% compared to the buccal site.
The resaturation delay to 90% of finger SpO2 was also 18.4 + 11.4 seconds slower than the buccal site. For Bland-Altman analyses, the research team adjusted buccal and finger SpO2 data sampling times to 18 - 26 seconds and 8 seconds, respectively, prior to 70%, 80% and
35
90% induced hypoxemia event markers (De Jong et al., 2011). Analysis involved buccal
SpO2 data points recorded earlier than finger SpO2 data points. Therefore, simultaneous comparison of measurements, as Bland-Altman analysis requires, did not occur and data concurrency was lost (Hanneman & Katz, 2008). This asynchronous analysis may have artificially delayed, or elevated, the buccal SpO2 data curve and contributed to the positive bias reported by the research team.
Other factors affecting saturation/de-saturation times could include: the pulse oximeter device, patient ventilation/perfusion ratios, ventilation distribution and pulmonary diffusion capacity, vasoconstriction and vasodilation (Gruber, Kwiatkowski, Silverman,
Flaster, & Auerbach, 1995; Severinghaus & Naifeh, 1987). Severinghaus and Naifeh (1987) compared re-saturation and desaturation times, determining that ear pulse oximetry reflected re-saturation desaturation and re-saturation quicker than finger sensors.
Researchers also found that resaturation reflected more quickly than desaturation in both ear and finger sites. The NellcorTM oximeter and ear sensor reflected desaturation times of 9.6 + 4.3 seconds, compared to finger desaturation at 24.0 + 14.4 seconds. Resaturation times as measured by ear and finger pulse oximetry were 8.6 + 4.4 seconds and 19.4 + 6.6 seconds, respectively (Severinghaus & Naifeh, 1987). The more rapid resaturation may be due to compensatory increased cardiac output in response to hypoxemia. As hyperoxygenation occurred, the more rapid heart rate and increased cardiac output accelerated the delivery of re-oxygenated blood to the periphery (Gruber et al., 1995).
Without flow studies or investigating other potential moderators, it remains unclear whether the finger SpO2 resaturation lag times were directly caused by blood flow distance or by effects of physiologic, compensatory mechanisms to preserve core/central oxygenation such
36 as vasoconstriction and shunting.
D. Pulse Oximetry Technology.
As previously described in Chapter 1 and Figure 2, pulse oximetry derives the ratio of oxygenated hemoglobin to total hemoglobin. Pulse oximeters use spectrophotometry
(hemoglobin oxygen saturation) with optical plethysmography (pulsatile changes in the capillary bed (Kamat, 2002). Pulse oximeter sensors emit light at two wavelengths--infrared light (940 nm, absorbed by saturated oxyhemoglobin) and red light (660 nm, absorbed by reduced hemoglobin/deoxyhemoglobin) (Rutherford, 1993b; Sharma & Haber, 2002). The non-pulsatile components, such as bone, tissue and skin are theoretically eliminated, enabling the pulse oximeter to convert the differences of absorbed red and infrared light logarithmically into a pulse oximetry saturation percentage value (Nellcor, 2003b;
Rutherford, 1993; Volgyesi & Spahr-Schopfer, 1991).
Under normal circumstances, oxyhemoglobin more readily absorbs light at 940 nm and deoxyhemoglobin more readily absorbs light at 660 nm. The ratio of absorption is then converted algorithmically and converted into a pulse oximetry value (Sharma & Haber,
2002). Pulse oximetry applies the Beer-Lambert law, which states that if "hemoglobin concentration and light intensity are held constant, oxygen saturation becomes a logarithmic function" of the transmitted light intensity (Rutherford, 1993b). Therefore, pulse oximeters precisely measure the "red-to-infrared pulse Modulation Ratio," or the varying waveform, and calculate saturation (Nellcor, 2003b).
At present time, no pulse oximeter sensor exists for specific use in the buccal area.
Past studies of pulse oximetry in the buccal area either modified, disposable finger sensors, or used non-disposable, clip-style finger or earlobe sensors. The NellcorTM OxiMax-A, wrap
37 around finger sensor was found feasible for buccal use as the width of the buccal area and finger is about the same although tissue types differ.
Known research comparing buccal pulse oximetry bias with finger oximetry bias using like SpO2 sensors is limited to one known study (Landon et al., 1992; O'Leary et al.,
1992). O'Leary and colleagues studied 33 nonsmoking participants (23 under general anesthesia in the operating room and 10 ICU patients). Researchers modified a wrap-around style oximetry sensor designed for finger/toe application and applied it to the back of a malleable metal bar. O'Leary and colleagues found that buccal oximetry with a modified finger oximetry sensor agreed more closely with SaO2 in mechanically ventilated patients and anesthetized operating room (OR) patients from ages 10 – 82 (Landon et al., 1992;
O'Leary et al., 1992). O'Leary and colleagues recorded simultaneous buccal oximetry, finger oximetry and SaO2 measurements. They found buccal values in the OR patients were mean of 99.3% + 2.9% while buccal values in the ICU patients were 96.4% + 2.9. SaO2 in the OR patients was 99.5% + 0.7 and SaO2 in the ICU group was 96.6% + 3.5 (O'Leary et al., 1992).
In between-method statistical comparison, the OR group demonstrated buccal limits of agreement (LOA) with SaO2 of 2.0 to -2.9%. LOA conveys how well two measurement methods agree. The smaller the range, the better the agreement. O'Leary and colleagues found that the OR patient group demonstrated LOA for finger oximetry - SaO2 of 1.7 to –
2.3%. The ICU group demonstrated buccal - SaO2 LOA of 3.8 to – 2.5% and finger - SaO2
LOA of 3.0 to – 5.4%. Buccal values in both the OR and ICU groups tended to be higher than finger pulse oximetry values and correlated more closely with SaO2 (Landon et al.,
1992; O'Leary et al., 1992).
38
1. Transmittance Pulse Oximetry (TPO).
TPO is the most prevalent modality used in both stateside nursing units and military combat ICUs. TPO is normally monitored at the finger, toe or earlobe and requires a pulsating arterial bed between the light source (emitter) and light detector (Rutherford, 1993).
TPO oximetry values are determined by the amount of red and infrared light transmitted through the arterial bed and that reaches the light detector (Schutz, 2005).
2. Reflectance Pulse Oximetry (RPO).
In contrast to TPO, in reflectance pulse oximetry (RPO) the light detector measures the backscatter versus absorption of light (Wax, Rubin, & Neustein, 2009). RPO uses a light detector which sits adjacent to (not across an arterial bed) the emitter and is typically monitored on the forehead in adults. RPO was found to have acceptable agreement with arterial blood oxygen content using the forehead on adult vascular surgery patients, the cheek surface of severely burned adult patients and on the chests and upper backs of neonates
(Kugelman et al., 2004; Wax et al., 2009; Weesner, Shepherd, & Patel, 1998). Weesner and colleagues performed a retrospective chart review for 9 adult burn patients (23 to 56 years) who underwent 21 surgical excision and grafting. Although no specific data were provided, the burn team established that a buccal sensor placed on the cheek surface, when compared to data obtained from digit SpO2, revealed "little difference between these two measures of oxygen saturation" (Weesner et al., 1998). Wax and colleagues studied RPO on the foreheads of 20 patients undergoing vascular surgery. Researchers found limits of agreement, when compared to SaO2, of -3.3% to 3.4% (Wax et al., 2009). Kugelman and colleagues studied RPO and TPO on 18 infants. Researchers found RPO correlation coefficient of 0.93 and TPO correlation coefficient of 0.88 (p < 0.0001). Bias and standard
39 deviation compared to SaO2 were 0.09 + 4.5% and 1.26 + 5.9% for RPO and TPO, respectively (Kugelman et al., 2004).
3. Clinical Intermix of Transmittance and Reflectance Pulse Oximetry
Technologies.
When different oximetry sensor technologies are compared, this may potentially introduce variables that could interfere with findings. Anecdotal reports of clinicians applying TPO finger sensors onto the tongue, and used in an RPO fashion, is an example of incorrect application of oximetry technology. As previously discussed, TPO sensors use
"absorption of light as it passes through pulsatile blood" to determine oximetry values.
Accurate function depends on a capillary bed between light source and light receptor
(Sheridan et al., 1995). Conversely, RPO sensors read light backscatter and, therefore, do not need to be folded around a pulsating capillary bed (Kugelman et al., 2004).
Researchers from the U.S. Army Institute of Surgical Research (ISR), Fort Sam
Houston, TX, similarly studied a NellcorTM TPO device, applying it as if it were an RPO sensor. That is, researchers applied both light emitter and sensor sides of the device adjacent to each other on the external buccal surface of burn patients undergoing surgical excision and grafting procedures (Weesner et al., 1998). Weesner and colleagues did not provide specific data, but asserted finding "little difference between [the] two measures of oxygen saturation"
(Weesner et al., 1998). The need to study buccal oximetry further is clear.
Although RPO is used in some stateside operating room settings and in some portions of the deployed healthcare system, TPO remains the standard monitoring modality for pulse oximetry in stateside, deployed nursing units and during CCATT transport missions.
Therefore, the present study focused on TPO--the current, dominant technology in the critical
40 care clinical nursing setting.
E. Factors Affecting Accuracy and Precision of Arterial Blood Gas and Pulse
Oximetry Measurement.
The current study compared pulse oximetry values obtained from the buccal and finger sites with arterial blood gas analysis drawn and processed onsite by trained ICU personnel. The three host adult ICUs all employed the Siemens RapidPoint 400 arterial blood gas analyzer. These ABG analyzers remained fixed within the host unit in a climate- controlled environment. Calibration, controls, maintenance, ABG program management processes and machine operation in place will be detailed in Chapter 3.
Since inception in 1972, pulse oximetry has developed into a more refined technology
(Rutherford, 1993). The methods by which pulse oximeters cycle and average pulse oximetry data have improved device accuracy and precision. Pulse oximetry accuracy may still be affected in conditions of poor peripheral perfusion, motion artifact and ambient light interference (Kugelman et al., 2004). Accuracy may also be affected by darker skin phototype or incorrect sensor application. Investigating potential moderators may illustrate if a relationship exists with accurate pulse oximetry values. As previously mentioned, factors that may affect device accuracy and precision are presented using Burns and Groves' five categories of potential sources of error when retrieving physiologic measures. These categories are: subject (participant), environment, user, machine/device and interpretation
(Burns & Grove, 2001b).
1. Participant.
a. Biological factors. Biological sources of potentially impacting pulse oximetry accuracy and precision include hypoxemia, low MAP and pulse pressure,
41 vasoconstriction related to low body temperature or hypothermia, stress response or vasopressor infusions and skin phototype. These factors were explored in detail earlier in this chapter. Additionally, source of the arterial blood gas sample was recorded to explore potential impact of sampling sites at different distances from left ventricle.
b. Skin phototype. Researchers have suggested that pulse oximeters were historically calibrated on lighter-skinned individuals with potential impact on accuracy in individuals with darker skin phototype (Bickler, Feiner, & Severinghaus, 2005; Feiner,
Severinghaus, & Bickler, 2007). In 2005, Nellcor technicians conducted an accuracy test of their N-595 and N-600 pulse oximetry technology on three individuals categorized with light skin, seven with medium skin and two with dark skin phototypes (Nellcor, 2005). Technical staff drew 301 blood samples and summarized mean bias and precision as 0.9 and 1.9, respectively for the OxiMax N-595 pulse oximeter and the MAX-A finger sensor (Nellcor,
2005). However, data were not analyzed by skin phototype. Reports such as this serves as a basis for discussions and concern of potential impact of darker skin phototype on SpO2 accuracy.
In 2007, Feiner and colleagues tested the effects of skin phototype on pulse oximetry accuracy using six types of oximetry sensors on 36 adults (ages 29 + 5 years) (Feiner et al.,
2007). Ethnicities of participants included African American, Caucasian, Hispanic, Indian,
Filipino and Vietnamese. Researchers classified skin phototype as either light, dark or intermediate. Feiner and colleagues found that five of the six pulse oximeters overestimated
SaO2 in participants with hypoxia ranges below 80% (Feiner et al., 2007). The devices were more inaccurate at lower the saturation ranges in individuals with darker skin phototype. The two adhesive sensors mentioned earlier (NellcorTM N-595 and Nonin 9700) demonstrated a
42 bias of 4.5% to 4.5% in the saturation range of 60% to 70% and 2.4% to 3.6% bias in the saturation range of 70% to 80% in participants with dark skin phototype/pigment (Feiner et al., 2007). Researchers asserted that the "effective light path for red light through the finger will vary with skin pigmentation" as melanin and deoxyhemoglobin are the primary light absorbers in oximetry technology (Feiner et al., 2007). Therefore, the lower the pulse oximetry value in individuals with darker skin phototype, it is believed that pulse oximetry measurement becomes more inaccurate.
The literature is inconsistent in how skin pigment, skin color and skin phototypes are categorized. For example, Feiner and colleagues defined skin color groups by ethnicity versus true phototype (Feiner et al., 2007). As the literature is inconsistent on whether skin phototype affects pulse oximetry accuracy and precision, skin phototype was investigated in the current study.
c. Nail polish. Potential interference by nail polish on SpO2 measurements has been a common question for decades (Rutherford, 1993b). Almost thirty years ago, Cote' and colleagues studied the effects of finger nail polish on the pulse oximetry technology current of that time—the NellcorTM N-100 pulse oximeter (Cote', Goldstein,
Fuchsman, & Hoaglin, 1988). In a study of 14 adults (29 years + 9.2 years), researchers found that black, blue and green nail polish caused a statistically significant decrease (p <
0.05) in pulse oximetry readings when compared to the unpolished control values (Cote' et al., 1988).
Rubin investigated 31 colors of nail polish, finding that only one blue shade significantly affected SpO2 values, decreasing SpO2 values from 97% to 87%. Utilizing spectrophotometry, Rubin found that the blue nail polish absorbed most of the 660nm light
43 emitted by the pulse oximeter. Researchers hypothesized that the pulse oximeter light detector interpreted this as a reduction in SpO2 (Rubin, 1988).
