Health and Social Development, Faculty of (Okanagan)
Health and Exercise Sciences, School of (Okanagan)
DSpace
UBCO
Bird, Jordan Douglas
Master of Science - MSc
University of British Columbia
Current understanding of diaphragm blood flow (Q̇DIA) is almost exclusively informed by animal models with no viable method of routinely interrogating Q̇DIA in humans. We aimed to assess the feasibility, validity, and reliability of contrast-enhanced ultrasound (CEUS) as a means of quantifying Q̇DIA within the medial costal diaphragm of humans during graded inspiratory pressure threshold loading (ITL). We hypothesized that 1) Q̇DIA would linearly increase with respiratory muscle work and 2) that there would be good test-retest reliability and inter-observer reliability of CEUS as a measure of Q̇DIA. Quantitative CEUS perfusion imaging of the medial diaphragm was performed in sixteen healthy participants (10M/6F; Age 28±5 yrs; BMI 22.8 ± 2.0 kg/m2) at the 8-10th right intercostal space. Respiratory muscle work was modulated by four 5-minute bouts of ITL (baseline, stage 1, stage 2, stage 3). Nasogastric balloon catheters were used to collect transdiaphragmatic pressures and calculate diaphragmatic work. CEUS was performed during a constant-rate intravenous infusion of lipid-stabilized microbubbles during the last two minutes of each level of ITL. CEUS images were acquired after a destruction-replenishment sequence and time-intensity data was measured from regions of interest placed over the diaphragm to determine Q̇DIA. %MIP for baseline (BL), stage 1 (S1), stage 2 (S2), and stage 3 (S3) were 0.7 ± 0.4, 9.3 ± 0.4, 17.6 ± 0.4, and 25.2 ± 0.4%, respectively. Q̇DIA significantly increased with ITL (3.1 ± 3.1 vs, 6.9 ± 3.6 vs, 11.0 ± 4.9 vs, 13.5 ± 5.4 AU·s-1) at BL, S1, S2, and S3 (all P < 0.01). There was a reproducible linear relationship between Q̇DIA and diaphragmatic work from day-to-day. There was good-to-excellent test-retest (ICC = 0.86 [0.77, 0.92]; P < 0.0001) and inter-observer (ICC = 0.93 [0.90, 0.95]; P < 0.0001) reliability for Q̇DIA. Mean differences with 95% limits of agreements for Q̇DIA were 0.6 ± 6.1 AU·s⁻¹ and 0.1 ± 4.4 AU·s⁻¹ for test-retest and inter-observer reliability, respectively. These data demonstrate that CEUS-derived measurements of Q̇DIA have valid physiological underpinnings and are reliable day-to-day and observer-to-observer. CEUS appears to be a viable and minimally invasive method of assessing Q̇DIA in health humans.
1 QUANTIFYING DIAPHRAGM BLOOD FLOW WITH CONTRAST-ENHANCED ULTRASOUND IN HEALTHY HUMANS: FEASIBILITY, VALIDITY, AND RELIABILITY by Jordan Douglas Bird B.HSc., Mount Royal University, 2019 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE in THE COLLEGE OF GRADUATE STUDIES (Health & Exercise Sciences) THE UNIVERSITY OF BRITISH COLUMBIA (Okanagan) September 2023 © Jordan Douglas Bird, 2023 2 The following individuals certify that they have read, and recommend to the College of Graduate Studies for acceptance, the thesis entitled: Quantifying Diaphragm Blood Flow with Contrast-Enhanced Ultrasound in Healthy Humans: Feasibility, Validity, and Reliability submitted by Jordan D. Bird in partial fulfilment of the requirements for the degree of Masters of Science in Health & Exercise Sciences Examining Committee: Dr. Glen Foster, School of Health & Exercise Sciences, UBC Okanagan Supervisor Dr. Neil Eves, School of Health & Exercise Sciences, UBC Okanagan Supervisory Committee Member Dr. Paolo Dominelli, Department of Kinesiology & Health Sciences, University of Waterloo Supervisory Committee Member Dr. Jordan Guenette, School of Kinesiology, UBC Vancouver University Examiner 3 Abstract Current understanding of diaphragm blood flow (Q̇DIA) is almost exclusively informed by animal models with no viable method of routinely interrogating Q̇DIA in humans. We aimed to assess the feasibility, validity, and reliability of contrast-enhanced ultrasound (CEUS) as a means of quantifying Q̇DIA within the medial costal diaphragm of humans during graded inspiratory pressure threshold loading (ITL). We hypothesized that 1) Q̇DIA would linearly increase with respiratory muscle work and 2) that there would be good test-retest reliability and inter-observer reliability of CEUS as a measure of Q̇DIA. Quantitative CEUS perfusion imaging of the medial diaphragm was performed in sixteen healthy participants (10M/6F; Age 28±5 yrs; BMI 22.8 ± 2.0 kg/m2) at the 8-10th right intercostal space. Respiratory muscle work was modulated by four 5-minute bouts of ITL (baseline, stage 1, stage 2, stage 3). Nasogastric balloon catheters were used to collect transdiaphragmatic pressures and calculate diaphragmatic work. CEUS was performed during a constant-rate intravenous infusion of lipid-stabilized microbubbles during the last two minutes of each level of ITL. CEUS images were acquired after a destruction-replenishment sequence and time-intensity data was measured from regions of interest placed over the diaphragm to determine Q̇DIA. %MIP for baseline (BL), stage 1 (S1), stage 2 (S2), and stage 3 (S3) were 0.7 ± 0.4, 9.3 ± 0.4, 17.6 ± 0.4, and 25.2 ± 0.4%, respectively. Q̇DIA significantly increased with ITL (3.1 ± 3.1 vs, 6.9 ± 3.6 vs, 11.0 ± 4.9 vs, 13.5 ± 5.4 AU·s-1) at BL, S1, S2, and S3 (all P < 0.01). There was a reproducible linear relationship between Q̇DIA and diaphragmatic work from day-to-day. There was good-to-excellent test-retest (ICC = 0.86 [0.77, 0.92]; P < 0.0001) and inter-observer (ICC = 0.93 [0.90, 0.95]; P < 0.0001) reliability for Q̇DIA. Mean differences with 95% limits of agreements for Q̇DIA were 0.6 ± 6.1 AU·s-1 and 0.1 ± 4.4 AU·s-1 for test-retest and inter-observer reliability, respectively. These data demonstrate that 4 CEUS-derived measurements of Q̇DIA have valid physiological underpinnings and are reliable day-to-day and observer-to-observer. CEUS appears to be a viable and minimally invasive method of assessing Q̇DIA in health humans. 5 Lay Summary The current state of knowledge for diaphragm blood flow (Q̇DIA) is almost exclusively informed by work in animals with almost all methods being highly invasive and not conducive to routine use in humans. This poses two issues for translation to human understanding as 1) there are incongruencies in function and structure that preclude direct comparison between species and 2) highly invasive measurements in humans can put participants at unnecessary risk. This gap in understanding warrants a reliable method of assessing Q̇DIA in humans. In our study, we tested whether we could get minimally invasive and reproducible measurements of Q̇DIA in humans. We showed that it is feasible, valid, and reliable to measure Q̇DIA across a range of respiratory efforts in humans. We highlight the associated limitations of the technique and future applications for basic science (e.g., exercise) and clinical medicine (e.g., patients on mechanical ventilation). 6 Preface This thesis contains original data collected in the Cardiopulmonary Laboratory for Experimental and Applied Physiology. Jordan Bird and Dr. Glen Foster were both responsible for the conception and design of the experiment. Jordan Bird, Megan Lance, Ty Banser, Conan Shing, Paul Cotton, Scott Thrall, Mohammad Soltani, Brooke Shafer, and Paige Witt contributed to the set up and collection of experimental data. Jordan Bird was responsible for complete data analyses with Megan Lance providing parallel data analysis for inter-observer reliability measurements; both under the technical guidance of Dr. Glen Foster. The writing in the thesis document was completed by Jordan Bird with the editorial assistance of Dr. Glen Foster. Ethical approval for this project was received from the University of British Columbia Clinical Research Ethics Board (H22-01082). This work was supported by the Natural Sciences and Engineering Research Council of Canada and Mitacs through the Mitacs Accelerate program in collaboration with Kent Imaging Inc. 7 Table of Contents Abstract ...........................................................................................................................................3 Lay Summary .................................................................................................................................5 Preface .............................................................................................................................................6 Table of Contents ...........................................................................................................................7 List of Tables ................................................................................................................................11 List of Figures ...............................................................................................................................12 List of Equations ..........................................................................................................................14 List of Abbreviations ...................................................................................................................15 Acknowledgements ......................................................................................................................17 Chapter 1: Introduction ..............................................................................................................18 1.1 Introduction to the Literature Review ........................................................................... 18 1.2 Anatomy and Function of the Diaphragm .................................................................... 19 1.3 Overview of Diaphragm Blood Flow ........................................................................... 20 1.3.1 Mechanical Impediment............................................................................................ 21 1.3.2 Diaphragm Regional Blood Flow Heterogeneity ..................................................... 24 1.3.3 Other Factors Affecting Diaphragm Blood Flow ..................................................... 29 1.4 Measuring Diaphragm Blood Flow .............................................................................. 29 1.4.1 Radiolabeled & Fluorescent Microspheres ............................................................... 30 1.4.2 Drop Counters, Electromagnetic Flow Probes, and Doppler Flowmetry ................. 31 1.4.3 Intravital Microscopy ................................................................................................ 33 1.4.4 Near-Infrared Spectroscopy with Indocyanine Green (NIRS-ICG) ......................... 34 8 1.4.5 Contrast-Enhanced Ultrasound (CEUS) ................................................................... 36 Chapter 2: Specific Research Objectives and Hypotheses .......................................................39 2.1 Brief Background .......................................................................................................... 39 2.2 Research Objective ....................................................................................................... 40 2.3 Research Hypothesis ..................................................................................................... 40 Chapter 3: Methodology and Data Analysis .............................................................................41 3.1 Participants .................................................................................................................... 41 3.2 General Procedures ....................................................................................................... 41 3.3 Screening Procedures .................................................................................................... 42 3.3.1 Pulmonary Function Test .......................................................................................... 43 3.3.2 Diaphragm Thickness Measurements ....................................................................... 44 3.4 Experimental Procedures .............................................................................................. 44 3.5 Specific Methodology ................................................................................................... 48 3.5.1 Cardiovascular and Respiratory Measurements........................................................ 48 3.5.2 Esophageal and Gastric Balloon Catheterization ...................................................... 49 3.5.3 Inspiratory Pressure Threshold Loading ................................................................... 49 3.5.4 Contrast-Enhanced Ultrasound Measurements ......................................................... 50 3.6 Data Analysis ................................................................................................................ 51 3.6.1 Cardiorespiratory Parameters.................................................................................... 51 3.6.2 Transdiaphragmatic and Respiratory Muscle Pressures ........................................... 52 3.6.3 Ultrasound Cine Loops ............................................................................................. 52 3.7 Statistical Analysis ........................................................................................................ 53 3.7.1 Cardiorespiratory Statistical Analysis....................................................................... 54 9 3.7.2 Reliability Statistical Analysis .................................................................................. 54 Chapter 4: Results........................................................................................................................57 4.1 Participants .................................................................................................................... 57 4.2 Cardiorespiratory Data .................................................................................................. 59 4.2.1 Respiratory Measurements........................................................................................ 59 4.2.2 Respiratory Pressure Measurements ......................................................................... 63 4.2.3 Cardiovascular Measurements .................................................................................. 68 4.2.4 End-Expiratory Lung Volumes and Apnea Durations during CEUS Measurements 71 4.3 Blood Flow Measurements ........................................................................................... 72 4.4 Relationship between Q̇DIA and Respiratory Work ....................................................... 75 4.5 Reliability Data ............................................................................................................. 76 4.5.1 Test-Retest Reliability (Stability Reliability) ........................................................... 76 4.5.2 Inter-Observer Reliability (Objective Reliability) .................................................... 79 Chapter 5: Discussion ..................................................................................................................83 5.1 Main Findings ............................................................................................................... 83 5.2 Validity – Relationship Between Q̇DIA and Respiratory Muscle Work ........................ 83 5.3 Reliability – Test-Retest Reliability.............................................................................. 84 5.4 Reliability – Inter-Observer Reliability ........................................................................ 85 5.5 Sex Differences in Diaphragm Blood Flow .................................................................. 86 5.6 Methodological Considerations .................................................................................... 87 5.6.1 Respiratory Muscle Anatomy & Physiology Considerations ................................... 87 5.6.2 Ultrasound Measurement and Data Processing Considerations ............................... 89 5.6.3 Reliability and Error Considerations ........................................................................ 89 10 5.6.4 Sample Size Consideration ....................................................................................... 90 5.7 Utility and Future Applications .................................................................................... 91 5.7.1 Basic Science ............................................................................................................ 91 5.7.2 Human Performance ................................................................................................. 