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Indirect Calorimetry Based on Oxygen Luminescence Quenching

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Indirect Calorimetry Based on Oxygen Luminescence Quenching by Craig T. Flanagan A dissertation submitted to the faculty of The University of Utah In partial fulfillment of the requirements for the degree of Doctor of Philosophy Department of Bioengineering The University of Utah January 2003 Page i Copyright Craig Thomas Flanagan 2003 All Rights Reserved Page ii THE UNIVERSITY OF UTAH GRADUATE SCHOOL SUPERVISORY COMMITTEE APPROVAL of a dissertation submitted by Craig Thomas Flanagan This dissertation has been read by each member of the following supervisory committee and by majority vote has been found to be satisfactory. ______________ ____________________________________ Chair: Dwayne R. Westenskow ______________ ____________________________________ Joseph A. Orr ______________ ____________________________________ Richard A. Normann ______________ ____________________________________ Douglas A. Christensen ______________ ____________________________________ Kenneth Johnson Page iii THE UNIVERSITY OF UTAH GRADUATE SCHOOL FINAL READING APPROVAL To the Graduate Council of the University of Utah: I have read the dissertation of Craig Thomas Flanagan in its final form and have found that (1) its format, citations and bibliographic style are consistent and acceptable; (2) its illustrative materials including figures, tables, and charts are in place; and (3) the final manuscript is satisfactory to the supervisory committee and is ready for submission to The Graduate School. __________________ ____________________________________ Date Dwayne R. Westenskow Chair, Supervisory Committee Approved for the Major Department ____________________ Vladimir Hlady Chair/Dean Approved for the Graduate Council ____________________ David S. Chapman Dean of The Graduate School Page iv ABSTRACT This Ph.D. dissertation describes the development of an indirect calorimetry system for use in a critical care setting. Indirect calorimetry systems measure the quantity of oxygen consumed (VO 2) by a patient, the quantity of carbon dioxide produced (VCO2), and the respiratory quotient (RQ) or the ratio of VCO 2 to VO 2. The clinical determination of VO 2, VCO 2 and RQ values from patients undergoing mechanical ventilation therapy is useful in the determination of patient metabolic energy expenditure as well as the proportionate determination of substrate (protein, fat and carbohydrate) utilization. This information is valuable from a clinical standpoint for determination of proper nutritional management of critically ill (ventilator dependent) patient populations. Caloric mismanagement has been associated with increased morbidity and mortality as well as increased hospital stay. Further, improper management of substrate balance may produce ventilator weaning difficulties. A compact indirect calorimetry system capable of obtaining and tracking these measurements in real-time using an on airway sensor, would represent a significant advancement in the state of the art of medical care. This advancement would allow physicians to tailor nutritional support regimens to the current needs of patients as well as making possible the tracking of nutritional parameters, while at the same time not necessitating the temporary removal of patients from life support systems. The system developed herein solves algorithm challenges that such a system would present. The ultimate goal of this dissertation is to set the foundation for an improvement in the standard of critical care medicine delivery. Page v LIST OF FIGURES Figure Page 2.1. Diagram of the metabolic simulation system. 9 2.2. Timing of gas removal. 11 2.3 . Pnuematic schematic of flow delivery system. 13 2.4. Functional Arrangement of Valve Timer Board 14 2.5. Programmable valve timer board. 15 2.6. User interface of metabolic simulator software 17 2.7. Test lung volumes 23 2.8. Valve on-time vs. delivered gas volume for CO 2. 32 2.9. Valve on-time vs. delivered gas volume for vacuum. 33 2.10. Valve on-time vs. delivered gas volume for O 2. 34 2.11. Bland-Altman plot of Valve on-time vs. delivered gas volume for vacuum. 35 2.12. Bland-Altman plot of Valve on-time vs. delivered gas volume for CO 2. 36 2.13. Bland-Altman plot of Valve on-time vs. delivered gas volume for N 2. 37 3.1. Gas partial pressures before/after the addition/removal of respiratory gases.45 3.2. RQ represented as the slope of the change in volumes of CO 2 and O 2. 48 3.3. RQ as determined from partial pressure slope vs. FIO 2. 50 3.4. Slope Algorithm and Deltatrac Energy Expenditure Results in Kcal/day 67 3.5. Slope Algorithm and Deltatrac RQ 68 3.6. Bland-Altman plot of the Slope Algorithm EE vs. the (Slope EE + Deltatrac EE)/2 output. 