In 2003, Chan found small, statistically significant decreases in Biox Ohmeda transmittance pulse oximetry readings in presence of black nail polish (SEM 95.1 + 0.46%) and brown nail polish (SEM 97.0 + 0.31%; Chan, M., Chan M.M. & Chan, E.D, 2003). In
2013, the same research team cited more recent studies that found that nail polish has minor, non-clinically significant effects on SpO2 values (Chan, E., Chan, M.M. & Chan, E.D.,
2013).
In 2007, Hinkelbein and colleagues studied nail polish effects on SpO2 values for 50 adult ICU patients. Using NellcorTM DS-100A finger sensors, researchers applied multiple colors of polish on finger nails of Caucasian participants. The research team found pulse oximetry variances as follows: black polish (∆S = +1.6 + 3.0%, P < 0.002), purple polish
(∆S - +1.2 + 2.6%, P < 0.004) and dark blue nail polish (∆S = +1.1 + 3.5%, P < 0.04)
(Hinkelbein, Genzwuerker, Sogl, & Fiedler, 2007). While this study revealed interference in oximetry values caused by darker nail polishes, the bias was determined not "clinically" significant.
In 2007, Rodden and colleagues studied effects of nail polish in 27 healthy, adult
(27 - 57 years) volunteers (Rodden, Spicer, Diaz, & Steyer, 2007). Researchers tested red, orange, yellow, green, blue, purple, pink, black, brown and white nail polish colors using
NellcorTM N-100 technology. The darker brown and blue nail polish colors demonstrated a statistically significant decrease in SpO2 readings (p < 0.05) compared to unpolished fingernails (Rodden et al., 2007). Two coats of brown polish decreased SpO2 from 98.33%
(SD 1.39) in the same unpolished fingernail to 97.63% (SD 1.69, P = 0.010). Blue nail
44 polish decreased SpO2 from 98.33 (SD 0.96) to 97.44% (SD 1.12, P < 0.001). Although statistically significant, researchers assert that that the SpO2 decreases were clinically insignificant as changes were less than 1% (Rodden et al., 2007).
In 2008, Yamamoto and colleagues investigated nine colors of nail polish and 210 pairs of SpO2 measurements on five participants. Researchers found no statistically significant decrease from SpO2 values without nail polish (M = 91.4% + 4.1%) and with nail polish (M = 91.2% + 3.5%). The mean difference was 0.2% with a 95% confidence interval ranging from -0.2% to 0.4% (L. G. Yamamoto, J. A. Yamamoto, J. B. Yamamoto, B. E.
Yamamoto, P. P. Yamamoto, 2008).
More recently in 2014, Villaflor and colleagues studied potential effects of polished,
TM thick acrylic gel nail art on Nellcor OxiMax N-600x SpO2 readings. Paired-samples t-test in this small study with 12 adult female participants was accomplished with the oximetry sensor positioned in a non-traditional radial-ulnar position and in the traditional dorsum- palmar (light emits across the nailbed) position. Villaflor found no statistically significant decrease in SpO2 without nail art (M = 99.43%, SD = 0.42) and with nail art (M = 99.20%,
SD = 0.44), p = 0.17 (Villaflor, Mogan, Lim, & Leng, 2013). As pulse oximetry technologies improve, so has ability to overcome influence of nail polish on device accuracy.
Therefore, nail polish was not included as a factor for the present study.
2. Environment.
Potential environmental influences for the present study may include items such as environmental temperature, barometric pressure and ambient light (Burns & Grove, 2001b).
a. Environmental temperature and barometric pressure. The manufacturer operation specifications for the OxiMax N-600x pulse oximeter indicate that
45 the equipment is designed to operate within the environmental temperature range of 41°F to
104° F and altitudes between -1,254 to 9,882 feet (Nellcor, 2010). Because the current study was performed in a controlled, ICU setting in a Southwestern city at approximately 750 feet above sea level, temperature and barometric pressure were not concerns for this study.
However, environmental temperature could be a consideration for patients in the deployed setting and should be an area for future research.
b. Ambient light. Anecdotal reports of ambient light interference on pulse oximetry accuracy tend to reference direct illumination of the sensor location, such as with an operating room spotlight (Costarino, Davis, & Keon, 1987; Hanowell, 1987, Trivedi et al., 1997). In 1987, Hanowell reported a case in which an operating room overhead fluorescent light was suspected of causing a false high pulse oximetry heart rate. Costarino and colleagues reported a case of a 6-day-old baby who, despite accidental extubation and resultant cyanosis, continued to display oxygen saturation of 100% (Costarino et al., 1987).
Researchers reproduced the "artifactual" pulse oximetry readings using the same type of operating room xenon arc lamp. Trivedi and colleagues studied effects of an operating room light on pulse oximetry accuracy on eight adults (ages 19 to 37 years) (Trivedi, Ghouri, Shah,
Lai, & Barker, 1997). Triveldi illuminated a 150-watt bulb directly over five pulse oximeters applied to participants' hands. Researchers found that the NellcorTM N-200 pulse oximeter, an older generation of the sensor used in the current study, demonstrated a difference (bias) of greater than 4% from SaO2 10.9% of the time (Trivedi et al., 1997). The same sensor exhibited a non-display, or zero, on the screen 17.9% of the time.
Flock and colleagues also studied the effects of ambient light on finger pulse oximetry on a convenience sample of 45 adult volunteers between the ages of 20 – 59, using
46 a NellcorTM N-200 pulse oximeter (Fluck, Schroeder, Frani, Kropf, & Engbretson, 2003).
Researchers compared finger pulse oximetry readings taken in complete darkness and with ambient light from quartz-halogen, infrared and incandescent light bulbs, finding no statistically significant difference in pulse oximetry readings. Flock and colleagues went on to explain that photoplethysmography, the technology employed in pulse oximetry, is dependent on the amount of light absorption change during pulsatile arterial blood flow. As the ambient light is present during both phases of the pulse, its effect on pulse oximetry readings becomes negligible (Fluck et al., 2003). Further, direct spotlight illumination of pulse oximeter monitoring site in the ICU environment is not the norm. Therefore, ambient light was not included as an area of focus in the present study.
3. User.
Improper arterial blood gas sample collection technique may alter test results. For the present study, ABG samples were obtained and analyzed by ICU nurses and Respiratory
Therapists (RTs) who had accomplished appropriate training and were deemed competent in the procedures by their own respective departments/units. Competency training included sampling from arterial lines to prevent accidental dilution of arterial blood sample or overexposure to room air. ICU and RT personnel collected ABG samples in pre-heparinized syringes and expelled any air bubbles in the sample prior to transport and analysis (Bucher,
2005; Darovic, 2002). The heparinized syringe prevented the samples from clotting.
Expelling excess air prevented potential alteration in PaO2 results (Bucher, 2005; Darovic,
2002). Processing of samples placed on ice could be delayed up to 30 minutes before results might be affected (Darovic, 2002). However, blood gas analysis machines were located within or immediately adjacent to the host units, or samples were carried immediately to the
47
Blood Gas Laboratory. All ABG samples were analyzed within 10 minutes or less and there were no delays in sample processing. The medical facility centralized lab coordinator performed bi-yearly ABG analyzer calibration checks which would render the devices unavailable for approximately 30 minutes. However, these calibration checks never were in session during any of the blood sampling processing for this study. Therefore, no delays in sample processing occurred due to machine calibration.
User errors when collecting physiologic data may include variations in supplies or pulse oximeter sensor placement (Burns & Grove, 2001b). Additionally, incorrect application of technology, such as using a TPO sensor on an RPO site, could also impact physiologic data retrieval. For this study, only one type of TPO disposable sensor was used and the technique for buccal sensor placement, finger sensor placement and finger selection was standardized.
Objective physiologic data for the present study was directly retrieved from the bedside monitors (heart rate, MAP), mechanical ventilator (FiO2, VE), thermometer device
(temperature), intravenous infusion pumps (vasopressor infusion type and rate), ICU flow sheet (vasopressor infusion dose) and observation of arterial line insertion site. There was concern of potential data inconsistency with subjective assessment of skin phototype
(pigment). Therefore, the principle investigator conducted all the skin phototype assessments using the skin phototype palette at Figure 4. As explained in more detail in Chapter 3, the phototype palette was created as no consistent skin color palette was identified in the literature. All data retrieval steps and procedures are discussed in detail in Chapter 3.
4. Machine/Device.
a. Finger sensors modified for buccal use. Disposable, finger pulse
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Figure 4. Skin Phototype Palette. Skin phototype assessments were performed using simple skin color palette.
49
b. Oximetry technology. Nellcor determined that the Max-N sensor oximetry sensors were modified by the principal investigator for the present study. The specific modification procedure will be detailed in chapter 3. The principal investigator performed the modifications using the same steps, but there were no ongoing, rigorous quality control measures for each modified device. This is a weakness of the present study. paired with the OxiMax N-600x oximeter generated a mean bias of 0.1, precision of 1.8 and
ARMS of 1.8 in the SaO2 ranges between 70% to 100% (Nellcor, 2005). Nellcor performance specifications list the saturation accuracy tolerance of the oximeter at 70 to 100% + 2
(Nellcor, 2010). Precision is described as "(1) the degree to which the same method produces the same results on repeated measurements" (Hanneman & Katz, 2008).
c. Arterial blood gas analyzers and blood gas sample processing. All host units accomplished arterial blood gas analyses using the same blood gas analyzer model.
These analyzers underwent a rigorous maintenance and calibration program that will be described in more detail. Instrumentation Laboratory's Gem 3500 arterial blood gas analyzers were located within the Surgical/Trauma Intensive Care Unit (ICU) and within the main Blood Gas Laboratory. Surgical/Trauma ICU ABG samples were analyzed on the nursing unit. ABG samples from the Medical and Transplant ICUs were analyzed in the
Blood Gas Laboratory. All analyzers were maintained by the host facility central laboratory with oversight of one chief laboratory director. The centralized laboratory abided by Clinical
Laboratory Improvement Amendments (CLIA), regulated by the Centers for Medicare and
Medicaid Services (CMS). CMS regulates all non-research laboratory testing performed on humans in the United States.
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d. Blood gas analyzer calibration. Potential data error sources may be related to problems with machine operation stability or calibration variance (Burns & Grove,
2001b). Chapter 3 will detail the procedure for accounting for the oximeter calibration and stability in accordance with manufacturer recommendations. Additional data accuracy interest items for the current study were sensor and oximeter compatibility and confirming comparison of like pulse oximetry technologies. Per the manufacturer, the NellcorTM
OxiMax-A disposable finger sensor is designed for use and compatible with the NellcorTM N-
600x oximeter (Nellcor, 2010).
Whenever a new ABG cartridge was inserted into the blood gas analyzer, the device performed a series of non-interruptible, self-calibration cycles lasting approximately three minutes each. Each analyzer would then perform additional, more intensive, self-calibration cycles. These cycles occurred more frequently with new cartridges and slowed in frequency as cartridge usage time elapsed. For example, the Gem 3500 performed an abbreviated calibration check every 2 minutes during the first 3 hours of cartridge life. Full calibration self-checks occurred every 20 minutes during the first 50 minutes of cartridge life. By the
40-hour timeframe of cartridge life, the full self-calibration cycles slowed to every 4 hours.
The Gem 3500 analyzer is designed to perform self-corrective actions in response to QC or self-calibration failures ("GEM Premier 3500 Operator's Manual," 2009). No self-corrective actions were identified in progress during attempts to perform ABG analyses for this study.
To confirm data points were correct and rule out processing drifting or shifts occurred, central laboratory personnel reviewed quality control (QC) calibration reports at least monthly or more frequent if concerns arose. The Blood Gas senior lab technician directed ABG analyzer calibration to be performed every 6 months on all Gem 3500 ABG
51 analyzers. Data was reviewed to confirm ABG results were within manufacturer's specifications.
The ABG analyzers each provided individual printouts of ABG results and the devices were also linked to the hospitals laboratory component of the inpatient electronic medical record. This minimized an additional potential for human error in transcription as
ABG results downloaded automatically into the electronic medical record.
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III. METHODS.
A. Study Design.
A quantitative, prospective, agreement analysis approach was used to determine the accuracy and precision of buccal pulse oximetry in critically ill, mechanically ventilated adults. Bland Altman analyses were investigated as both buccal and finger pulse oximetry measurements were compared to the gold standard arterial blood oxygen saturation (SaO2).
Correlations were also investigated between buccal and finger SpO2 bias and potential influencing variables related to oxygenation and oxygen transport (FiO2, PaO2, VE, HR,
MAP), regional blood flow (MAP, body temperature, presence of vasopressor infusions,
SpO2 buccal and finger waveform amplitude) and skin phototype. For the present study, nail polish and dyshemoglobins were not investigated as literature review revealed well-known interference on SpO2 values (Barker, Tremper, & Hyatt, 1989; Sharma & Haber, 2002).
Additionally, nail polish was not investigated as recent studies failed to suggest the influence on pulse oximetry values is important (Chan et al., 2013; Villaflor et al., 2013; Hinkelbein et al., 2007; Rodden et al., 2007).
B. Setting.
The current study was conducted in four large adult ICU complexes within a Level 1 trauma center in a large southwestern city. The nursing unit sub-specialty designations included adult surgical/trauma (16 beds), cardiac (10 beds), medical (10 beds) and cardiothoracic surgery/transplant ICUs (8 beds).
C. Sample.
The intended target population was adult, critically ill, mechanically-ventilated adults.
Participation was not randomized as recruitment was limited to patients with families or legal
53 proxies who were willing to provide informed consent to allow their loved one to participate in the study.
1. Inclusion/Exclusion Criteria.
a. General criteria. Study inclusion criteria were presence of mechanical ventilation and age > 18. Exclusion criteria included inaccessibility to the buccal corner of the mouth due to injury, extensive edema, dressings or inaccessibility to at least one finger. Due to concerns with presence of a non-sterile, modified pulse oximeter into the buccal corner of the mouth, participants were excluded if diagnosed as immune- compromised.
b. Dyshemoglobinemias. Consideration was given on whether all participants should be screened for dyshemoglobins and excluded if receiving medications or exposed to chemicals known to pose risk of development of acquired dyshemoglobinemias.
