Creating an Individualized Predictive Model of PAO2 and PACO2 Changes During Voluntary Static Apnea for Sedentary Subjects
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DEGREE PROJECT IN MEDICAL ENGINEERING, SECOND CYCLE, 30 CREDITS STOCKHOLM, SWEDEN 2018 Creating an individualized predictive model of PAO2 and PACO2 changes during voluntary static apnea for sedentary subjects Att skapa en individualiserad prediktiv modell av PAO2- och PACO2-förändringar under frivillig statisk apné för stillasittande personer DIANA SVEA ANTHONY KTH ROYAL INSTITUTE OF TECHNOLOGY SCHOOL OF ENGINEERING SCIENCES IN CHEMISTRY, BIOTECHNOLOGY AND HEALTH Creating an individualized predictive model of PAO2 and PACO2 changes during voluntary static apnea for sedentary subjects DIANA ANTHONY Master in Medical Engineering Date: June 26, 2018 Supervisor: Mikael Gennser, Co-supervisor: Pawel Herman Examiner: Ola Eiken Swedish title: Att skapa en individualiserad prediktiv modell av PAO2- och PACO2-förändringar under frivillig statisk apné för stillasittande personer School of Engineering Sciences in Chemistry, Biotechnology, and Health iii Abstract The primary aim of this study was to fill a gap in the literature in understanding maximal BH in untrained, non-divers by generating a predictive numerical model for PAO2 and PACO2 throughout BH. There have been little to no previous attempts at explicitly character- izing the influence of impermanent personal or environmental factors on PAO2 or PACO2 at BH breakpoint. The metabolic human consump- tion of O2 and production of CO2 as measured through alveolar par- tial pressures was observed over time during a voluntary maximum breath-hold for 18 members of the general population. The coefficient of determination was used to determine accuracy of the model in fit- ting participants’ BH data from this experiment. The volume of the last inhalation pre-BH, time to subjective breakpoint, and breath-to- breath calculated respiratory exchange ratio (RER) were identified as the most influential combination of key variables that improved PAO2 model fit (from R2 = 0.8591 to R2 = 0.8840). Clustering methods cou- pled with two sample t-tests or ANOVA were then used to identify survey responses most correlated to inter-BH similarities. These were barometric pressure, age, height, weight, resting HR, smoker/ freed- iver/ scuba experience, and weekly frequency of engaging in physical exercise. The model was validated on testing data from an experienced free-diver, from non-rebreathing trials of a sample of the participants, and from simulated dives of 5 participants from research in the En- vironmental Physiology Department of Karolinska in 1994 [1]. It has been suggested that the presented model can be a valuable tool in de- veloping safer free diving practices. Furthermore, interesting trends in continuous HR, starting PACO2 values, and O2 consumption were observed and analyzed using statistical analysis. Findings were dis- cussed with connection to the underlying physiological principles that might explain the results obtained. iv Acknowledgements I would like to thank my supervisors, Dr. Mikael Gennser, and Pro- fessor Pawel Herman for providing me both guidance when I needed help, and their support when I needed encouragement. Their feed- back allowed me not only to feel confident pursuing this project, but also to find the best structure to make the most of this opportunity. I would also like to thank Xiaogai Li, Brynar Hilmarsson, Linda Bjorgvins- dottir, Puja Romulus, Anna Pogosian, Quiantailang Yuan, and Quentin Chometon, for their review and input on my thesis. Lastly, thanks to Dillon Arey, with whom I discussed safer free-diving and what we as engineers might be able to do about it. Contents 1 Introduction 2 1.1 Shallow water blackout . 2 1.2 Literature review of BH modeling in humans . 2 1.3 Aim . 3 2 Methods 4 2.1 Pre-BH procedure . 4 2.2 BH experimental set-up . 4 2.3 Post-BH variable determination . 5 2.4 Data analysis . 5 3 Results 10 3.1 Explicit model of PAO2 and PACO2 during BH: itera- tion 1 . 10 3.2 Statistical testing to identify key variables for the model . 16 3.3 Incorporating key variables into model: iteration 2 . 16 3.4 Incorporating survey responses in model: iteration 3 . 18 3.5 Model validation . 20 3.6 HR, O2 consumption, and starting PACO2 influence on BH................................ 24 4 Discussion 26 4.1 Methodology . 26 4.2 Model evaluation . 27 4.3 Safe dive planning tool . 28 4.4 Indicator for maximum voluntary BH length . 29 4.5 Characterizing the training effect of repeated BH . 30 4.6 Machine learning with small, uncontrolled sample pop- ulation . 30 4.7 Future work . 31 v vi CONTENTS 5 Conclusion 33 Bibliography 34 A Background Information and Literature Study 37 A.1 Gas exchange in the lungs . 37 A.1.1 Pulmonary system basics . 37 A.1.2 Oxygen in the body . 38 A.1.3 Carbon dioxide in the body . 39 A.2 Physical laws that govern physiological gas exchange . 