92 5.7.3 Clinical Medicine ...................................................................................................... 92 Chapter 6: Conclusion .................................................................................................................94 References .....................................................................................................................................95 Appendices ..................................................................................................................................112 Appendix A Individual Responses.......................................................................................... 112 A.1 Q̇DIA vs. MIP ........................................................................................................... 113 A.2 Q̇DIA vs. PDIMAX ...................................................................................................... 114 A.3 Q̇DIA vs. PTPDIA (Summed) ..................................................................................... 115 A.4 Q̇DIA vs. PTPDIA (Averaged) ................................................................................... 116 Appendix B Supplementary Sex Differences Data ................................................................. 117 Appendix C Instrumentation & Calibrations .......................................................................... 120 11 List of Tables Table 1.1. Summary of studies that reported regional heterogeneity of Q̇DIA. ............................. 27 Table 4.1. Participant anthropometrics, pulmonary function data, and diaphragm ultrasound parameters. .................................................................................................................................... 58 Table 4.2. Summary of respiratory and control data averaged over the 5 minutes of inspiratory pressure threshold loading for both experimental days. ............................................................... 61 Table 4.3. Summary of respiratory pressure and respiratory work data averaged over 5 minutes of inspiratory pressure threshold loading for both experimental days. ......................................... 65 Table 4.4. Summary of cardiovascular data averaged over the 5 minutes of inspiratory pressure threshold loading for both experimental days. .............................................................................. 69 Table 4.5. Group end-expiratory lung volumes and apnea durations during CEUS measurements....................................................................................................................................................... 71 Table 4.6. SEM and TEM for Test-Retest Reliability .................................................................. 78 Table 4.7. SEM and TEM for Inter-Observer Reliability. ............................................................ 81 Table 6.1. Sex differences in CEUS blood flow variables as an average of both days. ............. 119 12 List of Figures Figure 1.1. Influence of mechanical impediment on diaphragm blood flow. ............................... 22 Figure 1.2. Influence of breathing frequency on diaphragm blood flow. ..................................... 23 Figure 1.3. Schematic of the diaphragmatic circulation. .............................................................. 32 Figure 1.4. Cross-sectional ultrasound image of the left 7th intercostal space............................. 35 Figure 1.5. Annotated ultrasound image of the diaphragm. ......................................................... 37 Figure 3.1. Schematic of pre-screening and experimental days. .................................................. 42 Figure 3.2. Experimental setup and instrumentation. ................................................................... 45 Figure 3.3. Day-to-day respiratory signal waveforms for each experimental trial. ...................... 47 Figure 4.1. Group respiratory parameters throughout inspiratory loading trials. ......................... 62 Figure 4.2. Grouped respiratory pressure parameters throughout inspiratory loading trials. ....... 67 Figure 4.3. Group cardiovascular parameters throughout inspiratory loading. ............................ 70 Figure 4.4. Destruction-replenishment curves for diaphragm and liver blood flows. .................. 74 Figure 4.5. Diaphragm blood flow measurements during graded increases in inspiratory pressure threshold loading. .......................................................................................................................... 75 Figure 4.6. Linear mixed models for the relationship between Q̇DIA and respiratory pressures. .. 76 Figure 4.7. Intraclass Correlation Coefficient Plots for Test-Retest Reliability. .......................... 77 Figure 4.8. Bland-Altman Plots for Test-Retest Reliability. ........................................................ 79 Figure 4.9. Intraclass Correlation Coefficient Plots for Inter-Observer Reliability. .................... 80 Figure 4.10. Bland-Altman Plots for Inter-Observer Reliability. ................................................. 82 Figure 6.1. Destruction-replenishment curves for diaphragm and liver blood flows stratified by sex. .............................................................................................................................................. 117 13 Figure 6.2. Linear mixed models for the relationship between Q̇DIA and respiratory pressures when compared by sex. ............................................................................................................... 118 Figure 6.3. Inspiratory pressure threshold loader and its pressure-flow relationship across the physiological range. .................................................................................................................... 120 Figure 6.4. Time to video intensity plateau with continuous infusion of Definity microbubbles...................................................................................................................................................... 120 14 List of Equations Equation 1. Tension-Time Index of the Diaphragm ...................................................................... 21 Equation 2. Diaphragm Thickening Fraction ............................................................................... 44 Equation 3. Tension-Time Index of Respiratory Muscles ............................................................. 52 Equation 4. Destruction-Replenishment Kinetics ......................................................................... 53 Equation 5. Standard error of measurement ................................................................................ 55 Equation 6. Technical error of measurement ............................................................................... 55 Equation 7. Limits of agreement ................................................................................................... 55 Equation 8. Fick Equation ............................................................................................................ 83 15 List of Abbreviations BP Blood pressure CEUS Contrast-enhanced ultrasound CV Coefficient of variation DBP Diastolic blood pressure DLCO Diffusion capacity of the lung to carbon monoxide EELV End expiratory lung volume fB Breathing frequency FEV1 Forced expiratory volume in one second FRC Functional residual capacity FVC Forced vital capacity ICC Intraclass correlation coefficient ITL Inspiratory pressure threshold loading LOA 95% limits of agreement MAP Mean arterial pressure MBF Microvascular blood flux MBV Microvascular blood volume MIPFRC Maximal inspiratory pressure at functional residual capacity MIPRV Maximal inspiratory pressure at residual volume NIRS-ICG Near-infrared spectroscopy with indocyanine green Pab Abdominal pressure PCO2 Partial pressure of carbon dioxide PDIMAX, FRC Maximal transdiaphragmatic pressure at functional residual capacity PDIMAX, RV Maximal transdiaphragmatic pressure at residual volume PETCO2 Partial pressure of end-tidal carbon dioxide PETO2 Partial pressure of end-tidal oxygen PFT Pulmonary function test P̅DIA Mean inspiratory transdiaphragmatic pressure P̅ESO Mean inspiratory esophageal pressure P̅GAS Mean inspiratory gastric pressure 16 P̅M Mean inspiratory mouth pressure Pth Thoracic pressure PTPDIA Pressure-time product of the diaphragm PTPESO Pressure-time product of the esophageus PTPM Pressure-time product of the mouth Q̇DIA Diaphragm blood flow Q̇INT Intercostal blood flow Q̇LIV Liver blood flow ROI Region of interest RV Residual volume SBP Systolic blood pressure SD Standard deviation SEM Standard error of measurement SpO2 Pulse oxyhemoglobin saturation TEM Technical error of measurement TF Thickening fraction TLC Total lung capacity TTIDIA Tension-time index of the diaphragm TTIRESP Tension-time index of the respiratory muscles VCDIA Vascular conductance of the diaphragm VCLIV Vascular conductance of the liver V̇E Minute ventilation V̇O2max Maximal oxygen uptake WOB Work of breathing 17 Acknowledgements I’d like to recognize that this thesis was contrived, executed, and written on the traditional, ancestral, unceded territory of the Syilx Okanagan Nation. To Dr. Glen Foster, thank you for your unwavering support through this process and for taking a chance on me back in 2020. Hopefully the first of many collaborations. It has been a privilege to work with you. Thank you to my committee members Drs Neil Eves and Paolo Dominelli for your guidance and scholarly support throughout my studies and to Dr. Jordan Guenette for contributing as my university examiner. Thank you also to the members of CPLEAP for their long hours assisting me with data collection. Your continued help and support made this study possible. To quote Dr. Foster’s PhD dissertation dedication, without the support of the people around me, “this would have been a difficult task.” To Dr. Trevor Day, thanks for coffee. Just happy to be here. Finally, I would like to thank my parents, Doug and Karen, and siblings for their continued support through my academic pursuits and otherwise. 18 Chapter 1: Introduction 1.1 Introduction to the Literature Review In 1919, eventual Nobel laureate Shack August Steenberg Krogh (1874–1949) observed that skeletal muscles have a regular distribution of patent capillaries.1 He noted that the diaphragm had the most capillaries in close proximity to one another, suggesting abundant microcirculation compared to other skeletal muscles. Rous (another eventual Nobel laureate) and Gilding followed up on this work in 1929 by intravenously administering diffusible dyes into rabbits to determine the extent of muscle perfusion during both eupnea and reduced blood bulk (i.e., hemorrhage).2 They found that the diaphragm and intercostal muscles were especially well stained under both conditions and lead them to conclude that that respiratory muscle requires a high degree of blood flow that is well maintained even when blood volume is challenged. A few years after, Anrep, Cerqua & Samaan (1934) found that diaphragm blood flow (Q̇DIA) in dogs decreased during muscular contraction (i.e., inspiration) by measuring the rate of blood flow into the diaphragm using a hot-wire anemometer.3 Very little work on Q̇DIA occurred over the next few decades with the exception of Mott (1953) who first observed that artificial respiration decreased Q̇DIA while studying hemorrhage in dogs4, Bullard and Funkhouser (1962) who found that Q̇DIA was high in deeply hibernating ground squirrels,5 and Biscoe and Bucknell (1963) who mapped out the diaphragmatic circulation in cats.6 It was not until Abraham Rudolph introduced the radiolabeled microsphere technique in 1967 for the determination of regional blood flow7 that Q̇DIA was rigorously interrogated in the 1970s through the 1990s. Many of the landmark studies in Q̇DIA came during this time including the work of Robertson8–10 and Rochester11–13 in anesthetized dogs and Manohar with exercising horses.14–19 In the early 2000s, Guenette and colleagues introduced the use of near-infrared spectroscopy with indocyanine green (NIRS-ICG) 19 for determining superficial respiratory muscle blood flow in humans.20 This work highlights the unmet need for a feasible way to evaluate Q̇DIA in humans as the diaphragm is the primary respiratory muscle with a litany of animal studies that require human translation. The purpose of this literature review is to (1) summarize the current state of knowledge on Q̇DIA over the past 100 years, (2) outline current tools/techniques permitting the measurement of Q̇DIA whilst highlighting associated limitations and methodological challenges, and (3) to consider the utility of contrast-enhanced ultrasound (CEUS) as a novel approach for measuring the relative perfusion of the medial costal diaphragm. 1.2 Anatomy and Function of the Diaphragm Principally recognized as the primary inspiratory muscle for lung ventilation, the diaphragm dually functions as a partition and muscle pump.21 As a partition, the diaphragm is typically separated into three regions which all insert into the central tendon: 1) the sternal region originating from the posterior portion of the xyphoid process and xiphisternal synchondrosis; 2) the costal diaphragm originating from the lower rib cage (ribs 7-12 and corresponding costal cartilage); and 3) the crural region originating from the intervertebral fibrocartilages of the first three lumbar vertebrae and the arcuate ligaments overlying the aorta, vena cava, and psoas major and quadratus lumborum muscles.21 To act as a muscle pump, the diaphragm contracts and moves caudally to generate positive abdominal pressure (Pab) and negative thoracic pressure (Pth) with resultant inspiratory airflow. While seemingly separate functions, partitioning prevents movement of the abdominal organs into the thoracic space while also reducing compartment volume and improving the efficacy of positive Pab and negative Pth generation. This muscle pump functionality is crucial for the generation of persistent, non-fatiguing negative Pth that is required 20 to sustain the high duty cycle (i.e., time active versus inactive) of continuously active lung ventilation. 