Dyshemoglobinemias are not part of the ABG analysis performed by the Instrumentation
Laboratory's Gem 3500 analyzer. More importantly, the list of medications that could potentially cause dyshemoglobinemias, is extensive. These medications include local anesthetics (benzocaine, articaine, prilocaine), nitrates and nitrites (nitroglycerine, nipride infusions), dapsone, trimethoprim, sulfur compounds and acetaminophen (Dahshan &
Donovan, 2006; Shihana et al., 2010; Sin & Shafran, 1996). It was determined that excluding participants in the ICU with at-risk medication profile was impractical when actual risk of dyshemoglobins is low. Participants were excluded from the present study if methemoglobinemia, carboxyhemoglobinemia and/or sulfhemoglobinemia were a differential or known medical diagnosis.
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2. Sample Size.
Bland-Altman analyses is not a statistical test with a p-value. It does not test a null hypothesis, but instead assesses agreement between two methods of measurement. However, as the present study investigated mean scores, power analysis was performed to determine target sample size. With alpha set at .05, beta of .80, expected moderate effect size and power at 90%, a minimum sample size of 100 was identified (Cohen, 1988). To ensure adequate sample size, oversampling to 125 was established. The sample size was then adjusted back to 100 after decreased variance in SaO2 and SpO2 was discovered. This decreased variance will be further explained in Chapter 4.
3. Recruitment.
Potential adult participants were identified by the principal investigator (PI) during unit walk-throughs. Participants were mechanically ventilated and their medical condition prevented alertness; therefore, information on study activities and informed consent was obtained by the principal investigator from legal proxy.
D. Instruments.
Nellcor provided the following equipment used during the present study: disposable
NellcorTM OxiMax-A finger sensors, two NellcorTM N-600x pulse oximeters, power cables, interface cables, and a NellcorTM SRC-Max Pulse Oximetry Functional Tester (calibration device for oximeters). All equipment and four new pulse oximetry sensor prototypes modified for buccal use were provided to the host facility's Nursing Administration
Department and Biomedical Equipment (BMET) and Repair Department. BMET personnel performed operation/function, calibration and safety inspections on the buccal SpO2 sensor prototypes and the two oximeters. All devices and components passed BMET operation,
55 function, and calibration inspections. BMET determined the buccal sensor prototypes and oximeters posed no hazard to patients and deemed their use safe for the present study.
1. Pulse Oximetry Sensors. The NellcorTM OxiMax-A is a disposable pulse oximeter sensor device approved by the Food and Drug Administration for use on the finger
(Nellcor, 2003b). For the current study, these finger sensors were modified slightly for use in the buccal area. The host facility's Infection Control Department was consulted beforehand to ensure appropriate infection control procedures were implemented during modifications to sensors to protect participants. A new, modified, disposable sensor was used for each participant.
The sensors were modified for buccal use using clean technique and in a location away from the study site. New tools designated solely for this purpose were used for the sensor modifications (The Joint Commission, 2013). To preserve sensor integrity, adhesive margins were cut off while avoiding wires and optical components (Figure 5). The removed adhesive was then used to anchor the pulse oximeter sensor to a new metal paperclip (see
Figure 6). A prototype was developed and tested by the PI before the start of the present study to confirm consistent function of the modified sensor. Four modified sensors were separately trialed for function. Values were found identical to finger pulse oximetry and waveforms were highly similar. Engineering quality control was limited to these preliminary tests and operator level standardization in creating the modified sensors. No additional engineering quality control measures were performed on the modified pulse oximeter sensors and this is a limitation of this study.
Prior to data collection, the PI modified and tested the preliminary sample prototype buccal sensors by inserted into the PI's right buccal corner of the mouth and connecting the
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Figure 5. Pulse Oximeter Sensor with Delineated Adhesive Removal Areas. NellcorTM OxiMax-A disposable finger sensor in preparation for modification for buccal area use. Lines indicate adhesive removal areas.
57
.
Figure 6. Pulse Oximeter Sensor Modified for Buccal Use. NellcorTM OxiMax-A disposable finger sensor was modified for buccal use.
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TM buccal sensors to the Nellcor N-600x pulse oximeter and (see Figure 7). The buccal SpO2 waveform and plethysmograph waveform amplitude was observed on the screen of the N-
600x pulse oximeter for three minutes. During this observation period, the buccal SpO2 waveform pulse rate was compared to the PI's actual radial pulse rate. The buccal sensor plethysmographic waveform configuration and waveform amplitude was examined for uniformity in pulse amplitude. The blip screen pulsations were confirmed to be visible, SpO2 pulse and radial pulse rates matched, and no error messages appeared on the pulse oximeter display. An acceptable plethysmograph waveform was defined as having a sharp, discernible up and downward stroke with minimal without frequent beat-to-beat variability.
2. NellcorTM N-600x Pulse Oximeters.
The PI conducted calibration checks of the two NellcorTM N-600x pulse oximeter monitors using the NellcorTM SRC-Max Pulse Oximetry Functional Tester on launch of recruitment and every month thereafter. This calibration check frequency on both pulse oximeter monitors exceeded the manufacturer recommendation for semiannual calibration testing. Interpretation of calibration checks revealed that the function of both oximeter monitors met manufacturer specifications throughout the study period.
TM The blip screen view of the Nellcor N-600x oximeter (Figure 8) provided the SpO2 value, the dynamic pulse amplitude indicator indicating pulse strength, or blip bar and pulse rate (Nellcor, 2010). A stronger pulse would illuminate more bars per pulse. Heart rate (HR) speed limited visualization time of peak blip bar pulsation. For the present study, the mean
HR was 92 beats per minute. At this heart rate, the blip bar flash occurred every .65 seconds.
Due to the rapid and dynamic nature, the short visualization period prevented ability to measure the precise number of blip bars at each pulsation peak. Instead, it was determined
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Figure 7. Modified Pulse Oximeter Sensor Placement in the Buccal Area. Modified NellcorTM OxiMax-A disposable finger sensor placement for buccal SpO2 measurement.
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Figure 8. Pulse Oximeter Blip Display Screen. NellcorTM N-600x pulse oximeter display view and blip display screen (Nellcor, 2008). Copyright © 2017 Medtronic. All rights reserved. Used with permission of Medtronic.
61 that buccal and finger pulse amplitude would be measured as blip bar height in quarter percentages (< 25%, 25%, 50%, 70%, or 100%). This subjective measurement of the blip bar height is a limitation of this study.
TM The Nellcor N-600x pulse oximeter averages pulse and SpO2 data every 6 – 7 seconds (Nellcor, 2003a). Therefore, buccal and finger SpO2 values were observed 3 seconds prior to ABG sampling, at the time of ABG sampling and 3 seconds after sampling.
It was determined that if the three individual values differed, the two matching SpO2 values per site would be recorded. However, values did not differ during the data capture time periods.
3. Arterial Lines and Non-Invasive Blood Pressure Cuffs.
The PI confirmed placement of the arterial line pressure transducer's air exchange port to the level of the participant's right atrium (Preuss & Lynn-McHale Wiegand, 2005).
The researcher then performed a dynamic response (square-wave) test on all arterial lines prior to recording MAP. It was determined that if the square wave test did not reveal 1 – 2 oscillations and brisk return to the baseline arterial waveform, nursing personnel would be notified and blood pressure obtained by noninvasive means (Ahrens, 1999; Shaffer, 2005).
When indicated, the PI zeroed the radial arterial line immediately prior to the ABG sampling. At times, arterial lines may be unreliable due to factors such as incorrect pressure transducer position, arterial pressure line kinks, bubbles in pressure line, and catheter position. If unable to troubleshoot the arterial line, a non-invasive blood pressure (NIBP) measurement would be obtained using the noninvasive automated blood pressure cuff provided by the host unit. If NIBP also failed, manual sphygmomanometer would be the backup method. Blood pressure measurement using NIBP was required for 10 participants.
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Blood pressure measurement using manual sphygmomanometer was not required.
4. Consent.
Institutional review board approval from both the university and the host level 1 trauma center was obtained prior to the start of the study (see Appendices B, C, D and E).
The PI obtained permission from the host facility to place posters in the ICU respiratory therapy staff break rooms to alert healthcare personnel of the study (see Appendix F).
Posters were also placed in ICU visitor waiting areas. Medical center personnel were educated about the study at staff and committee meetings. Upon identification of an intubated, mechanically ventilated patient during unit walk-throughs, legal proxies were approached by the PI and informed consent was obtained (see Appendix G and Appendix H).
Informed consent forms were developed in both Spanish and English. The Spanish informed consent document was reviewed by two registered nurses fluent in Spanish, who confirmed that the content and meaning paralleled the English consent document. The principal investigator, who was fluent in Spanish, explained the study to Spanish-speaking only legal proxies. If the legal proxies were not present in the participant's room, the PI established contact during daytime hours via telephone. Telephone consent was obtained with third party confirmation by asking legal proxies to repeat their understanding and consent via the telephone to a hospital staff member. Participants' legal proxies were informed they could stop participation in the study at any time and they were assured of study anonymity. To meet host facility requirement, the PI annotated the patient's participation and noted who provided informed consent in the electronic medical record. A copy of each signed consent form was filed in each participant's hard copy inpatient medical record.
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The principal investigator was available via telephone to ICU staff, respiratory therapy staff and participants' legal proxies throughout the data collection process.
Recruited participants were assigned a sequential identification number. No personal names were included in the data collection worksheet (see Appendix I). Completed data worksheets were stored in a secured, locked cabinet.
5. Data Collection Worksheet.
The data collection worksheet (Appendix I) was designed to record participant demographic data to include age, sex and race. Race categories for the data collection worksheet were adapted from the Office of Management and Budget (OMB) standards on race and ethnicity categories (U.S. Office of Management and Budget, 2000). These include four of the five minimal OMB race categories: White, Black/African American, American
Indian or Alaska Native, and Native Hawaiian/other Pacific Islander. The fifth category was labeled "Other" and captured the fifth OMB race category of Asian and intended to capture multiracial participants for purposes of this study. Specific ethnicities within the race categories were not investigated in the present study. Skin phototype, arterial oxygen saturation (SaO2) from arterial blood gas, primary diagnoses from medical records and buccal and finger pulse oximetry values were also collected. The worksheet also contained areas to record potential confounding factors that have not been well investigated in previous buccal oximetry studies. The intent of studying these factors was to address skin phototype, inspired oxygen content, participant general perfusion status and factors that could affect peripheral vasoconstriction. Specific factors recorded were: skin phototype, inspired oxygen content (FiO2), minute ventilation (VE), heart rate, MAP, body temperature and vasopressor infusions.
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6. Skin Phototype Palette.
No consistent color palette was identified during literature search. The Fitzpatrick
Skin Phototype Scale is frequently used to describe skin color; however, the original intent of the scale is to describe skin reaction to sun exposure versus skin color/tone (Eilers, et al.
2013; Everett, Budescu, & Sommers, 2012). Therefore, a simple skin phototype palette containing multiple brown shades was created using a face makeup palette (previously presented in Figure 4).
The skin phototype palette prototype was reviewed by three female nursing colleagues. Two palette copies were finalized and printed. One copy was laminated to preserve color and stored with all data collection equipment and out of prolonged exposure to sun or artificial light. The principal investigator assessed all skin phototypes using the laminated skin color palette placed against the cheek of the participant. Sitek, Everett and colleagues described using the inner arm to gauge skin color (Sitek et al., 2016; Everett et al., 2012). However, for the present study, the cheek was selected to assess impact of skin color at site of pulse oximeter sensor placement. Skin phototypes falling within the color palette were characterized as "brown." Skin phototype lighter than the palette was characterized as
"white." Skin phototype darker than the color palette was characterized as "black." Inter- rater reliability, accomplished by a host unit clinical nurse on 12% of the participants, resulted in 100% agreement.
7. Arterial Blood Gas Supplies and Samples.
The arterial blood gas samples were obtained by ICU staff members or Respiratory
Therapists (RTs) in ICUs. These individuals completed ABG sampling training and competency assessment, as confirmed by unit directors (for nurses), RT trainers (for RTs)
65 and the centralized lab coordinator. Sample volumes typically ranged between 1 to 2 ml of arterial blood and were collected in pre-packaged, pre-heparinized syringes.
8. Arterial Blood Gas Analyzers.
Three of the host nursing units did not house their own ABG analysis equipment; therefore, arterial blood samples drawn within these units were either immediately transported to an adjacent ICU or sent to the central blood gas laboratory for processing.
Each arterial blood sample was processed in the analyzer within less than 10 minutes after collection. All samples for this study, whether processed on the host unit with its own ABG analyzer or the central blood gas lab, were processed using the Gem 3500 ABG analyzer as described previously in Chapter II. Respiratory Therapists or Surgical ICU nursing personnel replaced analyzer calibration cartridges and printing paper on site. This facilitated continuous ABG analyzer function. The Gem 3500 blood gas analyzer has a digital screen that provided prompts to users, such as when the analyzer was performing or preparing to perform a regularly-scheduled self-calibration. No analyses were delayed during the present study due to equipment self-calibration. No errors in calibration occurred during the present study.
9. Body Temperature Measurement Sources.
Whenever possible, body temperature was recorded from a central source such as a pulmonary artery catheter, central venous line, indwelling urinary catheter or continuous rectal temperature sensor (n = 20). When no central temperature source was available, temperature was obtained via a peripheral source as established by the host ICU (n = 78).
These sources included tympanic, oral or axillary temperature routes and are described in detail in Chapter IV.
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E. Procedure.
1. Pulse Oximeter Set Up and Preparation.
Upon notice of an impending arterial blood gas draw ordered by the provider, the PI introduced herself and described the study procedures to any visitors at the bedside and nursing staff caring for the patient. The PI positioned the two pulse oximeters in the patient's room for concurrent visualization of data and to permit rapid capture of heart rate and MAP from the bedside cardiac monitor. The buccal oximeter was placed directly on top of the finger oximeter (Figure 9). The PI connected and positioned the interface cables to both pulse oximeters. The PI powered both pulse oximeter devices on while recording presence and dose of vasoactive infusions directly from intravenous pumps and asked the patient's nurse or medical technician to obtain the patient's temperature.
2. Pulse Oximetry Sensor Preparation and Placement.
The PI placed a new adult NellcorTM OxiMax-A finger sensor on the participant's index finger on the hand contralateral to the arterial line or NIBP (Jopling, Mannheimer, & Bebout,
2002). The contralateral hand was selected to prevent influence of partially blocked distal flow by the arterial catheter or blood pressure cuff to the fingers (Severinghaus & Spellman,
1990). If the index finger was not feasible, the middle finger was used. The light emitter side of the pulse oximetry sensor was placed on the nail bed side of the finger, and the light detector side of the sensor was placed on the finger pad side.