39 A.2.1 Hydrostatic pressure, Boyle’s Law, Charles’s Law 39 A.2.2 The diving reflex . 41 A.2.3 The Haldane and Bohr effects . 42 A.3 Breath-hold influence on gas-exchange in the literature . 43 A.3.1 Mechanical changes . 43 A.3.2 Hemodynamic changes . 43 A.3.3 Physiological changes . 44 A.4 Modeling gas exchange . 46 A.4.1 Artificial neural networks . 46 A.4.2 Relevant clustering techniques . 47 A.4.3 Existing physiological models . 49 B Final model inputs, outputs, and respective code 52 C Additional figures 53 CONTENTS 1 Abbreviations and Shorthand Abbreviation Concept BH Breath-hold HR Heart rate O2 Molecular oxygen PO2 Partial pressure of oxygen PAO2 Alveolar partial pressure of oxygen CO2 Molecular carbon dioxide PCO2 Partial pressure of carbon dioxide PACO2 Alveolar partial pressure of carbon dioxide LOC Loss of consciousness IVC Inspiratory vital capacity SD Standard deviation GLM General linear model NLM Nonlinear model atm Atmosphere mmHg Milimeters of mercury kPa Kilopascal SpO2 Peripheral oxygen saturation HD High dimensional SOM Self organizing map RER Respiratory exchange ratio SHLP Single hidden layer perceptron Chapter 1 Introduction 1.1 Shallow water blackout Shallow water blackout, as referred to in this report, is a condition that causes loss of consciousness in freedivers upon returning to the surface after performing apnea (holding his/her breath) under water, not to be confused with the original definition of blurred consciousness as a result of hypercapnia in closed circuit oxygen breathing. In many cases around the world every year, experienced swimmer or not, shallow water blackout has been fatal [2]. The deeper one dives, the higher the ambient pressure experienced, resulting in greater internal partial pressures of gases [3]. Loss of consciousness occurs when the brain experiences hypoxia, which usually occurs when the partial pressure of oxygen in the lungs drops below 4kPa [4]. With the aid of increased ambient pressure during diving, a person might feel and behave fine, but upon returning to the surface, the decreased ambient pressure and sudden drop of partial pressure of oxygen, also known as hypoxia of ascent, can cause the swimmer to lose consciousness before surfacing and to drown. 1.2 Literature review of BH modeling in hu- mans Modeling breath-hold (BH) processes in humans in the literature to date has been attempted on a variety of topics, including hemoglobin dissociation curves [5] [6], arterial and peripheral saturation over time 2 CHAPTER 1. INTRODUCTION 3 [6][7][8], lung compression [9], and vascular pressures [10]. These models range in ability to generalize well to larger populations. Those that may generalize well tend to be very complex, requiring many bio- logical inputs. There also exists many clinical studies that have studied partial pressures during BH [11][12][13][14][15][16]; however, only im- plicit understandings of metabolism can be derived from these studies. There exists a gap in the literature of an explicit, generalized relation- ship that grounds maximal BH in humans and explains inter- and in- trapersonal differences in BH. A continuous, personalized BH model of PAO2 and PACO2 levels would be very useful for free divers in or- der to plan safer dives. This brings the question, can the partial pres- sure of oxygen and carbon dioxide be numerically modeled accurately enough from non-invasive surface variables (without continuous mea- surement) to predict end-tidal alveolar gas pressures? 1.3 Aim The aim of this thesis is to create an individualized model of O2 uptake and CO2 generation over time during prolonged voluntary apnea, and extrapolate it for conditions at depth. Furthermore, it is desired that individual details about a person can be clustered informatively to un- derstand their influence on the model, so that it may be used by any person to guide safer free diving practices. The process to fulfill this aim begins with determining whether or not there exists a universally characterized relationship of gas partial pressure changes during hu- man BH. Then, the inter and intra-individual factors that are most in- fluential on oxygen metabolism during BH for this sample population must be evaluated. Finally, the robustness of the blood gas model will be expanded by including these dependencies. Chapter 2 Methods 2.1 Pre-BH procedure Each participant filled in a consent form and questionnaire, reporting their height, weight, gender, resting heart rate, previous 2 hour diet and caffeine intake, smoking/freediving/scuba experience level, and perceived fitness status. Then his/her inspiratory vital capacity (IVC) was measured using a MIR handheld Spirobank three times and the largest value was used. The most relevant atmospheric pressure in Stockholm was recorded at the beginning of each participant’s testing. 2.2 BH experimental set-up Participants breathed in a mouthpiece through a three-way plexi-valve that allowed for sampling from a Datax NormoCap 200 gas analyzer and also attachment to a 3.0L rebreathing bag.