1.3 Overview of Diaphragm Blood Flow As the diaphragm is active throughout the life of an individual and represents the sole skeletal muscle essential for life,22 it is critical that diaphragm function is maintained. To achieve this, the circulation must ensure an adequate supply of oxygen and metabolic substrates to continuously support the contractile activity of the diaphragm as an imbalance in nutritive supply and demand23 may lead to diaphragm failure, patient morbidity and mortality.24 As a result of its importance, Q̇DIA has been extensively studied in hamsters,25 rats,26–64 squirrels,5 rabbits,2,65–71 cats,6,72 dogs,3,4,8–13,24,73–137 monkeys,138 sheep,139–147 lambs,148–151 pigs,75,152–156 and horses14–19,157 from as far back as the early 20th century. However, due to its inaccessibility in intact humans, there has been only a single study on human Q̇DIA to date. In 1989, Aubier and colleagues looked at the effects of dopamine on left inferior phrenic venous effluent in three intubated and mechanically ventilated acute respiratory failure patients.158 While they found that dopamine infusion transiently increased Q̇DIA for up to 45 minutes, the method of access (i.e., venous catheterization advanced from the right femoral vein through the inferior vena cava and horizontally through the left inferior phrenic vein) is extremely invasive, technically challenging, and not conducive to routine study. Even so, the prevailing sentiment is that there is an “inability”159 or “impossibility”52 to assess Q̇DIA in humans despite the potential applications and widespread physiological and clinical problems that human diaphragmatic perfusion could help address. Thus, our current understanding of Q̇DIA is informed almost exclusively by animal work with concomitant species-dependent75 variability in diaphragmatic structure and function.21,160 21 1.3.1 Mechanical Impediment The phenomenon of phasic oscillation of Q̇DIA with inspiration and expiration have been documented since the 1930s.3,76 During inspiration, Q̇DIA inflow becomes reduced and with expiration, it recovers or increases above pre-inspiratory levels.3,76,98 These phasic Q̇DIA observations gave rise to the diaphragm tension-time index (TTIDIA)76 which assesses the pressure generation and time under tension of the diaphragm (Equation 1). Equation 1. Tension-Time Index of the Diaphragm 𝑇𝑇𝐼𝐷𝐼𝐴 = %𝑃𝐷𝐼𝑀𝐴𝑋 ×𝑇𝐼𝑛𝑠𝑇𝐼𝑛𝑠 + 𝑇𝐸𝑥𝑝 Early reports suggested that a TTIDIA of 0.2 – 0.3 in anesthetized mechanically ventilated dogs maximizes Q̇DIA after which Q̇DIA progressively falls with further increases in TTIDIA.24,76 At a TTIDIA greater than 0.2 – 0.3, there is a post-contraction hyperemia that increases as a direct function of TTIDIA..76 At TTIDIA ≈ 0.75, Q̇DIA approaches zero as high intramuscular pressures developed during inspiration almost fully impede blood flow.76 This concept of TTIDIA impeding Q̇DIA above 0.2 is still routinely accepted and standard practice within joint American Thoracic Society and European Respiratory Society guidelines,161 however, consideration into its validity for Q̇DIA is warranted. First, cessation of blood flow was concluded from sustained contractions lasting 30 s which would be supraphysiologic inspiratory durations.76 Combine that with post-contraction hyperemia during relaxation to compensate for any inhibition of Q̇DIA during contraction and Q̇DIA is likely maintained.80 Further, most of the studies equating TTIDIA and Q̇DIA measurements used the venous circulation which is the most compliant and collapsible aspect of the diaphragm microcirculation24,76,89 likely overestimating when occlusion occurs. Next, the internal thoracic vein and phrenic arteries and veins24,76,98,103 22 show mechanical impediment, but not the azygos vein (drainage conduit for intercostal veins).103 This may be due to the diaphragm draining progressively more blood through the intercostal veins at high TTIDIA because of prolonged periods of high Pab limiting effective drainage of the phrenic and internal thoracic veins. The intercostal veins also likely account for ~60% of Q̇DIA while the phrenic and internal thoracic veins account for ~25% and 15%, respectively96,103 suggesting Q̇DIA might only be regionally impeded. Finally, TTIDIA does not account for contraction frequency. As Q̇DIA is proportional to the energy expenditure of the diaphragm, Q̇DIA changes in conjunction with contraction frequency (i.e., breathing frequency).79 The original TTIDIA impediment work had contractions of 2/min76 while other commonly cited work supporting TTIDIA impediment ranges from 50/min to 160/min79,80 which deviates from the normal human physiologic range of 10-60 breaths/minute.162 Other work has used 10-30/min24,84,89,98,100,103,109,112 but come from less cited literature and do not necessarily support the 0.2 TTIDIA occlusion thesis. While total Q̇DIA appears to be uniquely related to TTIDIA up to values of 0.15-0.20,8,9,11–13,76 the utility of TTIDIA to ascertain Q̇DIA impediment may be limited beyond that threshold.163 Figure 1.1. Influence of mechanical impediment on diaphragm blood flow. Relationships between diaphragm blood flow and duty cycle (A), percent of transdiaphragmatic pressure (B), and the tension-time index (C). Diaphragm blood flow was measured by Doppler flow probes to the left inferior phrenic artery. Image created with data from Hu et al., (1990, 1992)102,103. 23 It is perhaps more useful to consider the individual relationships Q̇DIA has with maximum transdiaphragmatic pressure, duty cycle (Figure 1.1), and breathing frequency (Figure 1.2). Q̇DIA is likely linearly related to %PDIMAX (Figure 1.2B),98,103,112 but there are also reports of Q̇DIA not changing above 10% PDIMAX.100 Next, Q̇DIA is a parabolic function of duty cycle with Q̇DIA maximized at 0.5.76,84,100,103,112 These data are within the normal human breathing frequency range and are consistent between the internal thoracic veins and phrenic arteries and veins.103 However, other literature suggests that Q̇DIA increases linearly with duty cycle in the azygous vein103 while other reports espouse that global Q̇DIA decreases with increasing duty cycles.24,80 It is worth noting that the studies that found that global Q̇DIA decreases with duty cycle was based on contractions of 50-72/min with either negative pleural pressure or positive abdominal pressure80 and 30 second contractions which are supraphysiologic.24 Finally, Q̇DIA is likely exponentially related to breathing frequency from 10-60/min.8,164 This is not fully known however as few studies have assessed Q̇DIA and breathing frequency at more than two timepoints and the only other study with more than two timepoints has suggested a linear relationship.9 It is worth noting though that of all the studies that have looked at both Q̇DIA and breathing frequency, the slopes of how Q̇DIA Figure 1.2. Influence of breathing frequency on diaphragm blood flow. Abbreviations: CO2, carbon dioxide; Gas, mixed CO2 and O2 stimuli; IR, inspiratory resistance; Stim, electrical stimulation of the phrenic nerve. 24 changes as a percentage with increased breathing frequency are all anecdotally similar (Figure 1.2) despite starting at markedly different breathing frequencies. This suggests an exponential relationship as a progressive dampening of slopes with increased breathing frequency would more so support a linear relationship between Q̇DIA and breathing frequency. Until such times as this can be properly addressed, both linear or exponential relationships will be supported in the literature. As a final note, O2 extraction plays a role in determining Q̇DIA based on the Fick equation and thus, both linear or exponential relationships are possible and require future investigation. Based on the relationships described in Figures 1.1 and 1.2, it is reasonable to suggest that a given TTIDIA obtained from a different combination of %PDIMAX and duty cycle would not be iso-Q̇DIA. It is more likely that Q̇DIA is mainly determined by %PDIMAX and modulated by duty cycle102 while fatigue might be more closely tied to higher duty cycles that do not permit enough nutritive inflow or outflow to support higher PDIs and breathing frequencies. As such, studies looking to isolate the effects of a perturbation on Q̇DIA should try to control for these factors individually and not as a collective index. 1.3.2 Diaphragm Regional Blood Flow Heterogeneity Historically, it was thought that the costal and crural diaphragm were two separate, functionally distinct muscles with differing embryological origins, cervical innervations, and fiber type composition.21 These assertions have subsequently been suggested incorrect as the costal and crural diaphragm are derived from the same embryological origins, have considerable overlap of cervical segmental innervation, and have similar mixed fiber type compositions within a given species.21 However, the proportion and caliber of each fiber type varies across species.21 With 25 that in mind, it is perhaps surprising that there is heterogenous blood flow between the costal and crural regions of the diaphragm16,25,153,156,165,35,55,57,128,132,133,148,150 with a further heterogeneity within the costal diaphragm.25,35,50,55,57,107,165 As such, work examining heterogeneity is conflicting and there is no agreement on greatest regional Q̇DIA. Under conditions of spontaneous breathing and increased WOB (e.g., exercise,16,17,25,35,165 hyperpnea,150 inspiratory resistance119,153,156), there is marked variability in blood flow to the costal and crural diaphragm regions (Table 1.1). These disparate findings may be due to several factors including species differences, age, and possible sampling bias. Smaller mammals have greater energy consumption (~6x more) compared to humans21 which in part explains the increased basal Q̇DIA in rats55,57 and hamsters25 compared to larger animals. Next, age may play a role in Q̇DIA distribution as mechanical coupling of the costal diaphragm muscle to the rib cage is less efficient at expanding the chest in newborns compared to adults.166 This would point towards newborn mammals having to rely more on crural function to drive inspiration which has been found in lambs and piglets.148,150,153,156 Investigation into lifespan dynamics of regional heterogeneity may help to reconcile this literature in the future. Further, newborn mammals also need a higher diaphragmatic V̇O2 to support a higher minute ventilation and whole-body oxygen uptake150,167 supporting higher Q̇DIA in newborns compared to older mammals. As minute ventilation decreases with age, Q̇DIA would likely diminish over time which has been found in lambs.148,150 Lastly, Manohar’s work14,18 involved horses with large areas of costal muscle. It is conceivable that the two studies sampled blood flow from different areas of the costal region (i.e., ventral, medial, dorsal) unaware of costal heterogeneity25,35,107 thereby making the results appear dichotomous or that there is considerable effect of training status.59,135 26 There have been nine studies that have explored heterogeneity of blood flow within the costal diaphragm (Table 1.1). These nine studies have provided little congruency which might be explained by boundary selections, energetics distribution, and body position. As the diaphragm lacks anatomical demarcations between the three aspects of the costal region, there may be nuance in selecting boundaries dependent on methodology of individual researchers. Methodological description of demarcation selection may help to standardize sectioning of the diaphragm moving forward. Next, species variability regarding total energetics distribution160,168 may lead to species-dependent heterogeneity. Lastly, body position and posture may alter regional perfusion despite assertions of gravitational independence.107 Any combination of these factors or others not highlighted may lead to conflicting results. While these factors prevent drawing any definitive conclusion, the medial costal diaphragm likely has the highest blood flow in many cases. This is supported by greater thickness and mass coupled with higher mass specific flow than other aspects of the costal diaphragm.22,168 Based on the regional Q̇DIA work to date, considerable variability exists for the magnitude of blood flow to the resting diaphragm both between and within species. Transposition to humans is further confounded as incongruencies arise with quadrupeds having different diaphragm orientations, compositions, and energy consumptions.21 As such, any purported conclusion drawn about regional Q̇DIA in humans would be spurious at best and requires future investigation. Thus, as the medial costal region is of primary importance in driving inspiratory mechanics and likely has the greatest metabolic demand, it would be clinically and experimentally useful to quantify medial costal Q̇DIA in humans. 27 Table 1.1. Summary of studies that reported regional heterogeneity of Q̇DIA. Study Species, N ANES, MV, Position Condition Diaphragm Blood Flow (ml/min/100g) Costal Crural Ventral Medial Dorsal Total Total Lockhat et al., (1985) Dogs, 7 Y, N, Supine SB 42 ± 9 40 ± 14 Soust et al., (1987) Lambs, 12 N, N, Standing SB 26 ± 2 34 ± 3 Manohar (1988) Horses, 10 N, N, Standing SB 19 km/h 25 km/h 32 km/h 13 ± 3 123 ± 12 190 ± 12 245 ± 18 13 ± 2 86 ± 10 117 ± 8 151 ± 14 Soust et al., (1989) Lambs, 6 N, N, Standing SB 7% CO2 36 ± 5 186 ± 13 42 ± 3 228 ± 18 Manohar (1990) Horses, 7 N, N, Standing SB 25 km/h 32 km/h 38 ± 12 230 350 24 ± 4 150 260 Brancatisano et al., (1991) Dogs, 8 Y, Y, Supine SB IR MV 10 16 5 22 32 6 17 25 9 Kawagoe et al., (1994) Dogs, 10 Y, Y, Supine SB IR 30 ± 7 42 27 ± 7 36 Sexton & Poole (1995) Rats, 20 N, N, Standing SB 70% V̇O2max 16 ± 1 77 ± 7 26 ± 3 135 ± 15 25 ± 3 127 ± 11 23 ± 3 105 ± 9 Sexton & Poole (1998) Hamsters, 27 N, N, Standing SB Exercise 36 40 41 53 41 60 52 65 Poole et al., (2000) Rats, 8 N, N, Standing Max exercise SB 10% O2; 8% CO2 430 120 560 340 110 520 400 160 560 280 130 420 Hsia et al., (2000) Dogs, 11 N, N, Standing SB 44.5 ± 6 24 ± 4 Johnson et al., (2002) Dogs, 6 N, N, Standing SB Light exercise Med exercise Heavy exercise 40 70 80 140 50 90 100 160 35 70 80 120 40 60 80 100 Uchiyama et al., (2006) Rabbits, 10 Y, Y, Supine PSV0 PSV6 PSV12 PCV6 PCV12 16 ± 10 8 ± 7 13 ± 9 8 ± 5 6 ± 3 19 ± 10 9 ± 7 14 ± 7 8 ± 5 6 ± 2 Davis et al., (2012) Rats, 41 Y, Y, Supine SB 30 min MV 6 hr MV 22 14 9 31 19 9 47 35 11 38 31 11 28 Study Species, N ANES, MV, Position Condition Diaphragm Blood Flow (ml/min/100g) Costal Crural Ventral Medial Dorsal Total Total Horn et al., (2020) Rats, 19 Y, Y, Supine SB Low PEEP Low PEEP + LAP SB High PEEP High PEEP + PTX 67 ± 12 48 ± 5 65 ± 6 49 ± 13 20 ± 3 36± 9 78 ± 10 52 ± 7 57 ± 6 74 ± 14 34 ± 8 52 ± 5 62 ± 6 44 ± 6 62 ± 12 57 ± 6 27 ± 2 52 ± 8 51 ± 11 39 ± 6 51 ± 14 37 ± 5 19 ± 4 23 ± 2 Horn et al., (2022) Rats, 10 Y, Y, Supine SB MV MV + 100% O2 SB MV + 100% O2 MV 53 ± 8 41 ± 14 17 ± 2 50 ± 3 20 ± 6 27 ± 5 81 ± 19 51 ± 15 23 ± 7 79 ± 16 26 ± 6 32 ± 6 52 ± 10 38 ± 4 29 ± 5 53 ± 6 40 ± 6 38 ± 8 31 ± 4 17 ± 3 13 ± 2 36 ± 3 16 ± 6 17 ± 4 Values are mean ± SEM. Abbreviations: ANES, anesthesia; CO2, carbon dioxide; O2, oxygen; IR, inspiratory resistance; LAP, laparotomy; MV, mechanical ventilation; PCV, pressure control ventilation; PEEP, positive end-expiratory pressure; PSV, pressure support ventilation; PTX, pneumothorax; SB, spontaneous breathing; V̇O2max, maximal oxygen consumption 29 1.3.3 Other Factors Affecting Diaphragm Blood Flow Reconciling and directly comparing the literature on diaphragm perfusion is messy as methodological differences often lead to divergent results. Factors such as hypotension and blood gas tensions also play a role in altering capillary blood flow and Q̇DIA as a whole.169 First, although there is not much work on blood pressure control and Q̇DIA, it has been demonstrated that hypotension decreases Q̇DIA77,78,83,93 while hyperperfusion/hypertension augments it.98 Considering blood pressure responses and cardiac output are seemingly two important factors involved in Q̇DIA regulation, consideration into how these parameters differ from study to study is warranted. Next, gas tensions are known to affect Q̇DIA. This is demonstrated by hypoxia30,64,73,77,110,134,144,153 and hypercapnia9,13,66,68,144,150,165 augmenting Q̇DIA while hyperoxia attenuates it.57 As such, it would be advisable to account for such factors when evaluating Q̇DIA literature as their variability can add unnecessary noise to interpretation. 1.4 Measuring Diaphragm Blood Flow To date, there are five techniques that have been routinely utilized to assess Q̇DIA. These techniques are radiolabeled microspheres, drop counters, electromagnetic flow probes/Doppler flowmetry, intravital microscopy, and NIRS-ICG. Of these techniques, only drop counters and NIRS-ICG have been used in humans. Radiolabeled microspheres are considered the gold standard technique with more than 70 papers assessing Q̇DIA under a variety of experimental conditions (e.g., pharmacological intervention, regional blood flow distribution within the diaphragm, blood flow with modulated work of breathing). However, radiolabeled microspheres require post-mortem dissection which is understandably a non-starter in human research. Next, drop counters, electromagnetic flow probes/Doppler flowmetry, and intravital microscopy were 30 introduced in the 1980s to monitor phrenic venous and arterial flows, respectively.63,76,84 While these techniques monitor partial diaphragmatic inflow or outflow,76,84,96,103 their implementation involves thoracotomy or laparotomy making them highly invasive. The most recent technique, NIRS-ICG, involves surface optodes being placed over a region of interest (ROI) to give a non-discriminative global assessment of blood flow to all tissues below the optodes up to a certain depth (usually 2 cm). While NIRS-ICG is the only technique currently feasible for routine human use, numerous drawbacks exist that limit its utility and interpretation. Herein we outline the origins and uses of the various techniques used to look at Q̇DIA. 1.4.1 Radiolabeled & Fluorescent Microspheres First introduced in 1967 by Rudolph and Heymann,7 the technique gained much popularity with the majority of Q̇DIA studies in animal models prescribing to the method over the last 50 years. When the microspheres are injected into the systemic circulation, their distribution is proportional to bulk blood flow as they become ensnared in the microvascular beds of tissue. While it is the most common blood flow measurement technique in the literature, radiolabeled microspheres have multiple caveats that need to be considered. Early validation work suggested that 1) blood flow measurements are similar irrespective of microsphere diameter (50, 15, 12, or 8 µm), 2) more than 400 total microspheres are needed within a tissue sample for accurate blood flow assessment, 3) the smaller microspheres are distributed more like red blood cells (8-15 µm), do not occlude vascular beds, and a greater number can be given, theoretically leading to more reliable blood flow measurements to smaller regions of tissue.170 Other work suggests that 10% of 15 µm microspheres bypass the systemic circulation in the dog and this figure increases with anaesthesia171 leading to preferential pooling. Temperature also 31 influences measurements as 1.6% of cardiac output (15 µm) bypassed the systemic circulation (i.e., arteriovenous anastomoses) under thermoneutral conditions but rose to ~22% (15 µm)172 or ~13% (50 µm)173 of cardiac output in warm environments. This may either result in microspheres being shunted across the diaphragm (if that is a possible phenomenon) or artificially inflate Q̇DIA as microspheres that bypassed other tissues may then be caught in the diaphragm with successive systemic circulation cycles. These values assume greater significance when it is recognized that capillary bypass would disproportionately affect some tissues and may be more present in some studies (e.g., exercise). Further, larger microspheres (25 µm) preferentially deposit to areas of higher flow compared to smaller microspheres (15 µm) which could influence measurements by as much as 10%.174 These limitations are all relatively minor and do not often warrant consideration, but there is the occasional study in the Q̇DIA literature that may not use enough microspheres33 or may use too small of microspheres for the animal studied.14 Nevertheless, radiolabeled microspheres remain the gold standard measurement despite the need for euthanasia after administration. 