An OxiMax-A sensor modified for buccal use was placed in the participant's corner of the mouth by the principal investigator (PI). To prevent potential interference on pulse oximetry waveform due to pulsation of the endotracheal tube during mechanical breaths, the
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Figure 9. Buccal and Finger Pulse Oximeter Placement. NellcorTM OxiMax N-600x placement during data capture.
68 pulse oximetry sensor was opposite from the side of the endotracheal tube. Therefore, the sensor position was dependent on endotracheal tube placement and not to one consistent side.
Inconsistent buccal SpO2 sensor placement was a limitation for this study as potential individual variance in microvasculature and skin blood flow was unknown (Van-Buendia et al., 2010). The sensor was placed so that the wire end rested against the outside of the cheek
(Figure 7, previously presented). To prevent ambient light interference (Costarino et al.,
1987; Hanowell, 1987), direct spotlight illumination of the pulse oximeter sensor sites was avoided during data collection. Both the finger and buccal pulse oximeter sensors were attached to interface cables for individual NellcorTM OxiMax N-600x bedside pulse oximeters.
The PI observed buccal and finger SpO2 waveforms, confirming acceptable waveforms were present. As previously described, an acceptable plethysmograph waveform was defined as having a sharp, discernible up and downward stroke with minimal without frequent beat-to-beat variability. The PI confirmed no error messages appeared on the pulse oximeter display prior to proceeding.
3. Mean Arterial Pressure and Heart Rate.
The PI recorded heart rate and mean arterial pressure directly from the bedside monitor. For participants with arterial lines, the PI visually confirmed presence of the arterial line waveforms on the bedside monitor and compared pulse rate from the oximeters to the heart rate displayed on the bedside cardiac monitor. All heart rate incongruences between heart monitors and oximeters resolved within < 20 seconds. If no arterial line concerns with function were identified, the PI proceeded with recording HR and MAP. If arterial line
69 accuracy was questionable and PI or staff was unable to troubleshoot, the NIBP cuff was cycled immediately prior to ABG sampling for MAP values.
4. Pulse Oximetry Data.
Immediately prior to ABG sampling, the PI switched both pulse oximeter displays to the blip screen view and confirmed positive blip bar pulsation, continued pulse rate agreement. The PI recorded data collection time directly from the PI's cellular telephone, which was synchronized to National Institute of Standards and Technology time. At time of
ABG sampling, the PI recorded simultaneous buccal and finger oximetry values directly from the two NellcorTM OxiMax N-600x pulse oximeters. Although pulse oximeter data visualization was concurrent, buccal SpO2 value was always recorded before finger SpO2.
The order of recording pulse oximetry values was not counterbalanced and, therefore, is viewed as a limitation of this study. The PI observed both buccal and finger pulsation strength blip bars and recorded bar height percentage. Buccal pulse blip bar height was recorded before finger bar height.
5. Arterial Blood Gas Sampling and Processing.
Host unit staff collected ABG samples, expelled extra air and capped ABG syringes.
Samples from the Neurotrauma and Surgical /Trauma ICUs were processed on the analyzer located within the Surgical/Trauma ICU. Staff placed ABG blood samples from the
Transplant and Medical ICUs immediately on ice and transported samples to the Blood Gas
Laboratory for processing. The ABG analysis program using the Gem 3500 blood gas was described earlier in this chapter.
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6. Fraction of Inspired Oxygen, Minute Ventilation, Presence of
Vasopressor Infusions, Partial Pressure of Oxygen, and Arterial Blood Oxygen
Saturation.
The PI recorded FiO2 and VE directly from the mechanical ventilator display immediately after ABG sampling. When vasopressor infusions were present, infusion rate in mL/hour, and dosage were recorded by the PI directly from the IV pumps at the bedside.
The PI retrieved arterial blood gas PaO2 and SaO2 values directly from the electronic medical record or from a printout provided by the respiratory therapist.
7. Post Procedure.
After data were retrieved, the buccal and finger pulse oximeters were removed from the participant. To facilitate post-hoc sensor investigation if needed, each buccal and finger sensor was labeled with the participant's identification number, double bagged and stored in a sealed plastic bin in the PI's office. No buccal or finger sensors were re-used. Oximeters and interface cables were cleaned using host unit disinfectant wipes.
F. Analysis of Specific Aims.
1. Aims 1 and 2: Level of Agreement Between Buccal Pulse Oximetry and
Finger Pulse Oximetry with Arterial Blood Oxygen Saturation.
To examine the level of agreement between SpO2 with SaO2, Bland-Altman analyses were performed to determine bias and LOA between buccal pulse oximetry values and SaO2.
Bias is the overall mean difference in values between the two types of measurement methods.
LOA are the mean difference + 1.96 SD (Hanneman & Katz, 2008). Bland-Altman analysis is a means to determine differences and LOA, helping to gauge agreement between two sets of measurements or methods measuring the same item or characteristic (Bland & Altman,
71
1999; Myles, 2007). To compare LOA buccal SpO2 and finger SpO2 relative to SaO2, Bland-
Altman analyses for each relationship was accomplished and compared. A key element of
Bland-Altman analysis is that the two methods being compared use the same scale of measurement. A second key element is that data is captured simultaneously (Myles, 2007).
Buccal SpO2, finger SpO2 and SaO2 are measured on the same scale of measurement, defined as a percentage of hemoglobin saturated with oxygen. SpO2 - SaO2 bias is the difference between buccal or finger SpO2 (surrogate measures) and SaO2 (the gold standard).
Variances will be discussed in detail in Chapter IV. Equivalence margin in clinical trial literature is generally defined when a new or alternate treatment or measurement is
"expected to have equivalent efficacy compared to the standard treatment" and defined as the
"largest difference that can be judged as being clinically acceptable and should be smaller than differences observed in superiority trials of the active comparator" (Le Henanff,
Giraudeau, Baron, & Ravaud, 2006; Snapinn, 2000; Wiens, 2002). Bland-Altman analyses require that researchers establish clinically significant bias prior to analyses. An SpO2 - SaO2 bias of 4% was established a priori as the acceptable limit of variance and bias deemed clinically nonsignificant (Bland & Altman, 1999; De Jong et al., 2011).
2. Aim 3: Potential Influencing Variables.
Several correlation analyses were performed to investigate the relationship between buccal and finger SpO2 - SaO2 bias with: FiO2, VE, HR, MAP, body temperature. Pearson correlation may be used when both variables are interval or ratio level (Corty, 2007a).
Pearson correlation analyses were performed to investigate the relationship between buccal and finger SpO2 - SaO2 bias with: FiO2, VE, PaO2, HR, MAP, body temperature. Spearman
Rank Order Correlation (rho) analyses was performed to determine whether a correlation
72 existed between buccal and finger SpO2 - SaO2 bias and skin phototype. Spearman rho analyses may be used for nominal or ordinal data (Pallant, 2007a).
Both Pearson and Spearman rho correlation analyses provide the linear relationship, strength and direction of the relationship between two continuous variables. Correlation coefficients may range from -1, indicating a negative, or inverse correlation, to +1, indicating a positive correlation (Pallant, 2007b). A value of zero would indicate no correlation, while the absolute value indicates the strength of a relationship. A small correlation would be absolute values r = .10 to .29. A medium correlation would be absolute values r = .30 to .49.
A large correlation would be absolute values r = .50 to 1.0 (Pallant, 2007a).
Independent samples t-test analyses were performed to determine whether a difference in buccal SpO2 - SaO2 bias existed in participants as a function of vasopressor infusions. Independent samples t-test was also performed to determine whether a difference between buccal SpO2 - SaO2 bias existed in participants as a function of skin phototype. An independent samples t-test will indicate whether there is a statistically significant difference in mean scores for the two groups of conditions (Pallant, 2007c). The samples are called
"independent" because participants offer in each group mutual exclusivity (Corty, 2007b).
Independent samples t-test requires that the dependent variable is either interval or ratio- level.
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IV. RESULTS.
A. Findings.
In this chapter, the results will be presented in eight sections. Discussion regarding sample size and effects on sample size caused by limited variance are presented in the first and second sections. Information about participants' demographic and medical diagnoses is presented in the third and fourth sections. The fifth and sixth section present biomarkers, airway, ABG sampling and vital sign modalities used. The term "biomarker" (biological marker) as used in this document, is defined as an "objective, quantifiable characteristic of biological processes" that can be measured in the body (Strimbu & Tavel, 2010). Examples of biomarkers include lab tests (SaO2, PaO2) and vital signs such as pulse oximetry (SpO2), pulse (or HR), blood pressure (or MAP), and body temperature.
The remaining two sections will present findings specific to the research hypotheses.
Section seven presents findings in relation to oxygen transport and oxygenation. Section eight presents findings in relation to SpO2 accuracy and precision, which includes skin phototype and SpO2 sensors.
1. Sample Size.
The final sample was comprised of 98 adult inpatients (Table 2). Attrition on the initial recruitment efforts that will be described in detail later in this chapter. One hundred thirty-six (136) participants were recruited. Twenty-eight participants were extubated prior to opportunity to collect data and, therefore, were disenrolled from the present study. Three additional participants were disenrolled as, although they remained intubated or with tracheostomy, mechanical ventilation was removed. Arterial blood gas sampling was cancelled by the medical provider for two participants prior to data collection. For two other
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Table 2
Participants Recruited and Disenrolled (N = 98) Participants Recruited 136 enrolled 28 disenrolled - mechanical ventilation removed 2 disenrolled - ABG cancelled by medical provider prior to data collection 2 disenrolled - staff inadvertently obtained VBG, ABG cancelled 1 disenrolled - condition deteriorated prior to data collection 1 disenrolled - finger pulse oximetry unobtainable 1 disenrolled - brain death/apnea testing and mechanical ventilation removed Note. ABG = arterial blood gas; VBG = venous blood gas
75 participants, staff inadvertently obtained venous blood samples instead of arterial blood required for arterial blood gas analysis. These two cases resulted in cancellation altogether of arterial blood gas sampling. Data collection was also not initiated for one participant when his conditioned required resuscitative interventions by his medical treatment team.
Data collection was also halted on another participant when finger SpO2 was unobtainable.
Lastly, one participant was disenrolled due initiation of a brain death/apnea test and removal of assisted ventilations (zero/0 VE).
2. Preliminary Analyses.
Prior to analyses, data were examined for normal distribution properties using
Statistical Product and Service Solutions (SPSS) software versions 24.0 for Windows.
Interval and ratio variables were assessed for normality of distribution. Frequency descriptive statistics were performed on age, race, gender, skin phototype, medical diagnoses, nursing unit, and presence of vasopressor infusions. Frequency descriptive statistics were also performed for SaO2, buccal and finger SpO2, FiO2, VE, PaO2, HR, MAP, and body temperature, buccal and finger waveform amplitude, and buccal and finger SaO2 bias.
3. Oxygen Saturation Data Homogeneity.
At midpoint goal of data collection (n = 60), the PI noted sample SaO2 homogeneity consistently elevated. More than 75% of participants had SaO2 levels > 95% (Table 3). This homogeneity was likely related to physiologic stabilization and oxygenation improvement as time elapsed after recruitment and while awaiting informed consent. Data collection occurred several hours after participants were admitted to the medical center and intubated.
Thus, as therapeutic interventions preceded data collection, many patients showed improvement in oxygenation.
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Discussions on two separate occasions between the principal investigator, dissertation chair and a second dissertation committee member addressed the limited variance and potential limitations on study findings. After 87 participants were recruited, it was clear that the oxygen saturation elevations we were going to see would remain elevated. It seemed to be of little value to continue to enroll patients. Therefore, it was agreed that if variance did not expand once sample size reached 98, recruitment and data collection would cease. SaO2 variation did not expand and, therefore, data collection halted. SaO2 values ranged from 85 -
100% with a mean of 97.2% + 3.1 (Table 3). Eighty-two participants (84%) had SaO2 values
> 95%. The final sample size was 98 adults.
4. Participant Demographic Information.
Demographic data for study participants is at Table 4. The participant mean age was
52 years (SD = 16.4), with a range of 18 to 91 years. Sixty-three percent of participants (n =
62) were age 50 or older. Race was categorized as defined by the United States Office of
Management and Budget (OMB) Standards for the Classification of Federal Data on Race and Ethnicity (U.S. OMB, 2000). The sample was predominantly of white race (n = 96,
98%) with only two participants (2%) categorized as Black/African American. Fifty-six
(57.1%) participants had brown skin phototype, 40 (40.8%) had white skin phototype.
Seventy-two (73.5%) of the participants were male.
5. Medical Diagnoses.
Most of the data collection occurred in one of the three ICUs. Fifty-seven participants (58.2%) were in one of the three Surgical/Trauma ICUs. Nineteen participants
(19.4%) were in the Cardiac ICU, sixteen (16.3%) were in the Medical ICU, while only six
(6.1%) were in the Cardiothoracic/Transplant ICU. Trauma was the most frequent medical
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Table 3
Frequencies and Percentages of Arterial Blood Saturation and Pulse Oximetry Saturation (N = 98) SaO2 Value n (%) < 89% 2 (2%) 90 - 94% 14 (14.3%) 95 - 97% 21 (21.4%) 98 - 100% 61 (62.2%) Note. SaO2 = arterial blood oxygen saturation
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Table 4 Participant Demographic Characteristics, Medical Diagnoses, Nursing Unit and Presence of Vasopressors (N = 98) Characteristics n (%) Age 18 - 39 18 (18%) 40 – 49 18 (18%) 50 - 59 32 (33%) 60 - 69 19 (19%) > 70 11 (11%)
Race White 96 (98%) Black/African American 2 (2%)
Gender Male 72 (74%) Female 26 (27%)
Skin Phototype White 40 (41%) Brown 56 (57%) Black 2 (2%)
Medical Diagnosis Trauma 47 (48%) Cardiac 9 (9%) Cancer 5 (5%) Sepsis 1 (1%) Organ Failure 9 (9%) Multiple 5 (5%) Miscellaneous 22 (22%)
Nursing Unit Surgical Trauma 57 (58%) Medical 16 (16%) Cardiac 19 (19%) Cardiothoracic/Surg Transplant 6 (6%)
Vasopressors Yes 22 (22%) No 76 (78%) Note: The following abbreviations was used: Surg - Surgical
79 diagnosis (n = 47, 48%). Injuries included multiple fractures, pulmonary contusions, hemo/pneumothorax, spinal cord injury, aortic tear, splenic laceration, traumatic amputation, subdural hematoma, and intracranial hemorrhage. The primary mechanisms of injury were motor vehicle crashes, but also included gunshot wounds, stabbing, motorcycle crashes and diving and horseback riding accidents. Cardiac and organ failure-related medical diagnoses comprised 9.2% of participants (n = 9). Cardiac diagnoses included heart valve replacement, coronary artery bypass and graft surgery, cardiogenic shock, and chest pain. Five participants (5%) had medical diagnosis of cancer. Five participants (5%) had multiple concurrent medical diagnoses. Diagnoses including hypertension, subarachnoid hemorrhage, cancer with bacteremia, alcohol withdrawal, and anaphylaxis were consolidated into a category titled "miscellaneous." Only 22 (22.4%) of the participants had intravenous vasopressor medications infusing.