69 3.7. Bland-Altman plot of the Slope Algorithm RQ vs. the (Slope RQ+ Page vi Deltatrac RQ)/2 output. 70 3.8 X-Y plot of Slope vs. Deltatrac with the addition of data from Harris- Benedict vs. Deltatrac. 71 A.1 Details of Polarographic (Clark) cell. 78 A.2. Details of fuel cell. 80 A.3. Details of zirconium oxide fuel cell. 83 A.4. Details of Pauling cell showing torsional force on dumbbell resulting from oxygen in sampling chamber. 85 A.5. Details of Hummel cell showing pneumatic path of sample and reference gases through the electromagnet gap and differential pressure measurement technique. 87 A.6. Joblinski diagram. 90 A.7. Construct of Phosphorescence Quenching Sensor. 95 A.8. Timing diagram for phosphorescence quenching sensor. 97 Page vii LIST OF TABLES Table Page 2.1. Applicable Training/Test Lung Specifications 18 2.2. Applicable Specifications 19 2.3. Potential sources of system error 20 2.4. Typical values of input variables for a V E of 4000 ml 28 2.5. Error sensitivity coefficients. 29 2.6. Monte Carlo analysis results 30 3.1. Algorithms Compared. Mean and Std. Dev. displayed at ventilator and lung settings. 63-64 A.1. Qualitative comparison of oxygen sensing technologies. 94 Page viii ACKNOWLEGEMENTS Thank you to Joe and Dwayne for helping me achieve my goal. Thanks to my mother Shirley for all of her patience and my father Lawrence who was my hero and gave me inspiration through the years. Page ix TABLE OF CONTENTS ABSTRACT v LIST OF FIGURES vi-vii LIST OF TABLES viii ACKNOWLEDGEMENTS ix CHAPTER 1. INTRODUCTION TO INDIRECT CALORIMETRY 1 2. A METABOLIC SIMULATION TECHNIQUE FOR INDIRECT 7 CALORIMETRY SYSTEM VALIDATION 3. A NOVEL RESPIRATORY QUOTIENT ALGORITHM BASED 42 ON PARTIAL PRESSURE OXYGEN AND CARBON DIOXIDE SENSORS 4. CONCLUSION 74 APPENDIX A. 75 Page x CHAPTER 1 INTRODUCTION TO INDIRECT CALORIMETRY This dissertation focuses on a novel approach to a clinical tool known as indirect calorimetry. Indirect calorimetry is a method by which the type and rate of nutritional substrate utilization and heat production are measured in vivo from gas exchange measurements. Specifically, indirect calorimetry is useful in estimating the amounts of energy derived from protein, carbohydrate and fat substrates. Clinically, indirect calorimetry allows for an estimated assessment of the metabolic state of a patient undergoing mechanical ventilation therapy for prescription of the proper amount and makeup of parenteral nutritional support. The dissertation describes a new and novel approach to indirect calorimetry. Specifically, in this dissertation a new algorithm for determining a parameter central to indirect calorimetry measurements known as the respiratory quotient (RQ) is both derived and validated for accuracy. This validation consists of the use of a new and novel metabolic simulator constructed for this dissertation and described herein. These two improvements to the state of the art (the algorithm and the simulator) make possible the construction of an indirect calorimetry device which provides many advantages over current systems such as improved accuracy, breath-to-breath determination of indirect calorimetry parameters, a smaller form-factor device, and the ability to easily trend such parameters. Such advantages comprise an evolutionary improvement to the current art of clinical metabolic patient assessment. Proper nutrition is important to the critically ill patient population. Improper patient nutrition has been associated with poor clinical outcome 1-6. Specifically, overfeeding can produce hyperglycemia and fatty infiltration producing liver dysfunction as well as respiratory acidosis/hypercapnia which are associated with ventilator weaning difficulties 7. Underfeeding can depress the immune system response and lead to loss of lean body mass. Page 1 Indirect calorimetry has established itself as a useful clinical tool in patient nutritional assessment and management. Common usage of the technology includes the following patient populations: Chronic pulmonary disease Cardiac failure Multiple organ failure Cancer Hypovolemic shock Sepsis Burn injury Major trauma or surgery Inflammatory bowel disease Obesity Thyroid disease CLINICAL MEASUREMENTS ASSOCIATED WITH INDIRECT CALORIMETRY Indirect calorimetry produces a number of important clinical measures of patient metabolic state. These include the respiratory quotient (RQ), minute oxygen consumption (VO 2), and resting energy expenditure (REE). VO 2 is a measure of the patient’s volumetric consumption of oxygen on a minute basis. The RQ is a measure of the ratio of per minute volumetric carbon dioxide production (VCO 2) to per minute volumetric oxygen consumption (RQ = VCO 2/VO 2). This ratio has clinical relevance in that it provides insight into patient substrate consumption. Specifically, the RQ varies according to the stoichiometry of substrate
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