1.4.2 Drop Counters, Electromagnetic Flow Probes, and Doppler Flowmetry Drop counters, electromagnetic flow probes, and Doppler flowmetry are three methods of quantifying Q̇DIA that are conveniently grouped together in that they monitor either the arterial inflow or venous outflow from the left hemidiaphragm. Only a handful of studies have used each of these methods with the main advantage being the time resolution of the measurement that allows quantification of Q̇DIA over very short periods of contraction as well as within individual contraction-relaxations cycles. 32 Drop counters evaluate venous effluent from the left inferior phrenic vein.24,76,89,92,158 This technique in dogs (as first introduced by Bellemare et al., 1983)76 involves a left-side thoracotomy whereby the 7th and 8th ribs are removed to create a thoracic window. This costectomy is then followed by dissection of the intercostal muscles to expose the phrenic nerves and inferior vena cava. The inferior vena cava is perforated from the right and a catheter is introduced through the wall of the vena cava and advanced into the left inferior phrenic vein to monitor diaphragm effluent. To date, the only evaluation of Q̇DIA in humans has used this venous effluent technique via right femoral vein access.158 Conversely, electromagnetic flow probes84,88,93,100,123 and Doppler flowmetry34,37,96,103,105,112,115 are synonymous in terms of preparation and are used to assess left phrenic artery inflow. Bark and Scharf (1986)84 developed the electromagnetic flow preparation in dogs to directly measure phrenic arterial inflow continuously during pacing-induced fatigue. The prep involves a large midline abdominal incision so that the stomach, intestines, and spleen can be exteriorized, and the liver and left kidney retracted. The left phrenic artery is then dissected, and an electromagnetic flow probe is placed around the artery between the aorta or abdominophrenic artery and the diaphragm. Doppler flowmetry96 and drop counters89 for phrenic artery and vein blood flows correlate well with the gold standard radiolabeled microsphere technique. These three techniques share two major limitations. The first limitation is the invasive nature is not conducive to routine use in humans. The second Figure 1.3. Schematic of the diaphragmatic circulation. Abdominal view of the diaphragm illustrating collateralization of phrenic, internal thoracic, and intercostal arteries in the dog. © Comtois A, Gorczyca W, Grassino A. Anatomy of diaphragmatic circulation. J Appl Physiol. 1987;62(1):238-244. Page 242. Modified with permission from the American Physiological Society 33 limitation is that left phrenic arterial/venous blood is sampled as a surrogate of left hemidiaphragmatic blood flow.84,88,93,100,103,105,112,115 Studies in dogs have found diaphragmatic inflow collateralization from the inferior phrenic, intercostal, and internal thoracic arteries with venous anastomoses between the phrenic, intercostal, and internal thoracic veins for the costal diaphragm.76,82,103,175 There are further venous anastomoses between phrenicoabdominal and inferior phrenic veins in the crural diaphragm.76 Further, the phrenic artery and vein seemingly only accounts for 20-25% of Q̇DIA96,103 but may get up to 50-70%.88 Thus, global diaphragmatic perfusion cannot be assessed from a single inflow or outflow vessel (Figure 1.3). Taken in context, singular inflow/outflow vessels would give relative but not absolute perfusion levels for the left hemidiaphragm. As a relative metric of Q̇DIA, there is great utility with using these techniques for temporal monitoring, but they are limited for localized perfusion. 1.4.3 Intravital Microscopy Intravital microscopy has been used for both in vivo28,29,32,43,44,63,129,131,176 and ex vivo46,47 preparations to determine Q̇DIA. For the original in vivo studies, a midline laparotomy was performed to allow for a three-dimensional articulated system to be set up perpendicular to the diaphragm. The diaphragm was then transilluminated via fiberoptic microprobes that were inserted into the thorax following thoracotomy of the 5th intercostal space. This preparation had the advantage of looking at the morphological characteristics of the diaphragmatic arteriolar network in vivo but sacrifices the abdominal and thoracic pressures influences on the diaphragm. As such, this technique has been extremely effective in determining mechanistic influences on the diaphragm through pharmacological interventions, but the highly invasive nature and degradation of mechanical influences have limited its utility to a small number of animal studies. 34 1.4.4 Near-Infrared Spectroscopy with Indocyanine Green (NIRS-ICG) Indocyanine green was first introduced by Fox in 1957 as an indicator of cardiac output which was not influenced by variations in blood oxygen saturation.177 The principal advantage of it as a dye was that it was confined to the vascular compartment by binding to plasma proteins,178 thereby allowing indocyanine green to transit the systemic circulation with the kinetics of albumin. Other advantages were its low toxicity, its rapid excretion once in the blood pool, and most importantly the presence of an absorption maximum at ~800 nm.179 Twenty years later, near-infrared spectroscopy technology was introduced as a non-invasive tool to assess dynamic changes in tissue oxygenation by measuring absorption spectra of light in the 400-1600 nm range. These two items were then combined as a way to quantify regional blood flow in skeletal muscle during dynamic exercise.180 Blood flow measurement is accomplished by placing NIRS optodes over a ROI such that NIRS could detect the rate and magnitude of the light-absorbing tracer accumulating and washing out within the NIRS’ field of detection.180 Using these principles, Guenette et al.181 applied the NIRS-ICG technique to studying respiratory muscle blood flows at the left 7th intercostal space (Figure 1.4).20 This offered a non-invasive alternative to respiratory muscle blood flow assessment that combined good temporal and spatial resolution to give a non-discriminative global assessment of blood flow to all tissues below the optodes up to a certain depth (~2 cm). As such, it has become the most used technique to study respiratory muscle blood flow in the last 15 years with a number of studies using it to assess physiological responses in healthy participants182–188 and COPD patients.159,189–192 Recently however, questions have arisen about the utility of NIRS-ICG given divergent results that have been found at high levels of exercise (reviewed by Sheel, Boushel, and 35 Dempsey).193 In brief, Vogiatzis et al., 182,183 found that respiratory muscle blood flows decreased at higher workloads up to maximum in trained cyclists which contrasted with previous work from their group from the prior year. This may be due to signal artifact as the chest wall moves through the respiratory cycle (Figure 1.4) such that NIRS measurements will be a product of a changing aggregation of tissues sampled. Further, NIRS-ICG is not able to differentiate between external obliques, intercostals, diaphragm, spleen, and lung for what tissues the blood flow signal is originating from which makes measurements individual dependent. For diaphragm specifically, NIRS-ICG may or may not even evaluate Q̇DIA as the diaphragm depth may be below the penetration depth of the NIRS optodes (0.8 – 4.9 cm diaphragm depth range).194 Lastly, as respiratory muscles contract, the depths of tissues change (e.g., diaphragm depth increases in Figure 1.4) which alters what blood flow signals as a function of both duty cycle and lung volumes. As such, while NIRS-ICG is the only technique currently feasible for routine human use, alternative measurements techniques are required that can overcome these challenges for the assessment of respiratory muscle blood flows, ideally for the primary respiratory muscles. Figure 1.4. Cross-sectional ultrasound image of the left 7th intercostal space. End-expiratory and tidal end-inspiratory diaphragm thicknesses are 0.19 cm and 0.31 cm, respectively. This equates to 9.5% and 15.5% of the 2 cm depth being occupied by diaphragm tissue when measured by NIRS-ICG. 36 1.4.5 Contrast-Enhanced Ultrasound (CEUS) One possible method of assessing Q̇DIA may be contrast-enhanced ultrasound (CEUS) which is a widely used technique that has applications in characterizing heart,195 tumor,196 organ,197 and skeletal muscle perfusion198–201 and shares many similarities with the established NIRS-ICG method. For both methods, a tracer agent with hemodynamics similar to components of blood (e.g., albumin, red blood cells) is added to the systemic circulation via venous catheterization. A recording probe is then simultaneously positioned over a ROI and emits either light (NIRS-ICG) or sound (CEUS). As the tracer-blood perfusate flows into the ROI, the tracer agent interacts with the wavelengths emitted from the recording probe by reflecting the waves back with a wavelength shift. The reflected waves are then received by the recording probe and the intensity of the recorded signal is computed. This signal intensity is proportional to the amount of tracer within the ROI. Because the hemodynamics of the tracer agents are similar to plasma proteins178 (NIRS-ICG) or red blood cells202 (CEUS), the kinetics of the tracer agent can provide an index of blood flow to a target tissue. Thus, the main differences between the NIRS-ICG and the CEUS techniques are 1) acoustic vs photoexcitable tracer properties, 2) the presence/absence of visual feedback of the ROI being measured, and 3) the need for a trained sonographer when performing CEUS. Despite the need for a trained sonographer, the presence of visual feedback and individual assessment of tissues with CEUS largely accounts for the current drawbacks with the NIRS-ICG technique. CEUS derived perfusion parameters having been validated against previously established measure of perfusion,203–205 including radiolabeled microspheres.206,207 Thomas et al. (2015)198 and Meneses et al. (2020)208 reported moderate to excellent intra-observer reliability (Thomas: ICC = 0.77 – 0.93; Meneses: ICC > 0.73) of CEUS in the static gastrocnemius muscle while 37 Kunz et al. (2020)199 reported excellent (r = 0.9) intra-observer, good (r = 0.84) inter-observer, and moderate (r = 0.60) inter-observer/inter-machine reliability of CEUS in the static deltoid muscle. Recently, Jones et al. (2023) reported a strong positive correlation between CEUS metrics (r > 0.75) in the vastus lateralis taken 14 days apart.209 Combine that with high inter/intra-observer reliability of ultrasound derived diaphragm thicknesses in supine210–212 and seated213 participants, data suggests that it should be feasible to obtain reliable CEUS-derived blood flow measurement in the diaphragm. While this does not guarantee reliability of CEUS derived perfusion parameters in the intercostals, costal diaphragm, and liver regions at rest or during different levels of work of breathing, CEUS of the costal diaphragm may prove to be a useful tool in the quantification of Q̇DIA. To perform diaphragmatic ultrasonography, a high frequency linear array transducer (>7 MHz) should be angled perpendicular to the chest wall near the anterior or mid-axillary line between the 8th and 10th intercostal spaces.210 This anatomical location provides a clear window of the medial costal diaphragm known as “the zone of apposition”. At this location, diaphragm depth is estimated between 8 mm and 49 mm into the skin with increased depth in participants with a higher body mass index.194 Our proposed CEUS methodology utilizes visual feedback of tissue (see Figure 1.5) while enabling the researcher to delineate ROI superficial (i.e., intercostal muscles) and deep (i.e., liver) to the bounds of the right medial costal diaphragm. Using destruction replenishment kinetics,206 the average signal intensity within each ROI can be plotted as a function of time thereby providing surrogate Figure 1.5. Annotated ultrasound image of the diaphragm. B-mode ultrasound image of the medial costal diaphragm taken from the 9th right intercostal space of a 25-year-old male participant. 38 measures of perfusion for the intercostal muscles, costal diaphragm, and liver under different conditions of respiratory work. In summary, there is an unmet need in physiology to assess Q̇DIA in humans as current techniques are non-viable or severely limited. CEUS may fill this methodological niche with improved spatial resolution and depth control over NIRS-ICG while permitting visualization of the medial costal diaphragm as it moves throughout the respiratory cycle.210 39 Chapter 2: Specific Research Objectives and Hypotheses 2.1 Brief Background Continual contraction of the diaphragm is crucial for maintaining pulmonary ventilation and gas exchange throughout the human life span. Consequently, the diaphragm and the heart are the only two muscles essential for life.22 Its function does not come without cost, however, as the diaphragm must have an adequate supply of oxygen and metabolic substrates to continuously support its contractile activity. Any impairment in blood supply to the diaphragm could results in a dangerous imbalance in nutritive supply and demand23 leading to diaphragm fatigue/failure, patient morbidity and mortality.24 Given its essential function, it is not surprising that Q̇DIA has been extensively studied using animal models dating back to the early 20th century. Despite this, there is no minimally invasive method to directly measure Q̇DIA in humans. As such, our knowledge is restricted to animal research despite species-dependent variability in diaphragmatic structure and function.21 To date, there has only been one study on direct measures of Q̇DIA in humans that was highly invasive and not conducive to routine human study.158 Thus, there is an unmet need for a valid, reliable, and accessible technique to measure Q̇DIA in humans. A technique capable of measuring Q̇DIA in humans would represent a major advancement in the field of respiratory medicine and could help to address widespread physiological and clinical problems. CEUS combined with 2D ultrasound imaging of the diaphragm represents a novel approach capable of measuring blood flow to the medial costal diaphragm. CEUS is widely used technique for quantifying blood flow that has applications in characterizing heart,195 tumor,196 organ,197 and skeletal muscle198–201 but has never before been used to quantify Q̇DIA. Further, CEUS derived perfusion parameters having been validated against previously established measure of blood flow,203–205 including radiolabeled microspheres206,207 which are 40 considered the gold-standard in Q̇DIA assessment. Before employing this approach in basic or clinical research, its feasibility, validity, and reliability needs to be determined. 