6. Airway, Arterial Blood Gas Sampling and Vital Signs Sources.
Table 5 lists types of artificial airway, ABG sampling method and modes for vital signs. Participant artificial airway types were either endotracheal tubes (96.9%) or tracheostomy tubes (3.1%). The most frequent method of ABG sampling was via existing arterial line (90.8%) over arterial puncture. Most (79.6%) of the arterial lines were in one of the participants' radial arteries.
When arterial puncture was performed for ABG sampling (9.2%), the brachial artery was accessed only once (1%) with the remainder of samples collected in the right radial artery (4.1%) or left radial artery (4.1%). The MAP was also primarily obtained (89.8%) from arterial lines. Remaining MAP values (10.2%) were obtained using non-invasive automated blood pressure cuffs. Body temperature measurement was performed 48% of the
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Table 5 Airway Types, Arterial Blood Gas Sampling Methods, Sites and Times, and Vital Signs Sources Characteristics n (%) Airway Type Oral ETT 95 (96.9%) Tracheostomy 3 (3.1%)
ABG Sampling Total via Arterial Line 89 (90.8%) Method Right Radial Arterial Line 30 (30.6%) Left Radial Arterial Line 48 (49%) Right Femoral Arterial Line 4 (4.1%) Left Femoral Arterial Line 4 (4.1%) Left Brachial Arterial Line 1 (1.0%) Right Pedal 2 (2%)
Total via Arterial Puncture 9 (9.2%) Right Radial Puncture 4 (4.1%) Left Radial Puncture 4 (4.1%) Left Brachial Puncture 1 (1%)
MAP Method Arterial Line 88 (89.8%) Automated BP Cuff 10 (10.2%)
Temperature Route Oral 10 (10.2%) Tympanic 47 (48%) Axillary 21 (21.4%) Bladder 9 (9.2%) Central Line 4 (4.1%) Esophageal 7 (7.1%) Note. ETT = endotracheal tube; ABG = arterial blood gas; MAP = mean arterial blood pressure; BP = blood pressure.
81 time using a tympanic thermometer device. The second most frequently used route for body temperature measurement was axillary (21.4%). Central temperature was obtained either via bladder, central venous line or esophageal methods. Equipment necessary and invasive lines and tubes to allow core, or central temperature measurement was present only 20.4% of the time during data collection.
7. Frequencies for Saturation, Biomarkers, FiO2, VE, and Waveform
Amplitude.
Section (a) will provide frequencies for saturation biomarkers: SaO2, buccal SpO2, finger SpO2, FiO2, and PaO2. Section (b) will provide frequencies for three VE, HR, MAP, body temperature, and buccal and finger pulse oximetry plethysmograph waveform amplitude.
a. Frequencies for SaO2, Buccal SpO2, Finger SpO2, FiO2, and PaO2.
Tables 6 and 7 presents frequencies for SaO2, buccal SpO2, finger SpO2, FiO2, PaO2,
PaO2. SaO2 ranged from 85 to 100%, with a mean of 97% + 3.1. Buccal SpO2 values ranged from 62 - 100% with a mean of 95% + 6.2. Buccal SpO2 values were between 95 -
100% for 66 participants (67%). Finger SpO2 values ranged from 82 - 100% with a mean of
97% + 3.4. Most participants (n = 79, 81%) had finger SpO2 values between 95 - 100%.
FiO2 ranged from 30 to 100% with mean 48% + 17. FiO2 was set at 40% for 54 participants
(55%). PaO2 values ranged from 51 to 324 with a mean of 125 + 56. Forty-three percent of participants had PaO2 ranges between 51 - 100.
b. Frequencies for VE, HR, MAP, body temperature, buccal waveform amplitude, and finger waveform amplitude. Tables 8 and 9 show frequencies for VE, HR, MAP, body temperature, and buccal and finger pulse oximetry waveform
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Table 6
Frequencies: Participant Oxygenation Biomarker Values/Ranges (N = 98) Biomarker Values n (%) SaO2 < 89% 2 (2%) 90 - 94% 14 (14%) 95 - 97% 21 (21%) 98 - 100% 61 (62%)
Buccal SpO2 < 79% 3 (3%) 80 - 89% 9 (9%) 90 - 94% 20 (20%) 95 - 100% 66 (67%)
Finger SpO2 < 89% 2 (2.0%) 90 - 94% 17 (17%) 95 - 100% 79 (81%)
FiO2 30% 8 (8%) 40% 54 (55%) 41 - 60% 25 (26%) 61 - 80% 5 (5%) 81 - 100% 6 (6%)
PaO2 51 – 100 42 (43%) 101 – 150 33 (34%) 151 – 200 13 (13%) 201 – 250 5 (5%) 251 - 300 1 (1%) 301 – 350 4 (4%)
Note. SaO2 = arterial blood oxygen saturation; SpO2 = pulse oxygen saturation; FiO2 = fraction of inspired oxygen; PaO2 = partial pressure of oxygen
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Table 7
Descriptives: Oxygen/Saturation Values (N = 98) Min Max Mean SD Skew Kurtosis SaO2 85 100 97 3.1 -1.59 2.70 Buccal SpO2 62 100 95 6.2 -2.52 9.09 Finger SpO2 82 100 97 3.4 -1.74 3.92 Buccal SpO2 Bias -36 10 -1.93 6.0 -2.9 12.59 Finger SpO2 Bias -8 10 .09 2.15 .38 6.21 FiO2 30 100 48 17.0 1.98 3.49 PaO2 51 324 124.7 56.4 1.4 2.39 Note. SaO2 = arterial blood oxygen saturation; SpO2 = pulse oxygen saturation; FiO2 = fraction of inspired oxygen; PaO2 = partial pressure of oxygen in arterial blood.
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Table 8
Frequencies: Participant Vital Signs and Pulse Oximetry Waveform Amplitude (N = 98) Biomarker Values n (%) Minute Ventilation 4.00 - 7.00 L/min 23 (24%) 7.01 - 10.00 L/min 43 (44%) 10.01 - 13.00 L/min 18 (18%) 13.01 - 16.00 L/min 7 (7%) 16.01 - 19.00 L/min 6 (6%) > 19.00 L/min 1 (1%)
Heart Rate < 50 1 (1%) 51 - 70 16 (16%) 71 - 90 32 (33%) 91 - 110 30 (31%) 111 - 130 15 (15%) 131 - 150 4 (4%)
Mean Arterial Pressure 53 – 59 8 (8%) 60 – 69 18 (18%) 70 - 79 16 (16%) 80 - 89 24 (25%) 90 - 99 22 (22%) 100 – 120 10 (10%)
Body Temperature (F) 90.6 - 94.9 2 (2%) 95.0 - 96.9 4 (4%) 97.0 - 99.9 72 (74%) 100.0 - 101.9 17 (17%) 102.0 - 103.5 3 (3%)
Buccal Waveform Amplitude < 25% 3 (3%) 25% 20 (20%) 50% 33 (34%) 75% 31 (32%) 100% 11 (11%)
Finger Waveform Amplitude < 25% 3 (3%) 25% 12 (12%) 50% 12 (12%) 75% 16 (16%) 100% 55 (56%) Note. The following abbreviations were used: HR = heart rate; MAP = mean arterial blood pressure; Body Temp = body temperature; F = Fahrenheit; Wave Ampl = waveform amplitude
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Table 9
Descriptives: Minute Ventilation, Heart Rate, Mean Arterial Pressure, and Body Temperature (N = 98) Min Max Mean SD Skew Kurtosis VE 4.4 20.3 9.6 3.4 .99 .47 Heart rate 46 147 92 20.5 .37 .03 MAP 53 120 82 15.3 .16 -.48 Body Temp 90.6 103.5 98.9 1.5 -1.32 9.15 Note. VE = minute ventilation; HR = heart rate; MAP = mean arterial blood pressure; Temp = body temperature.
86 amplitude. HR ranged from 46 to 147 beats per minute (BPM), with the mean HR at 92 +
20.5. Thirty-two participants (33%) had HR between 71 - 90 BPM. The next largest group
(n = 30, 31%) had HR between 91 - 110. MAP ranged from 53 - 120 mm/Hg, with a mean
MAP of 82 + 15. Seventy-three (73%) of participants in the present study had MAP values >
70 mm/Hg. Twenty-four participants (25%) had MAP values between 80 - 89 mm/Hg. The next largest grouping of participants (n = 22, 22%) had MAP values between 90 - 99 mm/Hg.
Body temperature ranged from 90.6 to 103.5 Fahrenheit, with mean temperature at 98.9 +
1.5. The majority (n = 72, 74%) of participants had a body temperature between 97.0 - 99.9.
Only 6% of participants had body temperature of < 97.0 degrees. VE ranged from 4.4 to 20.3 liters per minute (L/min), with the mean at 9.6 + 3.4. VE ranged between 7.01 to 10 L/min for 43 participants (44%).
e. Pulse oximetry waveform amplitude. Buccal and finger pulse amplitude assessment technique was described earlier in Chapter III. Waveform pulse amplitude was categorized in percentages of the blip bar that was visualized with each buccal and finger pulsation, respectively. Thirty-four (34%) of participants had pulsation amplitude of 50% on the buccal SpO2 blip bar screen. Thirty-one (31%) of participants had blip bar amplitude of 75%, while only 11 (11%) had blip bar amplitude of 100%. For finger SpO2, 12
(12%) of participants had blip bar amplitude of 50%, 16 (16%) of participants had blip bar amplitude of 75%, and most participants (n = 55, 56%) had blip bar amplitude of 100%.
8. Data Screening: Oxygen Transport and Oxygenation Variables.
a. Factors in relation to SaO2. Preliminary analyses were performed to explore data distribution and relationships. Review of histogram for SaO2 revealed left- sloping of data, which is desirable for SaO2 values (Figure 10). This left-sloping is expected
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Figure 10. Histogram: Arterial Blood Oxygen Saturation.
88 as the mean value of 97.16 was less than the median of 98. The data was not expected to be normally distributed as the SaO2 data has a maximum value of 100%.
b. Factors in relation to buccal SpO2. Review of the buccal SpO2 histogram also revealed left sloping (Figure 11). Mean value of 95.23 was less than the median of 97.
Histogram also revealed a wider range in buccal SpO2 values (62 – 100%). Review of buccal
SpO2- - SaO2 bias box plot revealed six outliers: cases 9, 15, 18, 29, 32, and 64 (Figure 12).
Two of these cases (29 and 32) were extreme points. As data entry error was ruled out, outliers were retained in the data set. The intraquartile range is illustrated by box length and represents 50 percent of the cases. Extreme outliers are defined as those appearing more than three intraquartile lengths from the edge of the intraquartile box. Findings from
Kolmogorov-Smirnov and Shapiro-Wilk tests of data distribution normality for buccal SpO2
- SaO2 bias revealed sig. < .01, suggesting violation of assumption of normality.
Scatterplot diagram was constructed to review distribution of buccal SpO2 - SaO2 bias with SaO2 (Figure 13). Scatterplots were also constructed to review distribution of buccal
SpO2 - SaO2 bias with SaO2 and independent variables FiO2, VE, PaO2, HR, MAP, and body temp data points (Figures 14-20). Review of these scatterplots demonstrated five cases (9,
18, 29, 32, and 64) as falling outside + 2 standard deviations of the absolute value mean buccal SpO2 - SaO2 bias. Buccal SpO2 over-predicted SaO2 for case 9 and under-predicted
SaO2 for the remaining four cases (18, 29, 32, 64). None of the data clusters were linear.
Therefore, Spearman rho analyses were accomplished to investigate correlation.
c. Factors in relation to finger SpO2. Findings from Kolmogorov-
Smirnov and Shapiro-Wilk tests of data distribution normality for finger SpO2 - SaO2 bias revealed sig. <.01, suggesting violation of assumption of normality. Review of finger SpO2
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Figure 11. Histogram: Buccal Pulse Oximetry.
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Figure 12. Boxplot: Buccal Pulse Oximetry – Arterial Blood Oxygen Saturation Bias.
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Figure 13. Scatterplot: Buccal Pulse Oximetry with Arterial Blood Oxygen Saturation.
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Figure 14. Scatterplot: Buccal Pulse Oximetry – Arterial Blood Oxygen Saturation Bias with Arterial Blood Oxygen Saturation. +1.96 SD = 9.87; -1.96 SD = -13.73; SpO2 = pulse oxygen saturation; SaO2 = arterial blood oxygen saturation.
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Figure 15. Scatterplot: Buccal Pulse Oximetry - Arterial Oxygen Saturation Bias with Fraction of Inspired Oxygen. Scatterplot of fraction of inspired oxygen and buccal pulse oximetry - arterial blood saturation bias (N = 98). +1.96 SD = 9.87; -1.96 SD = -13.73; SpO2 = pulse oxygen saturation; SaO2 = arterial blood oxygen saturation.
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Figure 16. Scatterplot: Buccal Pulse Oximetry - Arterial Oxygen Saturation Bias with Minute Ventilation. Scatterplot of minute ventilation and buccal pulse oximetry - arterial blood saturation bias (N = 98). SpO2 = pulse oxygen saturation; +1.96 SD = 9.87; -1.96 SD = -13.73; SaO2 = arterial blood oxygen saturation.