2.2 Research Objective The objective of this research study is to assess the feasibility, validity, and reliability of using CEUS to measure Q̇DIA in humans. Thus, our objectives are to determine (1) if it is possible to obtain measures of Q̇DIA using CEUS, (2) the relationship between diaphragm work and Q̇DIA, and (3) the test-retest reliability and inter-observer reliability of CEUS as a measure of Q̇DIA. 2.3 Research Hypothesis We hypothesize that (1) CEUS will provide time-intensity curves that quantify Q̇DIA, (2) increases in respiratory muscle work elicited by inspiratory pressure threshold loading will cause a linear increase in Q̇DIA with each stage of inspiratory pressure threshold loading, and (3) Q̇DIA will have good reliability for both test-retest and inter-observer reliability. Additionally, since the liver is conveniently located within our ultrasound images, liver blood flow was measured by CEUS as a non-active control. 41 Chapter 3: Methodology and Data Analysis 3.1 Participants The Clinical Research Ethics Board at the University of British Columbia provided ethical clearance of all experimental procedures (H22-01082) and written informed consent was obtained from all participants. In total, 20 volunteers (13 male, 7 female) were recruited and participated in this study, however analyses were completed with a smaller sample size (n=16) due to technical issues reported in detail in Chapter 4. As this was not a sex differences study, female participants were not stratified by menstrual cycle phase. 3.2 General Procedures The experimental pre-screening and protocol are summarized in Figure 3.1. Participants visited the lab on three occasions. During the first visit, participants filled out the Physical Activity Readiness Questionnaire for Everyone form, pre-screening questionnaire, and were walked through the informed consent document. If the participant was eligible for the study and willing, a pulmonary function test (PFT), blood pressure measurement, and baseline diaphragm thickness measurements were taken. This was followed by participant familiarization with the inspiratory pressure threshold loading (ITL) apparatus that was used to augment respiratory work during the experimental protocol days. Participants also practiced holding a 15-20 second end-expiratory apnea which was needed during the experimental protocol. During the subsequent two experimental visits, participants underwent four different levels of respiratory muscle work with measurement of diaphragm perfusion immediately following respiratory muscle loading. The CEUS imaging was used to delineate blood flow to the diaphragm and liver regions. Non-invasive measures of heart rate (HR), blood pressure (BP), 42 respiratory frequency (fB), tidal volume (VT), minute ventilation (V̇E), end-tidal carbon dioxide (PETCO2) and blood oxygen saturation (SpO2; finger pulse oximetry) were also monitored and recorded continuously. Participants were also instrumented with both an esophageal and gastric balloon tipped catheter for respiratory pressures. During the final two minutes of each ITL trial, a contrast agent (Definity; Lantheus Medical Imaging, North Billerica, MA, USA) was infused continuously. Upon completion of each ITL trial, the sonographer (J.D.B.) coached the participant to perform an end-expiratory apnea for 20-25 seconds while ultrasound cine loops were captured. Figure 3.1. Schematic of pre-screening and experimental days. Abbreviations: EE, end-expiratory; ITL, inspiratory pressure threshold loading; MIP, maximal inspiratory pressure. 3.3 Screening Procedures To be eligible for inclusion in this study, volunteers were required to be healthy males or females between the ages of 18 and 40 who are normotensive (<140/90 mmHg & >90/60 mmHg) and have normal pulmonary function (FEV1/FVC > 0.70). Additionally, participants were ineligible if they were taking any medications (prescribed or over the counter, other than oral contraceptives) throughout the duration of the experimental timeline. Volunteers were excluded if they had a known or suspected hypersensitivity to perflutren or an allergy to latex, lidocaine, 43 or antihistamines. Participants were free from known cardiovascular (e.g., hypertension, coronary artery disease, heart failure), metabolic (e.g., diabetes mellitus), and respiratory (e.g., chronic obstructive pulmonary disease, cystic fibrosis, sleep apnea, asthma) diseases. 3.3.1 Pulmonary Function Test Spirometry, lung volumes, and diffusion capacity tests were performed on each participant using body plethysmography (V62J, Vmax Sensormedics, Yorba Linda, CA, USA). Participants sat within the box with a rigid upright posture and their feet flat on the ground while breathing through a spirometer and bacteriological filter with their nose clamped for testing. Forced vital capacity (FVC) and forced expired volume in one second (FEV1) were assessed using an FVC maneuver that involves a full inspiration followed by a forced expiration. A minimum of three repeated maneuvers were performed (±5%) and the largest FEV1 and FVC value were selected. Vital capacity was assessed via full inspiration followed by a full slow expiration. Three maneuvers were performed with the largest vital capacity trial selected. A single breath carbon monoxide test was used to quantify diffusion capacity (DLCO).214 Body plethysmography was used to assess total lung volume, functional residual capacity (FRC), and residual volume (RV). Panting maneuvers were performed against a closed valve until three tests were obtained with values ± 5% of the mean value. Pulmonary function tests were conducted in accordance with American Thoracic Society and European Respiratory Society’s joint guidelines and compared against appropriate population-based predictions from the Global Lung Function Initiative reference values.215,216 44 3.3.2 Diaphragm Thickness Measurements Baseline measurements of diaphragm thickness were assessed with a 15 MHz (ML6-15; GE Healthcare Technologies, Piscataway, NJ, USA) transducer in the seated position to delineate regions of costal diaphragm, external obliques, intercostal muscles, and liver (see Figure 1.5). B-mode images were used to identify thickness measurements for diaphragm,210 intercostal (3-4 mm),217 and external oblique (~5 mm).218 B-mode cine loops were used to determine the change in diaphragm thickness with quiet tidal breathing and to measure thickening fraction (TF) as follows: Equation 2. Diaphragm Thickening Fraction 𝑇𝐹 =𝑇𝐸𝐼 − 𝑇𝐸𝐸𝑇𝐸𝐸× 100 Where TEI is diaphragm thickness at end-inspiration and TEE is diaphragm thickness at end-expiration. Measures of TEI and TEE were taken from an average of at least three consecutive breaths. Participant diaphragm values were compared to normative sex specific values for sitting participants.213,219 3.4 Experimental Procedures Participants visited the lab for two experimental days. Experimental days were a minimum of 48 hours apart and a maximum of seven days apart to minimize any influence of lifestyle on respiratory function. Prior to experimental days, participants were asked to abstain from exercise and alcohol for at least 12 hours prior to experimental sessions. Upon arrival in the lab, participants were instrumented with a venous catheter and instrumented for measurements of respiratory pressures, automated and beat-by-beat blood pressure (systolic blood pressure, SBP; 45 diastolic blood pressure, DBP; mean arterial pressure, MAP), oxyhemoglobin saturation (SpO2), respiratory effort strain gauges, ventilation (fB, VT), and end-tidal gases (Figure 3.2). Figure 3.2. Experimental setup and instrumentation. Figure 3.2 outlines the experimental set up with Figure 3.3 showing an individual participant’s raw signals plotted. Following instrumentation, participants were given a maximal inspiratory pressure (MIP) valve and coached through a series of MIP maneuvers at FRC and RV. These maneuvers were repeated until three trials within ±5% were achieved. Participants were then instructed to warm up their respiratory muscles and practice square wave breathing with the ITL device at a low workload for 5 minutes. After warmup completion, the microbubble suspension was prepared over the subsequent 5 minutes and connected to the venous catheter for infusion. 46 Participants were reminded of trial procedure prior to commencing ITL. The four experimental trials were baseline (BL), stage 1 (S1), stage 2 (S2), and stage 3 (S3) where each stage was a progressively larger inspiratory resistance. Experimental trial order was determined on a participant-by-participant basis in a pseudorandomized and counterbalanced order to control any order effect of blood flow measurements. Following a two-minute baseline period, participants were switched to the ITL device and coached to follow a square wave mouth pressure waveform at a fixed breathing frequency (15 breaths/min) and duty cycle (0.5) for each 5-minute trial. Microbubbles were continuously infused for the last two minutes of the trial to achieve steady state (see Appendix C, Figure 6.4).220 At the end of the 5-minute trial, the participant was coached into the final three breathes (making trials ~312 seconds in length) and told to hold an end-expiratory apnea at FRC. The sonographer performed the blood flow measurement protocol over the subsequent 20-25 seconds. Upon completion of the blood flow measurement, the sonographer coached the participant to perform a maximal inspiratory capacity maneuver to determine apneic lung volume to conclude the trial. The participant was then removed from the mouthpiece and allowed to rest for 5 minutes before the start of the next trial. After all trials were complete, the participant was de-instrumented marking experimental completion. 47 Figure 3.3. Day-to-day respiratory signal waveforms for each experimental trial. Raw tracings of PCO2, mouth pressure, esophageal pressure, and transdiaphragmatic pressuring during the 3rd minute of each experimental trial on both experimental days for an individual 27-year-old female participant. Abbreviations: ITL, inspiratory pressure threshold loading, PCO2, pressure of carbon dioxide; TTI, tension-time index; V̇E, minute ventilation. 48 3.5 Specific Methodology 3.5.1 Cardiovascular and Respiratory Measurements All cardiovascular and respiratory variables were acquired using an analog-to-digital converter (ML880 PowerLab 16/35; ADInstrucments, Colorado Springs, CO, USA) interfaced with a personal computer at 200 Hz. Through all experimental trials, participants breathed through a mouthpiece attached to a bacteriological filter and respiratory modulation setup while wearing a nose clamp. Respired end-tidal oxygen (PETO2) and carbon dioxide (PETCO2) gas pressures were sampled on the expiratory side of the experimental setup by a gas analyzer (ML206; ADInstruments, Colorado Springs, CO, USA). Inspiratory and expiratory flow were measured separately using two pneumotachometers (models 3813 and 4813; Hans Rudolph, Shawnee, KS, USA) connected to differential pressure amplifiers (model PA-1; Hans Rudolph, Shawnee KS, USA). The expiratory pneumotachometer were attached to a pneumotach heater control (3850A series; Hans Rudolph, Shawnee, KS, USA) set to 37℃. Non-invasive measures of beat-by-beat blood pressure by finger pulse photoplethysmography (Finometer PRO; Finapres Medical Systems, Amsterdam, The Netherlands), heart rate by lead II electrocardiogram (FE132; ADInstruments, Colorado Springs, CO, USA), and oxyhemoglobin saturation by pulse oximetry (7500FO; Nonin Medical, Plymouth, MN, USA) were continuously recorded. Cardiorespiratory data were collected continuously throughout the experimental protocol and saved for offline analysis using commercially available software (LabChart v8.0; ADInstruments, Colorado Springs, CO, USA). 49 3.5.2 Esophageal and Gastric Balloon Catheterization Esophageal (PESO) and gastric (PGAS) pressures were measured by balloon-tipped catheters (no. 47-9005, Ackrad Laboratory, Cranford, NJ, USA) according to previously described techniques. 221 Briefly, a topical anesthetic was applied to the participant’s nasopharynx (Xylocaine, 2% Lidocaine Hydrochloride). Subsequently, two balloon catheters were sequentially passed through the nose and positioned within the stomach. Participants then performed a Valsalva maneuver with the catheters open to the atmosphere to purge air from the balloons before the circuit was closed. A glass syringe was then connected to the circuit via a 3-way Luer lock and 1cc and 2cc of air were administered into the esophageal and gastric balloons, respectively. The balloon catheters were connected to calibrated piezoelectric pressure transducers and the signals were converted to digital signals using a data acquisition system. The esophageal catheter was positioned by having the participant periodically sniff to determine at what depth a change from positive to negative pressure deflection occurred. At this inflection point, the esophageal catheter was then withdrawn 10 cm. PESO readings were considered satisfactory between a range of -2 to -5 cmH2O with normal breathing. If required, minor adjustments to the esophageal catheter depth were made to ensure proper placement and signal range. 3.5.3 Inspiratory Pressure Threshold Loading Prior to inspiratory pressure threshold loading, participants were coached to perform a series of MIP maneuvers at FRC and RV. A minimum of three maneuvers were performed to get within ±5% for repeatability. Of these maneuvers, the highest value from the first day of experimental testing was used so that the load in grams was consistent from day-to-day. The respiratory 50 muscle loading was achieved using a custom built ITL device that was validated across a range of inspiratory flows and pressures (see Appendix C, Figure 6.3). During the protocol, participants wore a nose clip and would breathe through a mouthpiece and bacteriological filter connected to the ITL device. The ITL device consisted of a unidirectional airflow tube where inspiratory airflow could be achieved by releasing a respiratory valve that was gravitationally held in place by weights. To initiate inspiratory airflow, the participant had to generate sufficient inspiratory pressure to overcome the gravitational pull of the weights. This allowed for the fine tune adjustment (±2 cmH2O) of the minimal pressure needed to generate any airflow for each breath by altering the amount of weight on the end of the respiratory valve in the tube. Throughout the loading protocol, PETCO2 was monitored and, if necessary, CO2 was titrated into the ITL apparatus to keep participants normocapnic. Mouth pressures were measured from a port in the mouthpiece connected to a differential pressure transducer (model PA-1; Hans Rudolph, Kansas City, MO, USA). 3.5.4 Contrast-Enhanced Ultrasound Measurements On the experimental days, a 35 ml syringe was inserted into an infusion pump (Baxter AS50 Infusion Pump, Baxter International Inc., Deerfield, IL, USA) and connected to 60” microbore tubing feeding into a 22-gauge cannula placed in the antecubital vein. The syringe contained a vial of activated Definity (1.3 ml) and sterile saline (20.0 ml) for a volume of ~21.3 ml. Over the course of each experimental day, four Definity infusions were administered. Each trial volume limit was set to 5 ml of infusion with automatic cessation via the infusion pump. This dose aligns with the manufacturer recommended dose and doses previously used.196,222–227 The infusion rate was set at 150 ml/hr for all participants which is on the higher end of infusion rates in the 51 literature.198,228–230 All CEUS images were captured using a 9L linear array transducer on the ultrasound machine (Vivid E9; GE Healthcare Technologies, Piscataway, NJ, USA). Ultrasound settings used a low mechanical index of 0.9 – 0.11, depth of 3 – 3.5 cm, frequency of 4 MHz, dynamic range of 57, one focal zone immediately deep to the peritoneum, and no persistence or edge enhancement. Image acquisition was conducted at 3 Hz following an ultrasonic pulse of higher intensity used to burst all the microbubbles in the ultrasound field of view. The reaccumulating microbubbles within the ultrasound field of view are proportional to blood flow. Ultrasound cine loops for 15 seconds post bubble destruction were collected and stored. All CEUS examinations were performed by the same research trained sonographer (J.D.B.) who was certified in diaphragm ultrasonography and has an intra-individual coefficient of variation (CV) for diaphragm measurement of 4% (±0.1 mm) for diaphragm thickness. Two-dimensional ultrasound cine loops were exported, converted to .mov files, and analyzed using publicly available software (narnar, narnar.co). 3.6 Data Analysis 3.6.1 Cardiorespiratory Parameters Cardiorespiratory data were separated into inspiratory and expiratory components on a breath-by-breath basis. Data was cleaned for errant event triggering to remove respiratory artifact (e.g., swallowing). Inspiratory data for each five-minute experimental trial was averaged to get respiratory data during each inspiratory phase of breathing. Cardiovascular data was taken as an average of both inspiratory and expiratory phases and was averaged for each five-minute experimental trial. 52 3.6.2 Transdiaphragmatic and Respiratory Muscle Pressures Transdiaphragmatic pressure (PDIA) was calculated as the difference between gastric and esophageal pressures. The pressure-time product (PTP; i.e., integral of the pressure trace) of PDIA and PESO was calculated as described previously.231 PTPDIA is presented as an index of diaphragm muscle work. TTIDIA was calculated per Equation 1 and a non-invasive analog of the TTIDIA called the tension-time index of the respiratory muscles (TTIRESP) was also calculated as per Equation 3. Equation 3. Tension-Time Index of Respiratory Muscles 𝑇𝑇𝐼𝑅𝐸𝑆𝑃 =𝑃𝐼𝑀𝐼𝑃𝐹𝑅𝐶× 𝐷𝐶 Where PI is mean inspiratory pressure, MIP is maximal inspiratory pressure measured from FRC, and DC is the duty cycle.232 3.6.3 Ultrasound Cine Loops Time and acoustic intensity data from the .mov files were determined for ROIs within the diaphragm and liver and exported as .csv files. The ROI selection process in the diaphragm required freehand drawing within the bounds of the diaphragm to avoid acoustic artifact (e.g., tendinous aspects that run through the diaphragm that appear hyperechoic) that may be present. ROI selections were optimized to provide the most consistent and artifact free regions that were present throughout all cine loop frames in each trial. If there was movement of the diaphragm bounds, individual frames were realigned frame-by-frame to minimize movement and to keep pixel selection consistent. The .csv files were loaded into a custom program written in R and the data model fit (Equation 4).206 The parameters A and β were calculated using non-linear least 53 squares regression using the Levenberg-Marquardt algorithm and presented as an index of perfusion. Equation 4. Destruction-Replenishment Kinetics 𝑦 = 𝐴(1 − 𝑒−𝛽𝑡) Where y is the video intensity at pulsing interval, A is the plateau video intensity reflecting microvascular blood volume (MBV), and β is rate of microbubble replenishment reflecting microvascular blood flux rate (MBF). The multiplication of MBV and MBF gives microvascular blood flow for a given region (Q̇DIA, Q̇LIV). This analysis technique has been used extensively to characterize muscle perfusion.206,228–230,233 Vascular conductance was further calculated from microvascular blood flows and MAP. 3.7 Statistical Analysis Statistical analyses were performed using the R statistical language (R Foundation for Statistical Computing), lme4,234 lmerTest,235 emmeans,236 and rstatix237 statistical packages. Data are presented as the mean ± standard deviation, and contrasts are presented as mean difference with 95% confidence intervals. 95% limits of agreement (LOA) are presented as mean differences ± 95% confidence intervals. Statistical significance was determined at P < 0.05 for all comparisons. Participant anthropometric parameters, pulmonary function data, and basic diaphragm structure characteristics were analyzed with an independent samples Student’s t-test to determine differences between male and female participants. MIP and PDIA values at FRC and RV were compared between experimental days via paired t-test to determine the presence of respiratory muscle fatigue or a learning effect.238 54 3.7.1 Cardiorespiratory Statistical Analysis Likelihood ratio tests were conducted to compare linear mixed models and optimize model fits. Sex and trial order were not found to be significant fixed factors for blood flow measurements. As such, the optimized model based on Akaike information criterion and the Bayesian information criterion were linear mixed effects models with experimental trials (BL, S1, S2, S3) and day (Day 1, Day 2) as fixed factors and participant identifiers as a random factor. If significant F ratios were detected (p < 0.05), a Tukey post hoc test was used to identify where the differences lie. Additional linear mixed models with respiratory pressures as continuous predictors and random slopes (%PDIMAX, %MIP, PTPPDI, SUM, or PTPPDI, AVG), day (Day 1, Day 2) as a fixed factor, and participant identifiers as a random factor were used to determine the influence of respiratory pressures on Q̇DIA. Sex as an additional fixed factor term was included in these models, optimized based on likelihood ratio tests, and presented as figures are reported in the Appendix B. 3.7.2 Reliability Statistical Analysis Test-retest reliability (i.e., stability reliability) was assessed via two-way mixed effects absolute agreement single measure intraclass correlation coefficients (ICC).239 ICC estimates were assessed as having poor, moderate, good, or excellent reliability as previously described.240 Absolute reliability was assessed by standard error of measurement (SEM), technical error of measurement (TEM), and LOA.241,242 Coefficients of variation were computed as measures of absolute reliability if the assumptions for SEM and/or TEM were not met. 55 SEM is a measurement of the maximum difference in innate units between two measurements with a certainty of 68%.241 The two assumptions for use are that data is normally distributed and there is no relationship between error and size of measured values (i.e., heteroscedasticity). Normality of distribution and heteroscedasticity were assessed via QQ plots, histograms and plotting the fitted values against the residuals based on linear mixed models. SEM was calculated as Equation 5. Standard error of measurement 𝑆𝐸𝑀 =𝑆𝐷𝑑𝑖𝑓𝑓√2 where SDdiff is the standard deviation of the differences between the two measurements. TEM is similar to the SEM in that it represents the maximum difference between two measurements with a certainty of 68%.241 TEM has the same assumptions as SEM with an additional assumption that there is no systematic learning or fatigue effect between measurements. TEM considers random and systematic errors together as “total error” and is calculated as the following: Equation 6. Technical error of measurement 𝑇𝐸𝑀 = √∑ 𝑑22𝑛 where d is the difference between measurement one and measure two. LOA is a measurement of the maximum difference between any two repeated observations with a certainty of 95%,241 has the same assumptions as SEM, and was calculated as follows: Equation 7. Limits of agreement 𝐿𝑂𝐴 = 1.96 × 𝑆𝐷𝑑𝑖𝑓𝑓 = 1.96 × √2 × 𝑆𝐸𝑀 56 Inter-observer (i.e., objectivity) reliability was calculated using the same equations in all cases with the only difference being the comparison of analysis as determined by J.D.B. (Observer 1) and M.L.L. (Observer 2). 57 Chapter 4: Results 4.1 Participants Twenty participants (7F/13M) met inclusion and exclusion criteria for this study. Four participants were excluded owing to issues with image quality, ultrasound settings, contrast destruction, and movement of nasogastric balloons. Sixteen participants (6F/10M) were included in the final analysis. Anthropometric, pulmonary function, and diaphragmatic thicknesses are presented in Table 4.1. Sex differences in anthropometric characteristics, pulmonary function, and basic diaphragm ultrasound data are reported however, sex differences were not a primary objective of this study and are consequently underpowered. Relevant supplementary sex-based analyses are reported in Appendix B for exploratory purposes. MIPRV was not different between day 1 (-139 ± 36 cmH2O) and day 2 (-144 ± 36 cmH2O; P = 0.26). Conversely, MIPFRC increased from day 1 (-126 ± 31 cmH2O) to day 2 (-139 ± 31 cmH2O; P = 0.006). PDIMAX, FRC was not different between day 1 (157 ± 28 cmH2O) and day 2 (149 ± 26 cmH2O; P = 0.15). Likewise, PDIMAX, RV was not different between day 1 (130 ± 26 cmH2O) and day 2 (129 ± 23 cmH2O; P = 0.80). 58 Table 4.1. Participant anthropometrics, pulmonary function data, and diaphragm ultrasound parameters. ♂ ♀ Total P N = 10 N = 6 N = 16 Demographics Age, years 28 ± 6 29 ± 5 28 ± 5 0.77 Height, cm 184 ± 6 165 ± 6* 178 ± 10 <0.0001 Weight, kg 80 ± 9 60 ± 3* 73 ± 14 <0.0001 BMI, kg m-2 23.6 ± 1.8 21.6 ± 1.5 22.8 ± 2.0 0.04 Pulmonary Function Spirometry FVC, liters 6.4 ± 1.0 4.2 ± 0.3* 5.6 ± 1.4 <0.0001 FVC, % predicted 110 ± 16 106 ± 12 109 ± 14 0.57 FEV1, liters 4.8 ± 0.7 3.3 ± 0.3* 4.2 ± 0.9 <0.0001 FEV1, % predicted 100 ± 13 97 ± 8 99 ± 11 0.64 FEV1/FVC, % measured 74.5 ± 5.2 78.3 ± 5.5 75.9 ± 5.5 0.20 FEV1/FVC, % predicted 90 ± 7 91 ± 6 91 ± 7 0.79 Lung Volumes TLC, liters 8.8 ± 2.1 5.4 ± 0.6* 7.5 ± 2.4 <0.0001 TLC, % predicted 106 ± 43 103 ± 13 105 ± 34 0.87 FRC, liters 4.5 ± 1.8 2.8 ± 0.4* 3.9 ± 1.6 0.02 FRC, % predicted 122 ± 40 98 ± 14 113 ± 35 0.11 RV, liters 2.4 ± 1.6 1.3 ± 0.4 2.0 ± 1.4 0.06 RV, % predicted 135 ± 75 87 ± 23 117 ± 64 0.08 Diffusing Capacity DLCO, ml/min/mmHg 36.4 ± 8.5 25.9 ± 5.5* 32.2 ± 9.0 0.01 DLCO, % predicted 98 ± 22 101 ± 20 99 ± 21 0.82 DLCO/VA, ml/min/mmHg/liter 4.6 ± 0.6 5.3 ± 0.7 4.9 ± 0.7 0.09 DLCO/VA, % predicted 92 ± 14 109 ± 15 95 ± 14 0.05 Respiratory Muscle Pressure MIPRV, cmH2O 143 ± 37 105 ± 23* 129 ± 37 0.02 MIPRV, % predicted 128 ± 34 112 ± 24 122 ± 31 0.27 Diaphragm Ultrasound Diaphragm depth, mm 17.3 ± 3.4 15.8 ± 3.9 16.7 ± 3.5 0.47 End-expiration thickness, mm 2.2 ± 0.3 2.0 ± 0.2 2.1 ± 0.3 0.09 End-inspiration thickness, mm 4.2 ± 0.8 3.4 ± 0.4* 3.9 ± 0.8 0.02 ΔTidal thickness, mm 2.0 ± 0.8 1.4 ± 0.3 1.8 ± 0.7 0.07 Tidal thickening fraction, % 93 ± 46 73 ± 21 85 ± 39 0.28 N = 16; Values are mean ± SD. * Significant difference compared to male (P<0.05). Abbreviations: BMI, body mass index; DLCO, diffusion capacity of the lung for carbon monoxide transfer; DLCO/VA, DLCO corrected for alveolar volume; FEV1, forced expired volume in one second; FRC, functional residual capacity; FVC, forced vital capacity; MIPRV, maximal inspiratory pressure at residual volume from the pulmonary function test; P, probability for a difference between sexes based on a two-way unpaired t-test; RV, residual volume; TLC, total lung capacity. 59 4.2 Cardiorespiratory Data 4.2.1 Respiratory Measurements Average respiratory measurements for the 5-minute experimental trials are presented in Table 4.2 and 15-second binned averages for respiratory variables are presented in Figure 4.1. %MIP for BL, S1, S2, and S3 were 0.7 ± 0.4, 9.3 ± 0.4, 17.6 ± 0.4, and 25.2 ± 0.4, respectively. %PDIMAX for BL, S1, S2, and S3 were 15.2 ± 0.8, 26.1 ± 0.8, 34.6 ± 0.8, and 40.0 ± 0.8, respectively. There were significant main effects of trial for %MIP and %PDIMAX (Table 4.2). First, %MIP was significantly less at BL compared to S1 (8.6 [7.7, 9.6] %), S2 (17.0 [16.0, 17.9] %), and S3 (24.5 [23.5, 25.5] %; all P < 0.0001). Similarly, %PDIMAX was also significantly less at BL compared to S1 (10.8 [8.4, 13.2] %), S2 (19.4 [17.0, 21.7] %), and S3 (24.7 [22.3, 27.1] %; all P < 0.0001). There were main effects of both trial and day on PETCO2 (Table 4.2). PETCO2 was greater on day 2 (0.7 [0.1, 1.4] mmHg; P = 0.03) and was significantly lower for BL compared to S1 (1.9 [0.7, 3.1] mmHg; P = 0.0003), S2 (1.6 [0.4, 2.8] mmHg; P = 0.003), and S3 (2.2 [0.9, 3.4] mmHg; P < 0.0001). PETCO2 was not different between any of the stage trials (P > 0.65). There were significant main effects of both trial and day for VT and V̇E. VT (0.4 [0.3, 0.5] l; P < 0.0001) and V̇E (5.7 [3.4, 8.0] l·min-1; P < 0.0001) were significantly greater on day 1 compared to day 2. VT was significantly less for BL compared to S1 (0.6 [0.3, 0.9] l, S2 (0.9 [0.6, 1.2] l), and S3 (0.8 [0.5, 1.1] l; all P < 0.0001). VT was also greater in S2 compared to S1 (0.3 [0.0, 0.6] l; P = 0.048). V̇E had a similar response as VT as V̇E was significantly greater compared to BL for S1 (8.9 [4.6, 13.2] l·min-1), S2 (13.3 [9.1, 17.5] l·min-1), and S3 (11.6 [7.3, 60 15.9] l·min-1; all P < 0.0001). There was also greater V̇E for S2 compared to S1 (4.4 [0.1, 8.7] l·min-1; P = 0.04). Breathing frequency was not different between days or trials. There was a significant main effect of trial for duty cycle (Table 4.2). Duty cycle was significantly shorter for BL compared to S1 (0.08 [0.06, 0.10]; P < 0.0001), S2 (0.10 [0.08, 0.12]; P < 0.0001), and S3 (0.11 [0.09, 0.13]; P < 0.0001). Duty cycle for S1 was also significantly shorter than S3 (0.03 [0.01, 0.05]; P < 0.0001) but there was no difference compared to S2 (0.02 [0.00, 0.03]; P = 0.06]. There was also no difference between S2 and S3 (0.01 [ 0.00, 0.03]; P = 0.19). 61 Table 4.2. Summary of respiratory and control data averaged over the 5 minutes of inspiratory pressure threshold loading for both experimental days. Inspiratory Pressure Threshold Loading Trials P-value Day Baseline Stage 1 Stage 2 Stage 3 CV Total TRIAL DAY X Respiratory MIP, % Day 1 0.7 ± 0.2 9.1 ± 1.0† 17.6 ± 2.2†‡ 25.1 ± 3.4†‡§ 9.3% <0.0001 0.51 0.94 Day 2 0.7 ± 0.1 9.4 ± 0.8† 17.7 ± 2.1†‡ 25.4 ± 3.0†‡§ CV 11.1 [5.2, 23.3] 8.7 [1.7, 19.1] 8.4 [3.0, 20.3] 8.7 [3.6, 17.1] %PDIMAX, % Day 1 15.4 ± 2.3 27.5 ± 4.7† 34.4 ± 5.4†‡ 39.0 ± 5.3†‡§ 14.7% <0.0001 0.44 0.10 Day 2 15.2 ± 2.3 24.4 ± 3.6† 34.8 ± 4.5†‡ 40.5 ± 4.9†‡§ CV 12.9 [4.1, 26.7] 16.0 [5.3, 26.4] 15.8 [3.6, 23.5] 14.3 [4.9, 23.7] PETCO2, mmHg Day 1 31 ± 4 34 ± 3† 33 ± 3† 34 ± 4† 7.5% <0.0001 0.03 0.28 Day 2 33 ± 4 34 ± 3† 34 ± 3† 34 ± 3† CV 6.1 [2.0, 13.0] 7.8 [4.9, 15.3] 7.9 [5.7, 11.7] 8.6 [5.6, 14.9] SpO2, % Day 1 98 ± 1 98 ± 1† 98 ± 1† 98 ± 1† 0.7% 0.002 0.73 0.28 Day 2 98 ± 1 98 ± 1† 98 ± 1† 98 ± 1† CV 0.8 [0.1, 3.7] 0.7 [0.3, 1.1] 0.7 [0.4, 1.4] 0.7 [0.3, 1.0] VT, l Day 1 1.4 ± 0.6 2.0 ± 0.8† 2.4 ± 0.9†‡ 2.3 ± 0.8† 16.1% <0.0001 <0.0001 0.22 Day 2* 1.2 ± 0.3 1.7 ± 0.5† 1.8 ± 0.5†‡ 1.9 ± 0.6† CV 15.8 [8.9, 23.7] 15.6 [8.5, 31.5] 16.2 [8.4, 40.3] 17.0 [12.1, 21.2] fB, breaths·min-1 Day 1 15 ± 0 15 ± 1 15 ± 0 15.0 7.7% 0.06 0.06 0.17 Day 2 15 ± 0 15 ± 0 15 ± 0 15.0 CV 7.0 [3.5, 13.5] 9.1 [2.2, 25.7] 7.1 [2.9, 15.7] 7.6 [3.5, 12.4] V̇E, l·min-1 Day 1 20 ± 10 29 ± 12† 37 ± 13†‡ 34 ± 13† 17.5% <0.0001 <0.0001 0.21 Day 2* 18 ± 5 25 ± 8† 28 ± 8†‡ 28 ± 9† CV 17.2 [9.7, 25.1] 17.6 [8.8, 35.8] 17.2 [8.7, 41.0] 18.1 [12.8, 22.9] Duty cycle Day 1 0.49 ± 0.04 0.57 ± 0.03† 0.58 ± 0.05† 0.60 ± 0.05†‡ 8.3% <0.0001 0.61 0.64 Day 2 0.47 ± 0.04 0.56 ± 0.03† 0.58 ± 0.03† 0.59 ± 0.04†‡ CV 8.9 [4.4, 17.8] 8.2 [4.7, 16.8] 7.7 [3.8, 14.9] 8.5 [4.9, 19.1] N = 16; Values are presented as mean ± SD. Coefficients of variation are expressed as mean [minimum, maximum]. Significance was set to P <0.05. * Significant difference between days. † Significant difference compared to BL. ‡ Significant difference compared to S1. § Significant difference compared to S2. Abbreviations: %PDIMAX, percentage of maximum transdiaphragmatic pressure; CV, coefficient of variation; fB, breathing frequency; MIP, maximal inspiratory pressure; P, probability for a difference between conditions based on linear mixed models; PETCO2, partial pressure of end-tidal carbon dioxide; V̇E, minute ventilation; VT, tidal volume; X, interaction effect of trial and day. 62 Figure 4.1. Group respiratory parameters throughout inspiratory loading trials. Data are presented as 15 second bins with means ± standard deviations for 16 participants. Abbreviations: fB, breathing frequency; MIP, maximal inspiratory pressure; PDIMAX, maximum transdiaphragmatic pressure; PETCO2, partial pressure of end-tidal carbon dioxide; V̇E, minute ventilation; VT, tidal volume. 