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Figure 17. Scatterplot: Buccal Pulse Oximetry - Arterial Oxygen Saturation Bias with Partial Pressure of Oxygen Dissolved in Arterial Blood. Scatterplot of PaO2 and buccal pulse oximetry - arterial blood saturation bias (N = 98). +1.96 SD = 9.87; -1.96 SD = -13.73; SpO2 = pulse oxygen saturation; SaO2 = arterial blood oxygen saturation; PaO2 = partial pressure of oxygen dissolved in arterial blood.
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Figure 18. Scatterplot: Buccal Pulse Oximetry - Arterial Oxygen Saturation Bias with Heart Rate. Scatterplot of heart rate and buccal pulse oximetry - arterial blood saturation bias (N = 98). +1.96 SD = 9.87; -1.96 SD = -13.73; SpO2 = pulse oxygen saturation; SaO2 = arterial blood oxygen saturation.
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Figure 19. Scatterplot: Buccal Pulse Oximetry - Arterial Oxygen Saturation Bias with Mean Arterial Pressure. Scatterplot of mean arterial pressure and buccal pulse oximetry - arterial blood saturation bias (N = 98). +1.96 SD = 9.87; -1.96 SD = -13.73; SpO2 = pulse oxygen saturation; SaO2 = arterial blood oxygen saturation.
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Figure 20. Scatterplot: Buccal Pulse Oximetry - Arterial Oxygen Saturation Bias with Body Temperature. Scatterplot of body temperature and buccal pulse oximetry - arterial blood saturation bias (N = 98). +1.96 SD = 9.87; -1.96 SD = -13.73; SpO2 = pulse oxygen saturation; SaO2 = arterial blood oxygen saturation.
99 histogram revealed left-sloping (Figure 21). Finger SpO2 histogram also revealed tapering to the right, similar to SaO2 scatterplot (previously presented at Figure 10) with mean 97.26 and median of 98. Finger SpO2 - SaO2 bias boxplot demonstrated five extreme points: cases 25,
29, 48, 69, 76 (Figure 22), two of which were extreme points (cases 29 and 69). Data error was ruled out; therefore, outliers were retained in the data set.
Scatterplot was constructed to review distribution of finger buccal SpO2 with SaO2
(Figure 23). Scatterplots were also constructed to review distribution of finger SpO2 - SaO2 bias with FiO2, VE, PaO2, HR, MAP, and body temperature (Figures 24 - 29). Like scatterplots for buccal SpO2 - SaO2 bias, none of the data clusters were linear. Additionally, scatterplots revealed five cases (25, 29, 48, 69, and 76) as falling outside + 2 standard deviations of the absolute value mean buccal SpO2 - SaO2 bias. Finger SpO2 over-predicted
SaO2 for cases 48 and 69 and under-predicted SaO2 for cases 25, 29 and 76. These outliers were examined and found to be legitimate cases, sampled correctly and without identifiable data errors. Therefore, no data transformation or removal of outlier cases was performed.
Case number 29 was also found to be an outlier in preliminary analyses for buccal SpO2 -
SaO2 bias. Spearman rho analyses were then accomplished to investigate correlations regarding finger SpO2- - SaO2 bias.
9. Buccal and Finger Pulse Oximetry Saturation.
Buccal SpO2 - SaO2 bias ranged from -36 to +10 with a mean bias of -1.95. As discussed in Chapter III, a bias of + 4% was established a priori as the clinically acceptable limit of precision (Bland & Altman, 2007; De Jong et al., 2011). Buccal SpO2 - SaO2 bias was greater than + 4% in 23 (23%) participants. Seventy-five (77%) of participants had a buccal SpO2 - SaO2 bias + 4%. Buccal SpO2 was equal to SaO2 for 20 (20%) participants.
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Figure 21. Histogram: Finger Pulse Oximetry.
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Figure 22. Boxplot: Finger Pulse Oximetry – Arterial Blood Oxygen Saturation Bias.
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Figure 23. Scatterplot: Finger Pulse Oximetry with Arterial Blood Oxygen Saturation. Scatterplot of fraction of inspired oxygen and finger pulse oximetry - arterial blood saturation bias (N = 98).
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Figure 24. Scatterplot: Finger Pulse Oximetry - Arterial Oxygen Saturation Bias with Fraction of Inspired Oxygen. Scatterplot of fraction of inspired oxygen and finger pulse oximetry - arterial blood saturation bias (N = 98). +1.96 SD = 4.30; -1.96 SD = -4.11; SpO2 = pulse oxygen saturation; SaO2 = arterial blood oxygen saturation.
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Figure 25. Scatterplot: Finger Pulse Oximetry - Arterial Oxygen Saturation Bias with Partial Pressure of Oxygen Dissolved in Arterial Blood. Scatterplot of partial pressure of oxygen and finger pulse oximetry - arterial blood saturation bias (N = 98). +1.96 SD = 4.30; -1.96 SD = -4.11; SpO2 = pulse oxygen saturation; SaO2 = arterial blood oxygen saturation; PaO2 = partial pressure of oxygen dissolved in arterial blood.
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Figure 26. Scatterplot: Finger Pulse Oximetry - Arterial Oxygen Saturation Bias with Minute Ventilation. Scatterplot of minute ventilation and finger pulse oximetry – arterial blood saturation bias (N = 98). +1.96 SD = 4.30; -1.96 SD = -4.11; SpO2 = pulse oxygen saturation; SaO2 = arterial blood oxygen saturation.
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Figure 27. Scatterplot: Finger Pulse Oximetry - Arterial Oxygen Saturation Bias with Heart Rate Scatterplot of heart rate and finger pulse oximetry - arterial blood saturation bias (N = 98). +1.96 SD = 4.30; -1.96 SD = -4.11; SpO2 = pulse oxygen saturation; SaO2 = arterial blood oxygen saturation.
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Figure 28. Scatterplot: Finger Pulse Oximetry - Arterial Oxygen Saturation with Mean Arterial Pressure. Scatterplot of mean arterial pressure and finger pulse oximetry - arterial blood saturation bias (N = 98). +1.96 SD = 4.30; -1.96 SD = -4.11; SpO2 = pulse oxygen saturation; SaO2 = arterial blood oxygen saturation.
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Figure 29. Scatterplot: Finger Pulse Oximetry - Arterial Oxygen Saturation with Body Temperature. Scatterplot of body temperature and finger pulse oximetry - arterial blood saturation bias (N = 98). +1.96 SD = 4.30; -1.96 SD = -4.11; SpO2 = pulse oxygen saturation; SaO2 = arterial blood oxygen saturation.
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Buccal SpO2 over-predicted SaO2 for 33 (34%) participants. Buccal SpO2 under-predicted
SaO2 for 45 (46%) participants.
Finger SpO2 - SaO2 bias ranged from -8 to +10, with a mean of .11. Ninety-three
(95%) participants had a finger SpO2 - SaO2 bias within + 4. Finger SpO2 was equal to SaO2 for 29 (30%) participants. Finger SpO2 over-predicted SaO2 for 38 (39%) participants.
Finger SpO2 under-predicted SaO2 for 31 (32%) participants.
10. Aim 1: Buccal Pulse Oximetry Accuracy and Precision.
Aim 1 was to establish LOA between buccal SpO2 and SaO2 in mechanically ventilated adults. To determine buccal SpO2 accuracy and precision in estimating SaO2,
Bland-Altman analysis was performed. Bland-Altman analysis determines bias and LOA between simultaneous buccal SpO2 and SaO2 values. Review of buccal SpO2 - SaO2 scatterplot revealed mean bias of -1.9 + 6.0 (Figure 30, Table 10). There was tight clustering at saturation values > 95%. However, 95% LOA (+1.96 SD) were widely dispersed, ranging from -13.8 to 9.9.
11. Aim 2: Comparison of Buccal and Finger Pulse Oximetry Accuracy and
Precision.
Aim 2 was to compare the LOA for buccal SpO2 and finger SpO2 relative to SaO2 in mechanically ventilated adults. To determine whether buccal SpO2 was as precise as finger
SpO2 in estimating SaO2, Bland-Altman analysis of finger SpO2 and SaO2 was performed
(Figure 31) and compared to Bland Altman analysis of buccal SpO2 and SaO2. SaO2 bias scatterplot also revealed tighter clustering around mean bias at saturation values >92%.
There were 93 cases in which finger SpO2 - SaO2 bias was within a priori + 4% compared to only 75 cases where buccal SpO2 - SaO2 bias was within a priori + 4%.
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+1.96 SD = 9.87
Bias = -1.93
-1.96 SD = -13.73
Figure 30. Bland-Altman Plot: Buccal Pulse Oximetry and Arterial Blood Oxygen Saturation. Limits of agreement (+ 1.96 SD) between buccal pulse oximetry arterial blood oxygen saturation bias and mean buccal pulse oximetry and arterial blood oxygen saturation. SpO2 = pulse oximetry; SaO2 = arterial blood oxygen.
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Table 10
Bland-Altman Bias and Limits of Agreement: Buccal and Finger Pulse Oxygen Saturation, and Arterial Blood Oxygen Saturation (N = 98) Bias SD of Bias 95% LOA of the CI Buccal SpO2 - SaO2 -1.93 6.0 -13.8 to 9.9 Finger SpO2 - SaO2 0.09 2.2 -4.1 to 4.3 Note. SpO2 = pulse oxygen saturation; SaO2 = arterial blood oxygen saturation.
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Figure 31. Bland-Altman Plot: Finger Pulse Oximetry and Arterial Blood Oxygen Saturation. Limits of agreement (+ 1.96 SD) between finger pulse oximetry arterial blood oxygen saturation bias and mean finger pulse oximetry and arterial blood oxygen saturation. SpO2 = pulse oximetry; SaO2 = arterial blood oxygen.
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12. Aim 3: Buccal and Finger Pulse Oximetry and Potential Influencing
Variables.
a. Pearson Correlation: buccal and finger SpO2, FiO2, VE, PaO2,
HR, MAP, and body temperature. Pearson Correlation analyses were accomplished to determine whether buccal and finger SpO2 - SaO2 bias correlated with: FiO2, VE, PaO2, HR,
MAP, and body temperature (Table 11). There was a medium, correlation between more positive buccal SpO2 - SaO2 bias and finger SpO2 - SaO2 bias (r = .42, p < .01). Participants with more positive buccal SpO2 - SaO2 bias also had more positive finger SpO2 - SaO2 bias.
There was a moderate, positive, correlation between FiO2 values and PaO2 values (r = .28, p
< .01). There was a moderate, positive correlation between VE and heart rate (r = .20, p < .05). Participants with higher heart rates also had higher minute ventilation volumes.
There were no correlations identified with body temperature.
There was a moderate, positive, correlation between higher MAP and more positive finger SpO2 - SaO2 bias (r = .29, p < .01). Participants with higher MAP had finger SpO2 values that over-predicted SaO2. Because of this finding between MAP and finger SpO2 -
SaO2 bias, the PI further explored whether buccal or finger SpO2 – SaO2 bias correlated with a state of anemia. Therefore, Pearson Correlation analyses were performed between buccal
SpO2 – SaO2 bias, finger SpO2 – SaO2 bias and hemoglobin and hematocrit values (Table
12). Hemoglobin values ranged from 6.2 – 16.5 g/dl (mean 9.6). Hematocrit values ranged from 20 – 52.3% (mean 30.2). There were no correlations between either buccal or finger
SpO2 - SaO2 bias with hemoglobin or hematocrit. This finding was in line with Lee,
Tremper, and Barker (1991).
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Table 11
Pearson Correlation: Arterial Blood Oxygen Saturation, Buccal and Finger SpO2 - SaO2 Bias with Fraction of Inspired Oxygen, Minute Ventilation, Partial Pressure of Oxygen Dissolved in Arterial Blood, Heart Rate, Mean Arterial Pressure, and Body Temperature (N=98) Correlation 1 2 3 4 5 6 7 8 1. BBias .42** .08 -.12 -.06 -.15 .12 .02 2. FBias - -.08 -.10 -.02 -.04 .29** -.02 3. FiO2 - - .02 .28** .02 -.05 -.10 4. VE - - - -.15 .20* .17 .19 5. PaO2 - - - - -.07 .18 -.10 6. HR - - - - - .004 .11 7. MAP ------.12 8. Temp ------Note. SpO2 = pulse oxygen saturation; SaO2 = arterial blood oxygen saturation; BBias = buccal pulse oxygen saturation - arterial blood oxygen saturation bias; FBias = finger pulse oxygen saturation - arterial blood oxygen saturation bias; FiO2 = fraction of inspired oxygen; VE = minute ventilation; PaO2 = partial pressure of oxygen dissolved in arterial blood; HR = heart rate; MAP = mean arterial pressure; Temp = body temperature. *p < .05 **p < .01
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Table 12
Pearson Correlation: Buccal and Finger Pulse Oximetry – Arterial Blood Oxygen Saturation Bias with Hemoglobin and Hematocrit (N=98) Correlation 1 2 3 4 1. BBias .42** .09 .08 2. FBias - -.14 -.13 3. hemoglobin - - .96** 4. hematocrit - - - Note. SpO2 = pulse oxygen saturation; SaO2 = arterial blood oxygen saturation; BBias = buccal pulse oxygen saturation – arterial blood oxygen saturation bias; FBias = finger pulse oxygen saturation - arterial blood oxygen saturation bias. **p < .01
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b. Spearman Correlation: Buccal and finger SpO2 - SaO2 bias, oximetry waveform amplitude, and body temperature. To determine whether a relationship existed between SpO2 - SaO2 bias, HR, MAP and body temperature with oximetry waveform amplitude, Spearman rho analyses were accomplished (Table 13). There were no correlations identified between either buccal or finger oximetry waveform amplitude and SpO2 - SaO2 bias. There was a moderate, positive, correlation between buccal and finger
SpO2 - SaO2 bias (r =.23, p <.05). Participants with greater buccal SpO2 - SaO2 bias also had greater finger SpO2 - SaO2 bias.
There was a moderate, positive, correlation between buccal waveform and finger waveform (r = .44, p < .01). Participants with stronger buccal waveform amplitudes also had stronger finger waveforms. There was a moderate, inverse, correlation between heart rate and both buccal (r = -.25, p < .05) and finger (r = -.21, p < .05) waveform amplitudes.
Participants with faster heart rates had weaker buccal and finger waveform amplitudes.
Participants with slower heart rates had stronger buccal and finger waveform amplitudes.
There was a moderate, positive, correlation between body temperature and both buccal (r =
.21, p < .05) and finger (r = .29, p < .01) waveform amplitudes. Participants with higher body temperature also had stronger buccal and finger waveforms.
c. Buccal and finger SpO2 - SaO2 bias and presence of vasopressor infusions. The total number of participants with vasopressor infusions was 22 (Table 14).
Seventeen participants had only a single vasopressor infusion and only 5 participants had 2 vasopressors simultaneously infusing. No participants had more than 2 concurrent vasopressor infusions. Due to the small number of participants with vasopressors, analyses of specific infusion doses were not performed. Analyses included only the presence of
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Table 13
Spearman Correlation, Buccal and Finger Pulse Oximetry – Arterial Blood Oxygen Saturation Bias with Pulse Oximetry Waveform Amplitude, Heart Rate, Mean Arterial Pressure, and Body Temperature (N = 98) Correlation 1 2 3 4 5 6 7 1. BBias - .01 .23* -.004 -.08 .06 -.01 2. BWave - - -.15 .44** -.25* -.14 .21* 3. FBias - - - -.16 -.11 .33 -.12 4. FWave - - - - -.21* -.01 .29** 5. HR ------.01 .14 6. MAP ------.10 7. Temp ------Note. BBias = buccal pulse oxygen saturation - arterial blood oxygen saturation bias; BWave = buccal pulse oximetry waveform amplitude; FBias = finger pulse oxygen saturation - arterial blood oxygen saturation bias; FWave = finger pulse oximetry waveform amplitude; HR = heart rate; MAP = mean arterial pressure; Temp = body temperature. *p < .05 **p < .01
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Table 14
Vasopressor Infusion Medications (n = 22) Vasopressor # of Participants Single infusions intropin only 0 norepinephrine only 10 vasopressin only 6 phenylephrine only 1
2 concurrent norepinephrine & vasopressin 3 infusions intropin & epinephrine 1 intropin & vasopressin 1 Note. Describes participants with either single vasopressor infusions or 2 concurrent vasopressor infusions. No participant had more than 2 vasopressor infusions.
119 vasopressor infusions. To compare buccal and finger SpO2 - SaO2 bias based on presence of vasopressor infusions, independent-samples t-tests were performed (Table 15). There was no difference in buccal SpO2 - SaO2 bias based on presence of vasopressor infusions (M = -3.59,
SD = 9.20; t = 1.05), p = .30 (two tailed) and no vasopressor infusions (M = -1.45, SD =
4.71). Variances in buccal SpO2 - SaO2 bias for participants with and without vasopressors were not equal (Sig < .05). The magnitude of the differences in the means (mean difference
= 2.14, 95% CI: -2.05 to 6.34) was small (eta squared = .011). There was a statistically significant difference in finger SpO2 - SaO2 bias based on presence of vasopressors
(M = -1.0, SD = 2.35) and no vasopressor infusions (M = .41, SD = 1.99). The magnitude of the differences in the means (mean difference = 1.41, 95% CI: .41 to 2.40) was moderate (eta squared = .076).
Spearman Correlation was accomplished to determine whether buccal and finger
SpO2 - SaO2 bias correlated with presence of vasopressor infusions (Table 16). There was a moderate, inverse, correlation between the finger SpO2 - SaO2 bias and presence of vasopressor infusion (r = -.23, p < .05). Participants with vasopressor infusions also had finger SpO2 values that underestimated SaO2.
d. Buccal and finger SpO2 - SaO2 bias and skin phototype. As presented earlier in this chapter, only two participants (n = 2, 2%) were classified as having black skin phototype. To prevent type I error, that is, to prevent rejecting the null hypothesis when it is true, the two participants with black skin phototype were removed only for these analyses.
Spearman rho analyses were accomplished to determine whether a relationship existed between skin phototype and SpO2 - SaO2 bias (Table 17). There were no significant
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Table 15 Independent-Samples t Test: Buccal and Finger Pulse Oximetry – Arterial Blood Oxygen Saturation Bias with and without Vasopressor Infusions (N = 98) No Vasopressors Vasopressors Levene’s Test N M SD N M SD df t p Buccal Bias Equal Variance 76 -1.45 4.71 22 -3.59 9.20 24 1.05 .30 Not Assumed Finger Bias Equal Variance 76 .41 1.99 22 -1.00 2.35 96 2.81 .006* Assumed *p < .05. Equal variances assumed
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Table 16 Spearman Correlation: Buccal and Finger Pulse Oximetry – Arterial Blood Oxygen Saturation Bias and Presence of Vasopressor Infusions (N = 98) Correlations 1 2 3 1. Buccal Bias - .22* -.02 2. Finger Bias - - -.23* 3. Vasopressor Infusing - - - *p < .05
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Table 17 Spearman Correlation, Buccal and Finger Pulse Oximetry – Arterial Blood Oxygen Saturation Bias with Skin Phototype (n = 96) Correlations 1 2 3 1. Buccal Bias - .22* .01 2. Finger Bias - - .22* 3. Phototype - - - *p < .05
123 relationships between buccal SpO2 - SaO2 bias and skin phototype. There was a moderate, positive, correlation between finger SpO2 - SaO2 bias and skin phototype (r = .22, p < ,05).
Participants with brown skin phototype had more positive finger SpO2 - SaO2 bias.
To determine whether there was a difference in SpO2 - SaO2 bias based on skin phototype, independent-samples t-tests were performed (Table 18). There were no significant differences in buccal SpO2 - SaO2 bias based on brown skin phototype (M = -2.23,
SD 7.29; t = .81), p = .42 (two tailed) and white skin phototype (M = -1.23, SD = 3.57). The magnitude of the differences in the means (mean difference = 1.0, 95% CI: -1.47 to 3.49) was very small (eta squared = .007).
There were no significant differences in finger SpO2 - SaO2 bias based on brown skin phototype (M = .34, SD 2.31; t = -1.26), p = .21 (two tailed) and white skin phototype (M =
-.23, SD = 1.93). The magnitude of the differences in the means (mean difference = -.56,
95% CI: -1.45 to .32) was small (eta squared = .02).
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Table 18
Independent-Samples t-Test: Buccal and Finger Pulse Oximetry – Arterial Blood Oxygen Saturation Bias Based on Skin Phototype (n = 96) White Phototype Brown Phototype Levene’s Test N M SD N M SD t p Buccal Bias Equal Variance 40 -1.23 3.57 56 -2.23 7.29 .81 .42 Assumed
Finger Bias Equal Variance 40 -.23 1.93 56 .34 2.31 -1.26 .21 Assumed
Note. N = 96. SpO2 = pulse oxygen saturation; SaO2 = arterial blood oxygen saturation.
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V. DISCUSSION, IMPLICATIONS, AND RECOMMENDATIONS In this chapter, the PI presents a summary of the study findings, discussion of the findings as they relate to the existing literature. The chapter also presents implications for nursing education, clinical practice and research. The chapter concludes with study limitations and strengths of the study.
The purpose of this study was to: establish LOA between buccal SpO2 and SaO2 in mechanically ventilated adults. The purpose of this study was also to compare the LOA for buccal SpO2 and finger SpO2 relative to SaO2 in mechanically ventilated adults.
Additionally, the purpose of this study was also to examine the degree to which FiO2, PaO2,
VE, HR, MAP, SpO2 waveform amplitude, presence of vasopressors, body temperature and skin phototype influenced buccal and finger SpO2 in mechanically ventilated adults.
A. Summary and Discussion of Findings.
Review of results specific to the second aim of the present study did not support that buccal pulse oximetry was as accurate as finger pulse oximetry in estimating arterial oxygen saturation. Review of results of the present study revealed that finger pulse oximetry, using the NellcorTM OxiMax-A disposable finger pulse oximeter sensor, estimated arterial blood oxygenation more accurately than the buccal site. This finding was in line with De Jong
(2005).
Oxygen transport and oxygenation physiology, as explained in Chapter I, guided investigation of elements to include: fraction of inspired oxygen, partial pressure of oxygen, minute ventilation, heart rate, mean arterial pressure, body temperature, and waveform amplitude. Also studied were impact of vasopressor infusions and skin phototype.
Ninety-eight (98) adults participated in this study. As discussed in Chapter IV, the
126 nature and timing of the recruitment and informed consent processes delayed data collection.
Consequently, mechanically-ventilated participants had received oxygenation therapy and stabilizing interventions, which impacted SaO2 data homogeneity. This will be discussed in more detail later in this chapter.
1. Buccal Pulse Oximetry Accuracy and Precision.
a. Aim 1: Limits of agreement between buccal pulse oximetry and arterial blood oxygen saturation. LOA (95%) between buccal SpO2 and SaO2 ranged from
-13.8 to 9.87. Review of scatterplot revealed mean bias of -1.9 + 6.0. Buccal SpO2 - SaO2 bias dispersed more widely as values decreased < 90%. Buccal SpO2 - SaO2 mean bias exceeded the a priori + 4% using the NellcorTM OxiMax-A disposable finger pulse oximetry sensor.
b. Aim 2: Compare limits of agreement for buccal pulse oximetry and finger pulse oximetry relative to arterial blood oxygen saturation. LOA (95%) between finger
SpO2 and SaO2 ranged from -4.1 to 4.3. Review of finger SpO2 - SaO2 scatterplot revealed mean bias of -.09 + 2.2. There was a positive, correlation between buccal and finger SpO2 -
SaO2 bias. This correlation was an expected finding; however, the strength was only medium
(r = .42, p < .01). Review of findings of the present study demonstrated that buccal pulse oximetry was not as accurate as finger pulse oximetry.
2. Aim 3: Examine the Degree to which FiO2, PaO2, VE, HR, MAP,
Waveform Amplitude, Presence of Vasopressor Infusions, Body Temperature, and Skin
Phototype Influence Buccal and Finger Pulse Oximetry.
a. Buccal and finger SpO2 – SaO2 bias with MAP. There was a positive, correlation between MAP and finger SpO2 - SaO2 bias. Participants with lower
127 blood pressure had finger SpO2 values that underpredicted SaO2. There was no statistically significant correlation between MAP and buccal SpO2 - SaO2 bias.
b. Buccal and finger SpO2 – SaO2 bias with vasopressors. As previously discussed in Chapter IV, the potential influence of vasopressors by dose was not investigated as the number of participants with similar vasopressor infusions was very low (n
= 22). There was a difference in finger SpO2 - SaO2 bias in participants with (M = -1.0, SD =
2.35) and without (M = .41, SD 1.99) vasopressor infusions. These findings support that vasopressor infusions potentiate peripheral vasoconstriction and local blood flow may impact finger perfusion more so than the buccal region (Darovic, 2002; Haldane, 2001; Rutherford,
1993). These findings are also in line with Wan and Fernandez' research findings that microvascular flow to buccal mucosa, and perhaps other cerebral and facial areas, is preserved in low flow states (Wan et al., 2010; Fernandez et al., 2007). Review of results of the present study also revealed that finger pulse oximetry tended to underestimate arterial blood oxygen saturation in the presence of vasopressor infusions. Buccal and finger SpO2 both markedly underestimated SaO2 in one participant with two vasopressor infusions.
c. Buccal and finger SpO2 – SaO2 bias with skin phototype. As previously discussed in Chapter IV, due to race and skin phototype homogeneity (only 2 of
98 participants were classified with black skin phototype), skin phototype data analyses were accomplished only on participants identified with either white or brown skin pigment. There was a positive, correlation between finger SpO2 - SaO2 bias and skin phototype. Participants with brown skin phototype had more positive finger SpO2 - SaO2 bias. Finger pulse oximetry tended to over-estimate SaO2.
3. Correlation Findings for Independent Variables.
128
a. Buccal and finger SpO2 – SaO2 waveform amplitude with HR.
There was an inverse correlation between heart rate and both buccal (r = -.25, p < .05) and finger (r = .21, p < .05) SpO2 - SaO2 waveform amplitude. Participants with faster heart rates had weaker buccal and finger SpO2 waveform amplitudes. This could be a result of sympathetic nervous system activation activating peripheral vasoconstriction along with faster heart rates (Guyton & Hall, 2006a).
b. FiO2 and PaO2. There was a positive, correlation between FiO2 and
PaO2 (r = .28, p < .01). This was an expected finding as oxygen delivery is increased, partial pressure of oxygen should also increase.
c. Heart rate and VE. There was a positive, correlation between heart rate and VE (r = .20, p < .05). This might be explained by participants having both elevated heart rates and minute ventilation in response to illness severity.
d. Pulse oximetry waveform amplitude and body temperature. There was no correlation between pulse oximetry waveform amplitude and neither buccal or finger
SpO2 - SaO2 bias. This supports findings of Lawson and colleagues who found that even very low peripheral blood flow could still result in measurable pulse oximetry (Lawson,
1987).
There was a positive correlation between higher body temperatures and stronger buccal (r = .21, p < .05) and finger (r = .29, p < .01) pulse oximetry waveform amplitude.
Participants with lower body temperatures had weaker buccal and finger pulse oximetry waveform amplitude. This finding partially supports Awad, who found that finger pulse waveform amplitude decreased more than ear pulse amplitude during effects of cold (Awad,
2001).
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B. Implications for Nursing Education, Practice and Research.
1. Implications for Nursing Education.
Education at baccalaureate and graduate levels does not consistently deliver detailed information about pulse oximetry technology limitations. The differences between TPO and
RPO pulse oximetry technologies is also not consistently taught well in general critical care education programs. As such, nurses may not appreciate impact if differing pulse oximetry technologies are improperly used. TPO pulse oximetry sensors must be placed on sites that provide a pulsating bed in between the light emitter and light detector. On the other hand, only RPO sensors are appropriate for placement on flat monitoring surfaces, such as the forehead. Nurses must also be aware that pulse oximetry remains a surrogate measure that estimates the gold standard, SaO2, and that values must be appraised as part of the entire clinical picture.
2. Implications for Nursing Clinical Practice.
Clinicians must understand the operation and limitation of pulse oximetry technology.