63 4.2.2 Respiratory Pressure Measurements Average respiratory pressure measurements for the 5-minute experimental trials are presented in Table 4.3 and 15-second binned averages for all respiratory pressure variables are presented in Figure 4.2. There were significant main effects of trial on all mean inspiratory pressure measurements (P̅M, P̅ESO, P̅GAS, P̅DIA). P̅M became more negative compared to BL for S1 (-12.1 [-9.1, -15.2] cmH2O), S2 (-24.0 [20.9, -27.0] cmH2O), and S3 (-35.0 [-31.9, -38.0] cmH2O; all P < 0.0001). P̅M also became more negative from S1 to both S2 (-11.8 [-8.7, 14.9] cmH2O; P < 0.0001) and S3 (-22.8 [-19.7, -26.0] cmH2O; P < 0.0001). Lastly, P̅M became more negative from S2 to S3 (-11.0 [-7.9, -14.1] cmH2O; P < 0.0001). Similar results were found for both averaged and summed PTPM. P̅ESO became more negative compared to BL for S1 (-11.8 [-8.6, -14.9] cmH2O), S2 (-23.3 [-20.2, -26.4] cmH2O), and S3 (-32.6 [-29.4, -35.8] cmH2O; all P < 0.0001). P̅ESO became more negative from S1 to both S2 (-11.5 [-8.4, -14.7] cmH2O; P < 0.0001) and S3 (-20.8 [ -17.6, -24.1] cmH2O; P < 0.0001). Lastly, P̅ESO became more negative from S2 to S3 (-9.3 [-6.1, -12.5] cmH2O; P < 0.0001). Similar results were found for both averaged and summed PTPESO. P̅GAS increased compared to BL for S1 (6.1, 1.6, 10.5] cmH2O; P = 0.003), S2 (8.8 [4.4, 13.2] cmH2O; P < 0.0001), and S3 (8.1 [3.6, 12.6] cmH2O; P < 0.0001). However, PGAS was not different between S1, S2, and S3 (P > 0.39). P̅DIA progressively increased compared to BL for S1 (17.9 [13.4, 22.3] cmH2O), S2 (32.1 [27.7, 36.5] cmH2O), and S3 (40.8 [36.3, 45.3] cmH2O; all P < 0.0001). P̅DIA also increased from S1 to both S2 (14.2 [9.8, 18.7] cmH2O; P < 0.0001) and S3 (22.9 [18.3, 27.5] cmH2O; P < 64 0.0001). Lastly, P̅DIA increased from S2 to S3 (8.7 [4.2, 13.2] cmH2O; P < 0.0001). Similar results were found for both averaged and summed PTPPDI. TTIDIA increased compared to BL for S1 (0.07 [0.06, 0.09]), S2 (0.13 [0.11, 0.14]), and S3 (0.16 [0.15, 0.18]). TTIDIA also increased from S1 to both S2 (0.05 [0.04, 0.07]) and S3 (0.09 [0.07, 0.10]). TTIDIA further increased from S2 to S3 (0.03 [0.02, 0.05]). Similar results were found for TTIRESP. 65 Table 4.3. Summary of respiratory pressure and respiratory work data averaged over 5 minutes of inspiratory pressure threshold loading for both experimental days. Inspiratory Pressure Threshold Loading Trials P-value Day Baseline Stage 1 Stage 2 Stage 3 CV Total TRIAL DAY X Respiratory Pressures P̅M, cmH2O Day 1 -1.0 ± 0.3 -13.1 ± 3.8† -25.0 ± 7.5†‡ -36.0 ± 12.3†‡§ 9.3% <0.0001 0.63 0.99 Day 2 -0.9 ± 0.2 -13.4 ± 3. † -25.3 ± 8.1†‡ -36.6 ± 11.7†‡§ CV 11.1 [5.2, 23.2] 8.7 [1.7, 19.1] 8.4 [3.0, 20.3] 8.7 [3.6, 17.1] P̅ESO, cmH2O Day 1 -9.2 ± 1.9 -21.8 ± 4.8† -32.9 ± 8.1†‡ -42.9 ± 12.2†‡§ 16.5% <0.0001 0.28 0.96 Day 2 -8.9 ± 1.8 -20.2 ± 3.9† -32.1 ± 8.2†‡ -41.5 ± 10.3†‡§ CV 18.1 [7.1, 61.9] 18.9 [7.4, 39.0] 15.1 [3.4, 33.5] 13.8 [6.2, 32.3] P̅GAS, cmH2O Day 1 15.6 ± 2.7 23.2 ± 8.3† 23.3 ± 10.9† 20.6 ± 9.7† 23.0% <0.0001 0.95 0.23 Day 2 15.6 ± 3.2 19.4 ± 8.2† 24.9 ± 10.7† 24.9 ± 12.9† CV 13.9 [5.5, 32.2] 21.1 [9.5, 37.9] 26.7 [11.4, 45.2] 31.3 [11.9, 80.6] P̅DIA, cmH2O Day 1 24.8 ± 4.0 45.0 ± 10.4† 56.2 ± 12.5†‡ 63.5 ± 13.7†‡§ 14.7% <0.0001 0.49 0.16 Day 2 24.6 ± 3.7 39.6 ± 7.6† 57.0 ± 12.3†‡ 66.4 ± 13.4†‡§ CV 12.9 [4.1, 26.7] 16.0 [5.3, 26.4] 15.8 [3.6, 23.5] 14.3 [4.9, 28.6] TTIDIA Day 1 0.08 ± 0.02 0.16 ± 0.03† 0.20 ± 0.04†‡ 0.23 ± 0.03†‡§ 16.3% <0.0001 0.36 0.15 Day 2 0.07 ± 0.01 0.14 ± 0.02† 0.20 ± 0.03†‡ 0.24 ± 0.04†‡§ CV 15.8 [8.2, 27.5] 17.4 [6.4, 27.3] 16.9 [6.5, 26.3] 14.9 [6.9, 27.0] TTIRESP Day 1 0.00 ± 0.00 0.05 ± 0.01† 0.10 ± 0.01†‡ 0.15 ± 0.02†‡§ 9.2% <0.0001 0.45 0.89 Day 2 0.00 ± 0.00 0.05 ± 0.01† 0.10 ± 0.01†‡ 0.15 ± 0.02†‡§ CV 6.6 [0.0, 25.0] 10.4[3.6, 20.7] 9.9 [4.4, 21.0] 10.1 [5.7, 22.3] Respiratory Work Breath-by-breath PTPM, cmH2O·s Day 1 -2.1 ± 0.7 -30.3 ± 9.0† -58.1 ± 17.1†‡ -86.8 ± 27.5†‡§ 9.6% <0.0001 0.83 0.99 Day 2 -1.7 ± 0.4 -30.3 ± 9.2† -58.8 ± 19.3†‡ -86.5 ± 30.2†‡§ CV 11.8 [7.1, 18.7] 9.6 [4.9, 17.0] 8.9 [4.8, 15.0] 8.1 [5.5, 12.9] PTPESO, cmH2O·s Day 1 -10.6 ± 4.2 -41.5 ± 11.9† -73.9 ± 19.7†‡ -101.1 ± 30.9†‡§ 19.6% <0.0001 0.15 0.93 Day 2 -9.4 ± 3.1 -40.0 ± 10.0† -69.5 ± 19.8†‡ -97.3 ± 29.0†‡§ CV 29.6 [14.8, 75.6] 20.7 [9.7, 53.6] 14.5 [6.0, 26.5] 12.7 [7.2, 31.3] PTPDIA, cmH2O·s Day 1 21.6 ± 7.5 65.1 ± 23.0† 95.4 ± 28.9†‡ 113 ± 26.2†‡§ 23.3% <0.0001 0.23 0.30 Day 2 21.2 ± 7.4 62.6 ± 16.7† 101.3 ± 26.6†‡ 124.9 ± 30.5†‡§ CV 28.2 [15.4, 66.5] 26.5 [9.2, 109.0] 20.6 [8.4, 40.6] 17.1 [8.2, 34.6] 66 Inspiratory Pressure Threshold Loading Trials P-value Day Baseline Stage 1 Stage 2 Stage 3 CV Total TRIAL DAY X Cumulative sum PTPM, cmH2O·s Day 1 152 ± 56 2279 ± 679† 4338 ± 1282†‡ 6430 ± 2046†‡§ 7.4% <0.0001 0.88 0.99 Day 2 131 ± 29 2272 ± 676† 4389 ± 1487†‡ 6391 ± 2193†‡§ CV 13.9 [0.6, 55.6] 4.9 [0.0, 17.6] 5.2 [0.6, 15.5] 5.0 [0.7, 23.1] PTPESO, cmH2O·s Day 1 784 ± 318 3120 ± 890† 5516 ± 1470†‡ 7498 ± 2324†‡§ 8.9% <0.0001 0.13 0.91 Day 2 707 ± 226 3002 ± 738† 5185 ± 1533†‡ 7189 ± 2114†‡§ CV 11.9 [2.0, 30.7] 8.3 [0.4, 22.6] 7.1 [0.8, 19.2] 8.1 [2.6, 22.6] PTPDIA, cmH2O·s Day 1 1602 ± 557 4894 ± 1707† 7114 ± 2130†‡ 8390 ± 1938†‡§ 12.1% <0.0001 0.26 0.32 Day 2 1583 ± 550 4693 ± 1236† 7539 ± 2005†‡ 9234 ± 2253†‡§ CV 15.5 [0.7, 44.0] 15.0 [2.0, 38.5] 10.4 [0.1, 35.3] 6.9 [0.9, 18.8] N = 16; Values are presented as mean ± SD. Coefficients of variation are expressed as mean [minimum, maximum]. Significance was set to P <0.05. * Significant difference between days. † Significant difference compared to BL. ‡ Significant difference compared to S1. § Significant difference compared to S2. Abbreviations: CV, coefficient of variation; ITL, inspiratory pressure threshold loading; P, probability for a difference between conditions based on linear mixed models; P̅DIA, mean inspiratory transdiaphragmatic pressure; P̅ESO, mean inspiratory esophageal pressure; P̅GAS, mean inspiratory gastric pressure; P̅M, mean inspiratory mouth pressure; PTPDIA, pressure-time product of the transdiaphragmatic pressure; PTPESO, pressure-time product of the esophageal pressure; PTPM, pressure-time product of mouth pressure; TTIDIA, tension-time index of the diaphragm; TTIRESP, tension-time index of the respiratory muscles; X, interaction effect of trial and day. 67 Figure 4.2. Grouped respiratory pressure parameters throughout inspiratory loading trials. Data are presented as 15 second bins with means ± standard deviations for 16 participants. Abbreviations: P̅DIA, mean inspiratory transdiaphragmatic pressure; P̅ESO, mean inspiratory esophageal pressure; P̅GAS, mean inspiratory gastric pressure; P̅M, mean inspiratory mouth pressure; PTPDIA, pressure-time product of the transdiaphragmatic pressure; PTPESO, pressure-time product of the esophageal pressure; PTPM, pressure-time product of mouth pressure; TTIDIA, tension-time index of the diaphragm. 68 4.2.3 Cardiovascular Measurements Average cardiovascular measurements for the 5-minute experimental trials are presented in Table 4.4 and 15-second averages for cardiovascular variables are presented in Figure 4.3. There were main effects of both trial (P < 0.0001) and day (P = 0.0003) for HR. HR was higher on day 1 compared to day 2 (3.0 [1.3, 4.6] bpm; P = 0.0005). There was a graded increase in HR compared to BL for S1 (4.9 [1.8, 7.9] bpm), S2 (11.2 [9.2, 14.3] bpm), and S3 (14.1 [11.0, 17.2] bpm; all P < 0.0001). HR was also greater in S2 (6.4 [3.3, 9.4] bpm; P < 0.0001) and S3 (9.2 [6.1, 12.3] bpm; P < 0.0001) compared to S1. HR was not different for S2 and S3 (2.8 [-0.3, 5.9] bpm; P = 0.09). There were main effects of both trial (P = 0.0008) and day (P = 0.002) for Q̇. Q̇ was greater on day 2 compared to day 1 (0.5 [0.2, 0.8] l·min-1; P = 0.002). Further, Q̇ was greater at S2 (0.8 [0.2, 1.3] l·min-1; P = 0.004) and S3 (0.8 [0.2, 1.4] l·min-1; P = 0.003) compared to BL. There was a main effect of day for DBP, MAP, and SV. DBP (3.8 [1.7, 5.9] mmHg; P = 0.0006) and MAP (3.5 [1.2, 5.8] mmHg; P = 0.003) were higher on day 1 compared to day 2. Conversely, SV (9.2 [5.0, 13.4] ml; P < 0.0001) were greater on day 2 compared to day 1. SBP and TPR were not statistically significant for trials or day (Table 4.4). 69 Table 4.4. Summary of cardiovascular data averaged over the 5 minutes of inspiratory pressure threshold loading for both experimental days. Inspiratory Pressure Threshold Loading Trials P-value Day BL S1 S2 S3 CV Total TRIAL DAY X Cardiovascular HR, beats·min-1 Day 1 72 ± 11 76 ± 11† 84 ± 12†‡ 85 ± 11†‡ 5.5% <0.0001 <0.0001 0.71 Day 2* 69 ± 13 74 ± 11† 79 ± 12†‡ 84 ± 10†‡ CV 5.0 [2.9, 9.5] 5.4 [3.1, 13.4] 5.7 [3.3, 14.5] 6.1 [3.4, 13.9] SV, ml Day 1 65 ± 19 65 ± 16 65 ± 17 63 ± 19 13.5% 0.90 <0.0001 0.98 Day 2* 75 ± 21 76 ± 24 75 ± 23 70 ± 18 CV 10.2 [3.6, 50.7] 10.9 [4.8, 35.0] 15.2 [5.4, 38.6] 18.5 [9.0, 55.0] Q̇, l·min-1 Day 1 4.6 ± 1.5 4.9 ± 1.2 5.4 ± 1.3† 5.3 ± 1.4† 13.9% 0.0008 0.002 0.94 Day 2* 5.1 ± 1.4 5.6 ± 1.5 5.9 ± 1.6† 5.9 ± 1.5† CV 10.6 [4.1, 50.8] 11.5 [5.1, 34.4] 15.6 [6.4, 40.1] 18.6 [9.5, 55.7] SBP, mmHg Day 1 123 ± 12 130 ± 17 127 ± 14 130 ± 16 6.4% 0.17 0.09 0.41 Day 2 124 ± 14 124 ± 10 121 ± 8 126 ± 13 CV 4.1 [1.8, 7.3] 4.9 [2.4, 12.6] 8.0 [3.9, 22.6] 8.6 [4.9, 37.3] DBP, mmHg Day 1 76 ± 7 80 ± 13 77 ± 9 81 ± 12 7.7% 0.09 0.0003 0.76 Day 2* 72 ± 9 73 ± 8 73 ± 7 76 ± 9 CV 5.6 [3.2, 8.7] 6.4 [3.5, 11.7] 8.5 [4.7, 13.9] 10.7 [7.0, 19.6] MAP, mmHg Day 1 92 ± 8 97 ± 13 94 ± 10 97 ± 14 6.5% 0.06 0.002 0.60 Day 2* 89 ± 10 90 ± 7 89 ± 7 92 ± 10 CV 4.5 [2.3, 8.1] 5.3 [3.2, 12.0] 7.5 [3.9, 13.0] 9.1 [5.8, 25.3] TPR, mmHg·min·l-1 Day 1 20.2 ± 5.3 20.8 ± 7.8 18.0 ± 5.1 19.1 ± 5.9 15.2% 0.12 0.09 0.82 Day 2 18.2 ± 4.9 16.9 ± 4.6 16.4 ± 5.2 16.9 ± 4.5 CV 11.0 [5.4, 21.2] 12.6 [5.1, 25.5] 16.2 [6.5, 26.8] 21.7 [9.2, 87.0] N = 16; Values are presented as mean ± SD. Coefficients of variation are expressed as mean [minimum, maximum]. Significance was set to P<0.05. * Significant difference between days. † Significant difference compared to BL. ‡ Significant difference compared to S1. § Significant difference compared to S2. Abbreviations: CV, coefficient of variation; DBP, diastolic blood pressure; HR, heart rate; MAP, mean arterial pressure; P, probability for a difference between conditions based on linear mixed models; Q̇. cardiac output; SBP, systolic blood pressure; SV, stroke volume; TPR, total peripheral resistance; X, interaction effect of trial and day. 70 Figure 4.3. Group cardiovascular parameters throughout inspiratory loading. Data are presented as 15 second bins with means ± standard deviations for 16 participants. Abbreviations: 71 DBP, diastolic blood pressure; MAP, mean arterial pressure; Q̇, cardiac output; SBP, systolic blood pressure; SV, stroke volume; TPR, total peripheral resistance. 4.2.4 End-Expiratory Lung Volumes and Apnea Durations during CEUS Measurements End-expiratory lung volume (EELV) and length of apnea required for CEUS measurement are presented in Table 4.5. There were main effects of trial and day for EELV. EELV was greater on day 2 (0.4 [0.2, 0.6] l; P < 0.0001). EELV as a %FRC was also greater on day 2 (11.5 [6.8, 16.2] %; P < 0.0001). BL EELV was greater compared to S2 as both an absolute volume (0.4 [ 0.0, 0.7] l; P = 0.04) and as a %FRC (10.8 [2.2, 19.5] %; P = 0.007). Table 4.5. Group end-expiratory lung volumes and apnea durations during CEUS measurements Baseline Stage 1 Stage 2 Stage 3 P Trial Length, s TRIAL DAY X Day 1 319 ± 16 318 ± 11 314 ± 6 328 ± 28 0.47 0.61 0.26 Day 2 328 ± 32 317 ± 13 321 ± 30 318 ± 16 Apnea Length, s Day 1 24 ± 2 23 ± 2 22 ± 2 24 ± 2 0.14 0.95 0.28 Day 2 24 ± 3 24 ± 3 23 ± 3 22 ± 2 EELV, l Day 1 4.2 ± 1.8 4.1 ± 2.1 3.8 ± 1.8† 3.9 ± 1.7 0.03 <0.001 0.85 Day 2* 4.7 ±1.9 4.6 ± 2.0 4.3 ± 2.0† 4.5 ± 2.5 EELV, %FRC Day 1 110 ± 18 105 ± 17 98 ± 17† 99 ± 17 0.005 <0.001 0.73 Day 2* 121 ± 13 121 ± 20 111 ± 14† 115 ± 30 IC, %Baseline Day 1 100 ± 0 103 ± 32 105 ± 55 119 ± 27 0.35 0.71 0.03 Day 2* 100 ± 0 117 ± 25 124 ± 25 73 ± 119*§ N = 16; Values are mean ± SD. * Significant difference compared to day 1 (P<0.05). † Significant difference compared to BL (P<0.05). § Significant difference compared to S2 (P<0.05). Abbreviations: EELV, end-expiratory lung volume; FRC. Functional residual capacity; IC, inspiratory capacity; P, probability for a difference between conditions from linear mixed models; X, interaction effect of TRIAL and DAY. 72 4.3 Blood Flow Measurements Diaphragm blood flow data are presented in Figure 4.4 and 4.5. Additionally, sex differences blood flow data are presented in Appendix B. There were no interaction effects of day and trial for Q̇DIA measurements. There were no main effects of trial (P = 0.42) or day (P = 0.32) for diaphragm MBV (Figure 4.5A). There was a main effect of trial (P < 0.0001) but not day (P = 0.68) for diaphragm MBF (Figure 4.5B). Diaphragm MBF was significantly greater at S1 (0.27 [0.12, 0.41] s-1; P < 0.0001), S2 (0.51 [0.36, 0.65] s-1; P < 0.0001), and S3 (0.59 [0.45, 0.74] s-1; P < 0.0001) compared to BL. MBF increased from S1 to both S2 (0.24 [0.09, 0.39] s-1; P = 0.0002) and S3 (0.33 [0.18, 0.48] s-1; P < 0.0001). However, MBF was not different between S2 and S3 (0.09 [-0.06, 0.24] s-1; P = 0.42). Q̇DIA had a main effect of trial (P < 0.0001) but not day (P = 0.22). Q̇DIA increased compared to BL for S1 (3.9 [1.8, 5.9] AU·s-1; P < 0.0001), S2 (7.9 [5.9, 10.0] AU·s-1; P < 0.0001), and S3 (10.4 [8.3, 12.5] AU·s-1; P < 0.0001). Q̇DIA also increased from S1 to both S2 (4.1 [2.0, 62] AU·s-1; P < 0.0001) and S3 (6.6 [4.5, 8.7] AU·s-1; P < 0.0001). Finally, Q̇DIA increased from S2 to S3 (2.5 [0.4, 4.6] AU·s-1; P = 0.01). Diaphragm vascular conductance (VCDIA) progressively increased from baseline (0.03 ± 0.01 AU·s-1·mmHg-1) to S1 (0.08 ± 0.01 AU·s-1·mmHg-1), S2 (0.12 ± 0.01 AU·s-1·mmHg-1), and S3 (0.14 ± 0.01 AU·s-1·mmHg-1). VCDIA had a main effect of trial (P < 0.0001) but not day (P = 0.17). Specifically, VCDIA increased compared to BL for S1 (0.04 [0.02, 0.07] AU·s-1·mmHg-1; P < 0.0001), S2 (0.09 [0.06, 0.11] AU·s-1·mmHg-1; P < 0.0001), and S3 (0.10 [0.08, 0.13] AU·s-1·mmHg-1; P < 0.0001). VCDIA also increased from S1 to both S2 (0.05 [0.02, 0.07] AU·s-73 1·mmHg-1; P < 0.0001) and S3 (0.06 [0.04, 0.09] AU·s-1·mmHg-1; P < 0.0001). However, VCDIA was not different between S2 and S3 (0.02 [-0.01, 0.04] AU·s-1·mmHg-1; P = 0.21). Liver MBV was not statistically significant by trial (p = 0.15), day (P = 0.09), or interaction (P = 0.71). Liver MBV were 38.5 ± 2.5 AU, 37 ± 2.5 AU, 37.6 ± 2.5 AU, and 34.4 ± 2.5 AU for BL, S1, S2, and S3, respectively. There was an interaction effect of trials and day for liver MBF (P = 0.03). Liver MBF was greater on day 2 compared to day 1 for both the S1 trial (0.06 [0.00, 0.11] s-1; P = 0.04) and S3 trial (0.12 [0.06, 0.17] s-1; P < 0.0001). Additionally, S3 had a greater MBF than BL on day 2 (0.08 [0.01, 0.15] s-1; P = 0.02). There was an interaction of trials and day for Q̇LIV (P = 0.04). Q̇LIV was greater on day 2 compared to day 1 for both S1 (2.3 [0.3, 4.4] AU·s-1; P = 0.03) and S3 (4.7 [2.6, 6.8] AU·s-1; P < 0.0001). Further, liver vascular conductance (VCLIV) had a main effect of day (P < 0.0001) but not trial (P = 0.82). Specifically, VCLIV was lower on day 1 (0.08 ± 0.01 AU·s-1·mmHg-1) compared to day 2 (0.11 ± 0.01 AU·s-1·mmHg-1). This gave a day contrast of 0.03 [0.02, 0.04] AU·s-1·mmHg-1 for day 2 compared to day 1 (P < 0.0001). 74 Figure 4.4. Destruction-replenishment curves for diaphragm and liver blood flows. (A) Average time-intensity data from the diaphragm at graded levels of ITL. (B) Average time-intensity data from liver at graded levels of ITL. Ultrasound images are corresponding background-subtracted color-coded CEUS images of an individual participant at different levels 75 of ITL. Data in sub-tables for A and B are average microvascular flow values on a day-to-day and trial-to-trial basis. Figure 4.5. Diaphragm blood flow measurements during graded increases in inspiratory pressure threshold loading. † P<0.05 compared with BL. ‡ P<0.05 compared with S1. § P<0.05 compared with S2. 4.4 Relationship between Q̇DIA and Respiratory Work Figure 4.6A depicts individual participant responses and the linear mixed effects models that describe the association between Q̇DIA and respiratory muscle work. Individual participant responses are presented in Appendix A. For %MIP, greater total respiratory work was associated with greater Q̇DIA (P < 0.0001) and this relationship was not different between experimental days (P = 0.43; Figure 4.6A). A similar relationship was found for %PDIMAX where greater transdiaphragmatic pressures were associated with greater Q̇DIA (P < 0.0001) with no effect of day on this relationship (P = 0.34; Figure 4.6B). The average breath-by-breath pressure-time product of the diaphragm was also related to Q̇DIA (P < 0.0001) with no effect of day on this relationship (P = 0.94; Figure 4.6C). Lastly, the cumulative sum of the pressure-time product of the diaphragm was related to Q̇DIA (P = 0.046) and this relationship was not different from day-to-day (P = 0.90; Figure 4.6D). 76 Figure 4.6. Linear mixed models for the relationship between Q̇DIA and respiratory pressures. Lines fit to individual participant data and group data for %MIP (A), %PDIMAX (B), PTPPDI average (C), and PTPPDI sum (D). Abbreviations: PTPDIA, pressure-time product of the diaphragm; AU, acoustic units; %PDIMAX, percent of maximum transdiaphragmatic pressure. 