However, the sparse education about pulse oximetry technology differences may result in nurses' inaccurate assumptions that TPO and RPO sensors are interchangeable. As other modalities to measure oxygenation delivery and consumption, such as end-tidal CO2 monitoring, pulse oximetry remains in widespread use in critical care, emergency, and perioperative environments. Continuous pulse oximetry provides ability to monitor trends.
Pulse oximetry also remains a fundamental tool during ground and Critical Care Air
Transport. Nurses must also understand that pulse oximetry may serve only as an estimate of
SaO2. Nevertheless, a clinical benefit of continuous pulse oximetry is the ability to monitor trends.
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3. Implications for Nursing Research.
An area for future study is heart rates in relation to pulse oximetry and end-tidal CO2 waveform configuration and amplitude, as well as accuracy and precision. In the present study, faster heart rates correlated with lower buccal and finger waveform amplitude, but correlated only with less precise finger SpO2 - SaO2 bias. Further investigation on SpO2 precision based on heart rate may be beneficial as many critically-ill patients experience tachycardic states.
Additionally, as PaO2 was associated with finger pulse oximetry in the present study, another area for future study is pulse oximetry in relation to P50, or the partial pressure of oxygen (PO2) required to saturate hemoglobin to 50% (Leach & Treacher, 1998). Such a study could yield valuable information surrounding variances based on oxygen binding to hemoglobin.
As vasopressor infusions are frequently used for unstable patients, another study area of interest is the effect of vasopressor infusions and specific dose rates on pulse oximetry. In the present study, presence of vasopressors did not influence buccal or waveform amplitude.
Congruent with findings from Shelley, Awad and Jobes, mechanical ventilation variation was more pronounced on the buccal POP waveform than on the finger POP waveform (Jobes &
Nicolson, 1988; Awad et al., 2001; Shelley et al., 2006; Figure 31). The effect of respirations on POP baseline waveform, as a marker of fluid volume status, is well documented
(Addison, 2014; Chandler et al., 2012; Hoiseth, Hoff, Hagen, Kirkeboen & Landsverk, 2016;
Pizov et al., 2010; Shafique, Kyriacou, & Pal, 2012; Shelley et al., 2006; Shackelford et al.,
2015).
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Shelley and colleagues found that POP waveforms from the ear demonstrated effects of mechanical ventilation 18 times more strongly than POP waveforms from the finger.
Researchers hypothesized the findings were related to decreased ventilation signal attenuation due to the shorter distance from the ear to chest versus from the finger to chest
(Shelley et al., 2006). In the present study, waveform variation was noticed in approximately
5 participants (Figure 32). This waveform variation was mitigated with SpO2 sensor repositioning within the buccal area.
Shelley also surmised that the amplified respiration effects on ear POP waveform could be related to local insensitivity to "sympathetically mediated vasoconstriction". Mills and colleagues demonstrated significant waveform variation in a 15-month child with cardiac tamponade. The magnitude of variation in waveform disappeared after pericardiocentesis
(Mills, Udupa & Gow, 2016). In the present study, vasopressors were associated with differences in finger SpO2 - SaO2 bias. It would be interesting to investigate other potential effects of vasopressors on the fingers that could affect pulse oximetry accuracy and precision.
Moreover, as body temperature was associated with buccal and finger waveform amplitude and the use of targeted temperature management (formerly induced hypothermia) continues to rise, pulse oximetry should be studied in participants with extreme body temperatures. Future studies might include precise measurement of waveform amplitude.
Finger SpO2 - SaO2 bias correlation with buccal SpO2 - SaO2 bias was expected as both methods are surrogates for predicting SaO2. However, it was interesting to find this was only a medium strength correlation. This finding supports that there may be differences between buccal and finger sites in predicting arterial blood SaO2.
132
Figure 32. Buccal and Finger Pulse Oximetry Waveforms. Buccal waveform revealed marked baseline variation with respiratory cycle.
133
Finally, future research on combined use of both ETCO2 and SpO2 could provide richer information and more precise surrogate measurement of patient oxygenation status.
C. Limitations of the Study.
1. Sample Homogeneity.
The present study sample was primarily comprised of participants with white or brown skin phototype. Therefore, the study hypothesis was unable to be tested with participants with black skin phototype. Analyses by skin phototype was limited to independent samples t test. The present study lacked sufficient participants in the
Black/African American race category or any participants in race categories of Native
Hawaiian/other Pacific Islander (non-White), American Indian/Alaska Native or Asian. The present study participants were also predominantly male. Future studies should focus on including participants in race categories not well represented in the present study.
2. Blood Oxygen Saturation Homogeneity.
As 84% of participants in the present study had SaO2 levels > 95%, this greatly limits applicability to critically ill, mechanically ventilated adults with lower SaO2 values. This limited variance prevented investigation of buccal and finger SpO2 monitoring at lower saturation levels and in the presence of hypoxemia. Greater variation in SaO2 values may have been possible with data collection occurring immediately after endotracheal intubation.
Earlier, expedient blood gas analysis would have capture oxygenation effects imposed by acute and critical illness and injury. However, the recruitment process and timing of informed consent did not allow earlier timing for collection of data. The delays in data collection were further influenced as the current study design employed data collection with the next clinically-indicated arterial blood gas analysis. This data homogeneity trend may
134 have been mitigated through surrogate consent allowing data collection from arterial blood gas samples taken during early phases of resuscitation.
Most participants (62.2%) had SaO2 values between 98 - 100%, which typically reflect partial pressures of oxygen of 100 or greater (Guyton & Hall, 2006e). These higher saturation and extremely limited variance prompted termination data of collection for this study. This prevented ability to confirm findings of Nellcor researchers and Sendak, who found that pulse oximetry accuracy, even in presence of induced, marked hypoxemia, closely reflect SaO2 (Sendak et al., 1986; Nellcor, 2005). Due to SaO2 homogeneity, the PI was also unable to study whether buccal pulse oximetry values and the modified finger oximetry sensor were less accurate at lower saturation values in line with findings of Trivedi and colleagues (Trivedi, Ghouri, Lai et al., 1997).
3. Fraction of Inspired Oxygen Homogeneity.
Most participants (63%) had FiO2 set at 40% or lower. FiO2 of 40% is the traditionally lowest preferred range once ABGs have "normalized." The lower FiO2 also was impacted by stabilization and oxygenation status improvement during the delay to data collection as described earlier.
4. Mean Arterial Pressure.
For purposes of this study, MAP was investigated as a potential contributor to peripheral vasoconstriction as a compensatory mechanism for central pressure and potential impact on SpO2. Although higher MAP values can also be undesirable or even detrimental,
MAP values < 70 mm Hg were of interest for the present study, as this "cut-off" is considered the low-end of normal that may prompt clinical intervention. Only 26 (27%) of participants had MAP values < 70 mm Hg. Again, the more stable MAP findings in the
135 present study likely resulted from delay admission to the hospital to time of data collection.
The longer participants were hospitalized, medical treatment and clinical interventions helped stabilize fluid/volume and perfusion status.
5. Body Temperature.
The present study did not provide the opportunity to study pulse oximetry precision and accuracy at extreme body temperatures. This limited ability to fully investigate effects of peripheral vasoconstriction, possible effects on pulse amplitude and, therefore, on accuracy and precision of buccal SpO2. As induced, therapeutic hypothermia gains use for cardiac and brain-injured patients, further study on pulse oximetry accuracy with body temperature extremes is merited.
6. Pulse Oximetry Sensor.
To date, there is no pulse oximetry sensor on the market designed specifically for use in the buccal region. All past studies and the present study on buccal pulse oximetry utilized sensors designed for use on other anatomy. Finger sensors are designed to measure infrared and red light spectral differences through a pulsating capillary bed to estimate oxygen saturation of hemoglobin (Rutherford, 1993b). It remains unclear whether finger and buccal tissue differences additionally impact SpO2 device accuracy and precision. Therefore, application of modified finger SpO2 sensors to the buccal area introduced an additional variable and the potential for data collection error. Additionally, although the PI followed identical, structured steps when modifying the SpO2 finger sensors for buccal use, no additional device quality control measures were employed on each new, modified buccal pulse oximetry sensor.
136
7. Pulse Amplitude Blip Bar.
As previously discussed, SpO2 pulse amplitude waveform strength was visually assessed by the PI. The PI introduced subjectivity by estimating visualization of the height of the blip bar. Therefore, analysis of waveform strength was not as sensitive, threatening statistical validity. Future studies may include more precise pulse strength measurement.
D. Strengths of the Study.
1. Pulse Oximetry Sensor Technology.
A strength of the present study is that the same TPO pulse oximetry sensor type was used to measure both buccal and finger SpO2. The PI simultaneously applied the same pulse oximetry technology/sensor type to both finger and buccal sites. This was not the case in previous studies, in which different sensors types were applied to buccal and finger sites, thus introducing additional technology variability into study findings.
2. Pulse Oximetry Data Collection Times.
Another strength of the present study is that buccal and finger SpO2 values were captured at precisely the same time and within + 3 seconds of ABG sampling. Simultaneous data values are a critical point for Bland-Altman analyses (Myles, 2007).
3. Investigation of Multiple Potentially Influencing Variables. An additional strength of the present study was that the PI concurrently investigated multiple potential influencing variables. No previous single buccal pulse oximetry study investigated these variables.
E. Summary.
The present study found that finger pulse oximetry demonstrated greater agreement with arterial blood saturation over buccal pulse oximetry. Review of findings revealed that
137 buccal SpO2 under-predicted SaO2 for 45 participants and that 95% LOA exceeded the a priori clinically acceptable value of + 4%. Review of findings of the present study also revealed that participants with faster heart rates had greater accurate finger SpO2 - SaO2 bias.
Finger SpO2 overestimated participants with greater MAP values. Also of interest, the present study found that finger SpO2 - SaO2 bias differed in participants with and without vasopressor infusions.
138
APPENDIX A: Abbreviations Page 1 of 2
ABG – arterial blood gas
ARMS - root-mean-square of the differences.
AV - absolute value
B Bias - buccal pulse oxygen saturation - arterial blood oxygen saturation bias
BMET - Biomedical Equipment
CLIA - Clinical Laboratory Improvement Amendments
CMS - Centers for Medicare and Medicaid Services
CPB - cardiopulmonary bypass
CINAHL - Current Index to Nursing and Allied Health Literature
CCATT - Critical Care Air Transport Team
FiO2 – fraction of inspired oxygen in a gas mixture. In mechanical ventilation, the
percentage of oxygen delivered to the patient.
F Bias - finger pulse oxygen saturation - arterial blood oxygen saturation bias
HR - heart rate
IRB - Institutional Review Board
ICU - intensive care unit
LOA - limits of agreement
MAP - mean arterial pressure
MEDLINE - Medical Literature Analysis and Retrieval System Online (MEDLARS Online) nm – nanometer. A millionth of a meter.
OHDC - oxyhemoglobin dissociation curve
139
APPENDIX A: Abbreviations Page 2 of 2
OR - operating room
P50 - the oxygen concentration at which SaO2 is 50%
PaO2 – partial pressure of arterial oxygen. The part of total blood gas pressure exerted by
oxygen gas. Amount of oxygen dissolved in arterial blood. Normal range 80 – 100
mm Hg.
PO2 – partial pressure of oxygen. Measures the amount of oxygen dissolved in arterial blood
RPO - reflectance pulse oximetry
RT - respiratory therapist, respiratory therapy
SaO2 – saturation of arterial hemoglobin with oxygen. Measures the percentage of oxygen
carried by hemoglobin in arterial blood.
SpO2 – ratio of oxyhemoglobin (oxygen saturation in hemoglobin) as measured in a
capillary bed by pulse oximeter.
Temp - body temperature
TPO - transmittance pulse oximetry
VE – Minute ventilation. Total volume of air either inspired or exhaled in one minute
VO2 – oxygen uptake. The amount of oxygen taken up by the tissues.
140
APPENDIX B: IRB Approval Page 1 of 2
141
APPENDIX B: IRB Approval Page 2 of 2
142
APPENDIX C: IRB Approval - Change to Number of Participants, Adds Study Site, 2 ICUs and Sponsor, Updates Equipment
143
APPENDIX D: IRB Approval - Change to Inclusion/Exclusion Criteria
144
Appendix E: IRB Approval - Change to Inclusion Criteria, Data Collection Tool and Adds Information for ABG Analyzer
145
Appendix F: Recruitment Poster
146
APPENDIX G: Informed Consent - English
147
148
149
150
151
152
153
154
APPENDIX H: Informed Consent – Spanish
155
156
157
158
159
160
161
162
163
164
APPENDIX I: Data Collection Worksheet
165
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VITA
Patricia N. Meza (Pat) was born on January 1, , in El Paso, Texas to Isaias and
Elena Meza. Pat enlisted in the United States Air Force and served on active duty from 1982 to 1986. She then earned her Associates Degree in Nursing Science in 1990 from New
Mexico State University (NMSU) in Las Cruces, NM and worked as a registered nurse in the
Medical and Surgical ICUs at Memorial Medical Center, also in Las Cruces, from 1990 -
1992. Pat earned her Baccalaureate in Science of Nursing degree in 1992, also from NMSU, and re-entered active duty Air Force service as a second lieutenant in June 1992. She continued to serve on active duty for an additional 24 years.
Pat earned a Master’s of Science degree in Adult Education from Troy State
University, Montgomery, AL. She then earned a Master’s of Science degree in Critical Care and Emergency/Trauma Nursing from the University of Maryland at Baltimore.
Pat was admitted to the Ph.D. program in 2008. In February, 2010, Pat co-presented
“Predictors of Success among Caucasian and Hispanic Students in Traditional and
Accelerated Nursing Programs,” at Health Care Challenges of the Next Decade: 24th Annual
Conference of the Southern Nursing Research Society, Austin, Texas. She also co-authored the article “Accuracy and precision of buccal pulse oximetry” published in 2011 in Heart &
Lung – The Journal of Critical Care.
Throughout her Air Force career, Pat held a multitude of positions, primarily focusing in adult critical care/trauma. During this time, she deployed multiple times to locations throughout Southwest Asia, South America and West Indies. Pat's doctoral work was sponsored by the Air Force Institute of Technology. In 2016, she retired in the rank of
Colonel.
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