4.5 Reliability Data 4.5.1 Test-Retest Reliability (Stability Reliability) Diaphragm MBV had poor-to-moderate test-retest reliability (Figure 4.7A) while diaphragm MBF had moderate-to-good test-retest reliability (Figure 4.7B). Both diaphragm MBF and MBV 77 had a bias away from the line of identity. Q̇DIA however had good-to-excellent test-retest reliability (Figure 4.7C) and followed the line of identity. Liver MBV had poor-to-excellent test-retest reliability with a bias towards day 2 (Figure 4.7D). Liver MBF did not correlate from day to day (Figure 4.7E). Q̇LIV had poor-to-moderate test-retest reliability with a bias towards day 2 (Figure 4.7F). Figure 4.7. Intraclass Correlation Coefficient Plots for Test-Retest Reliability. The dashed line is the line of identity. ICCs are presented with 95% confidence intervals. Abbreviations: ICC, intraclass correlation coefficient; ITL, inspiratory pressure threshold loading. 78 Table 4.6. SEM and TEM for Test-Retest Reliability Baseline Stage 1 Stage 2 Stage 3 Day-to-Day Diaphragm A - Volume, AU SEM 5.78 3.88 5.64 3.40 4.81 TEM 5.62 3.78 5.77 3.33 4.81 Β - Flux, s-1 SEM 0.09 0.17 0.22 0.15 0.16 TEM 0.09 0.17 0.22 0.15 0.16 Aβ - Flow, AU·s-1 SEM 1.34 1.58 3.13 2.03 2.18 TEM 1.31 1.58 3.19 2.31 2.21 Liver A - Volume, AU SEM 6.58 8.19 7.85 5.68 7.14 TEM 6.98 7.92 7.66 6.47 7.29 β - Flux, s-1 SEM 0.07 0.07 0.06 0.10 0.08 TEM 0.07 0.08 0.06 0.12 0.08 Aβ - Flow, AU·s-1 SEM 3.00 2.64 2.11 4.29 3.26 TEM 2.91 3.01 2.35 5.43 3.55 Based on N = 16; Abbreviations: AU, acoustic units; ITL, inspiratory pressure threshold loading; SEM, standard error of measurement; TEM, technical error of measurement Due to the homoscedastic nature of the Q̇DIA data, SEMs and TEMs were computed in lieu of CV242. SEM and TEM on a trial-by-trial basis and on average between days are presented in Table 4.6. Bland-Altman plots with LOA for test-retest reliability are presented in Figure 4.8. Mean bias ± 95% LOA for test-retest diaphragm MBV, MBF and Q̇DIA were 0.9 ± 13.3 AU, 0.01 ± 0.46 s-1, and 0.6 ± 6.1 AU·s-1, respectively. Mean bias ± 95% LOA for test-retest liver MBV, MBF, and Q̇LIV were 2.5 ± 19.8 AU, 0.04 ± 0.22 s-1, and 2.1 ± 9.0 AU·s-1, respectively. 79 Figure 4.8. Bland-Altman Plots for Test-Retest Reliability. 4.5.2 Inter-Observer Reliability (Objective Reliability) Diaphragm MBV had moderate-to-good inter-observer reliability (Figure 4.9A) while diaphragm MBF had good-to-excellent inter-observer reliability (Figure 4.9B). Q̇DIA however had excellent inter-observer reliability (Figure 4.9C). Liver MBV and MBF had good-to-excellent inter-observer reliability (Figure 4.9D,E). Q̇LIV had excellent inter-observer reliability (Figure 4.9F). 80 Figure 4.9. Intraclass Correlation Coefficient Plots for Inter-Observer Reliability. The dashed line is the line of identity. ICCs are presented with 95% confidence intervals. Abbreviations: ICC, intraclass correlation coefficient; ITL, inspiratory pressure threshold loading. 81 Table 4.7. SEM and TEM for Inter-Observer Reliability. Baseline Stage 1 Stage 2 Stage 3 Observer Diaphragm A - Volume, AU SEM 3.54 3.27 2.71 2.97 3.15 TEM 3.66 3.26 2.69 3.05 3.18 β - Flux, s-1 SEM 0.09 0.13 0.14 0.16 0.13 TEM 0.10 0.13 0.14 0.16 0.13 Aβ - Flow, AU·s-1 SEM 0.75 1.18 2.02 2.02 1.58 TEM 0.78 1.17 1.99 2.03 1.58 Liver A - Volume, AU SEM 2.29 3.59 5.13 3.79 3.83 TEM 2.26 3.62 5.05 3.81 3.82 β - Flux, s-1 SEM 0.02 0.02 0.04 0.04 0.03 TEM 0.02 0.02 0.04 0.04 0.03 Aβ - Flow, AU·s-1 SEM 0.66 0.39 0.98 0.88 0.76 TEM 0.65 0.38 0.97 0.90 0.76 Based on N = 16; Abbreviations: AU, acoustic units; ITL, inspiratory pressure threshold loading; SEM, standard error of measurement; TEM, technical error of measurement SEM and TEM on a trial-by-trial basis and on average are presented in Table 4.7. Bland-Altman plots with LOA for inter-observer reliability are presented in Figure 4.10. Mean bias ± 95% LOA for inter-observer diaphragm MBV, MBF and Q̇DIA were 0.7 ± 8.7 AU, 0.03 ± 0.36 s-1, and 0.1 ± 4.4 AU·s-1, respectively. Mean bias ± 95% LOA for inter-observer liver MBV, MBF, and Q̇LIV were 0.1 ± 10.6 AU, 0.002 ± 0.08 s-1, and 0.06 ± 2.1 AU·s-1, respectively. 82 Figure 4.10. Bland-Altman Plots for Inter-Observer Reliability. 83 Chapter 5: Discussion 5.1 Main Findings The purpose of this study was to assess the feasibility, validity, and reliability of using CEUS to measure Q̇DIA in humans. We demonstrate that there is a reproducible linear relationship between Q̇DIA and respiratory work on a day-to-day basis. Furthermore, we present indices of both relative and absolute reliability which demonstrate good-to-excellent reliability for both test-retest and inter-observer reliability when assessing Q̇DIA. These findings provide justification that CEUS is a feasible, physiologically valid, and reproducible measurement technique for Q̇DIA in humans across a range of respiratory workloads. 5.2 Validity – Relationship Between Q̇DIA and Respiratory Muscle Work Validity refers to the ability of the measurement tool to reflect what it is designed to measure.242 As there is no gold standard comparison to measure Q̇DIA in humans, there is an inability to compare CEUS-derived estimates of Q̇DIA with an invasive direct measure of Q̇DIA. Instead, comparisons to known Q̇DIA responses in animals and blood flow responses in other human skeletal muscles are necessary to determine if CEUS-derived Q̇DIA responses are physiologically sound. There was a reproducible linear relationship between Q̇DIA and respiratory muscle work (Figure 4.6). Based on the Fick equation (Equation 8), greater levels of respiratory muscle work require a proportional fraction of Q̇ which has been supported in animal15 and human models.243 Equation 8. Fick Equation ?̇?𝑂2 = ?̇? × 𝐶(𝑎−?̅?)𝑂2 84 Animal work supports linearity between Q̇DIA and PDIA from 10 – 65% PDIMAX.103 Further, diaphragmatic V̇O2 is linearly related to both Q̇DIA16,60,77,135,150 and PTPDIA163 in animals, suggesting linearity between Q̇DIA and PTPDIA. Similarly, work in humans shows commensurate increases in V̇O2, Q̇, and locomotor muscle blood flow with incremental lower body exercise to exhaustion thus demonstrating that a linear relationship between blood flow and muscle work also applies to other skeletal muscles in humans.243–245 This suggests CEUS-derived Q̇DIA measurements have valid physiological underpinnings as corroborated by animal and human work. 5.3 Reliability – Test-Retest Reliability Relative reliability measurements are influenced by multiple sources of variation (e.g., participant, raters, trials) and error. In this study, there was good-to-excellent and poor-to-moderate test-retest reliability for Q̇DIA and Q̇LIV, respectively (Figure 4.7). Similar test-retest reliabilities have been demonstrated in the gastrocnemius (ICC > 0.73),198,208 deltoid (ICC = 0.91 [0.78, 0.96]),199 and liver (0.2 < ICC < 0.9).246 Absolute reliability measures (i.e., SEM, TEM, LOA) consist of biological and technological variations.247 The SEM is quite large (± 2.2 AU·s-1 for Q̇DIA) and point to a relative percent error of >15% day-to-day. Multiple factors including EELVs,89 Q̇ (CV = 14%), P̅DIA (CV = 15%), V̇E (CV = 18%), and PETCO2 (CV = 8%) contribute to biological variability135,165 and likely influence SEM. Technical error may arise from differences in transducer placement leading to insonation of tissue with differing perfusion characteristics (Table 1.1)22 and from variability in preparation and handling of microbubbles.198 Also worth noting, the mean bias for diaphragm measures were small (Figure 4.7), suggesting minimal influence of day. This mean 85 bias for both the diaphragm and liver are likely attributable to Q̇ being significantly elevated on day 2 and inherent day-to-day variation in resting muscle perfusion.248 Further evidence of test-retest reliability is supported by the relationship between Q̇DIA and respiratory work from day-to-day (Figure 4.6). The intercept for the relationship between Q̇DIA and respiratory work was considered coincident between days and the slopes were parallel. These relationships highlighted that Q̇DIA responses are reliable when accounting for trial variability in respiratory muscle work. Finally, the diaphragm has comparatively better test-retest reliability than the liver. Particularly, SEM for Q̇DIA was ~67% the SEM of Q̇LIV (Table 4.6) which is consistent with other CEUS skeletal muscle198,199,208 and liver246 reliability studies. 5.4 Reliability – Inter-Observer Reliability There was a moderate-to-excellent (ICC > 0.68) inter-observer reliability for all diaphragm and liver metrics (Figure 4.9). Similar inter-observer reliabilities have been found in the static deltoid muscle (ICC = 0.84 [0.54, 0.84]; P < 0.0001)199 and liver (ICC > 0.62).246 Notably however, LOA are greater in the diaphragm compared to the liver (Figure 4.10). This likely arises from smaller diaphragm ROIs, heterogeneity of Q̇DIA, and greater ultrasound artifact in the diaphragm. Small diaphragm ROIs are unavoidable as a function of anatomical size and increase variability. Regional heterogeneity of blood flow within the costal diaphragm exists (Table 1.1) making ROI selection and measurement more observer dependent. Lastly, intramuscular tendinous aspects and intercostal arteries21 appear hyperechoic in the diaphragm and can lead to measurement artifact. Observer ROI selection is then influenced by how to best minimize measurement artifact. Despite these challenges, both ICCs and LOA support that CEUS measurements of the diaphragm are reliable and have similar inter-observer reliability to previous CEUS studies.199,246 86 5.5 Sex Differences in Diaphragm Blood Flow Sex differences were not a primary objective of the current study; however, sex differences were evaluated from an exploratory analysis to help inform future work (see Appendix B). There were no main effects of sex on Q̇DIA in the present study using linear mixed models for day, trials, and sex. This finding may be due to a combination of the following factors. First, our study is underpowered to detect sex differences since only six females were included in analysis and two of them had notable variability in their Q̇DIA responses (CEUS12, CEUS16; Appendix A). Q̇DIA was elevated at BL in both participant and in CEUS16 Q̇DIA decreases compared to BL which is unphysiological. Second, the absolute volume of microbubbles administered was fixed for all participants and differences in body size between sexes were not accounted for245,249 leading to differences in microbubble acoustic concentrations. This likes artificially inflates diaphragm MBV and MBF in females compared to males. Further, females have known lower hepatic blood flows250 which was not observed in the present study, which supports that MBV and Q̇LIV were artificially inflated in female participants. Thus, for male and female participants to be compared with CEUS, the contrast intensity of the arterial pool is needed so absolute measures of blood flow can be obtained.201,251 Lastly, female participants have on average thinner diaphragms213 which reduces the size of the ROI that can be used to assess Q̇DIA. With smaller ROIs, the overall Q̇DIA measurements have less pixels to average from which can increase measurement variability. Particularly when blood flow is low (e.g., BL), small ROIs are highly susceptible to variability and may depend more on microbubble concentration and infusion rate. Qualitatively, there is a difference between male and female participant blood flow responses at BL (Appendix B, Table 6.1), but this variability may also be due to greater variability in measurement in 87 women. Of note, there are only two Q̇DIA sex differences studies in animals. The first study found elevated Q̇DIA in females (which numerically agrees with our study)70 while the second study found similar Q̇DIA between sexes (which statistically agrees with our data).52 Because of these considerations, it is not advisable to directly compare Q̇DIA between sexes in our study but instead focus on the relative changes in Q̇DIA with ITL which were similar. 5.6 Methodological Considerations There are multiple methodological considerations that need to be evaluated that can broadly be classified into anatomy and physiology considerations, ultrasound measurement and data processing considerations, and reliability and error considerations. Herein, discussion on each of these aspects is explored as they are imperative to understand for future adopters should they want to use CEUS in the diaphragm. 5.6.1 Respiratory Muscle Anatomy & Physiology Considerations A necessary aspect of acquiring Q̇DIA data is the inclusion of an end-expiratory apnea. Associated limitations arise however as i) post-occlusive reactive hyperemia arises with higher duty cycles/TTIDIA,109 ii) Q̇DIA changes as a function of muscle length,89,109 and iii) the diaphragm quickly (~30 s) reduces Q̇DIA when not in use.38 First, during apnea the intramuscular tension causing compression of blood vessels is relieved and Q̇DIA can surge during the expiratory/relaxation phase.3,76,109 This hyperemia is likely exacerbated with greater duty cycles (>0.5) and P̅DIA.109 Second, greater diaphragm fiber lengths (i.e., EELV closer to RV) reduce Q̇DIA.89 Smaller EELVs on day 1 (Table 4.5) likely reduced Q̇DIA responses through a mechanical effect of passive tension on vascular caliber.89 Lastly, the speed at which a Q̇DIA measurement is 88 taken after breathing cessation may dictate Q̇DIA magnitude. In our data, apnea lengths were not different (Table 4.5) but individual variability exists and may have contributed to day-to-day variability. Unfortunately, the need to obtain measurements during apnea is unavoidable but the associated limitations can be minimized with familiarization, coaching participants to FRC, and ensuring sonographers are well trained in measurement acquisition. For anatomy, the diaphragm exhibits interregional heterogeneity for structure (i.e., fiber lengths and thicknesses) and function (i.e., fibers length changes and blood flow).22 This can become an issue for the CEUS technique as there are multiple intercostal spaces that can be used to measure Q̇DIA each with their own specific thicknesses and microvascular flows. During greater ventilations, it can be challenging to insonate the same intercostal space and anatomical location with dynamic posture and chest dimensions. Thus, it is critical to mark the site of probe placement for anatomical consistency from measure-to-measure as this greatly improves measurement reliability.210 The human diaphragm has intramuscular tendinous aspects that run through the center of the muscle21 and are hyperechoic on ultrasound. Muscular activity (e.g., breaking apnea) or movement of the ultrasound probe measurement can cause these structures to move in and out of the ultrasound field of view which can significantly influence the overall image intensity and affect the measure of blood flow during video intensity analysis. Similarly, intercostal arteries run through the medial costal diaphragm,175 appear hyperechoic on ultrasound, and can move in and out of frame. With contrast, the intercostal arteries have relatively high blood flows compared with the diaphragm microvasculature. For experimental trials, it is crucial to consistently sample from the skeletal muscle microvasculature and not from the intercostal 89 artery.198 As a sonographer, it is advisable to avoid the intercostal arteries and tendinous aspects when possible to minimize artifact in video intensity analysis. 5.6.2 Ultrasound Measurement and Data Processing Considerations Lower ultrasound frame rates (3 Hz) were not always sufficient to adequately capture reperfusion data at higher levels of ITL. High frames rates have been used in other skeletal muscle (≥10 Hz)198,208 and are recommended for greater respiratory efforts as MBV and MBF measurements are likely underestimated at lower frame rates. Also worth considering, current data is only comparable within-participant. This arises as different diaphragm depths, focal positions, near and far field acoustics, ultrasound gains, and body masses impact the acoustic signal on a participant-by-participant basis. It is advisable to keep ultrasound settings consistent between repeated measures as to avoid quantification error that arises from a change in settings and not from a true physiological response.198,199 5.6.3 Reliability and Error Considerations TEM assumes that there are no systemic errors (e.g., learning effects, fatigue) present for the test-retest reliability measurements. Learning effects are unlikely as maximal pressures are reproducible (~5% day-to-day variability in PDIMAX, FRC) and pertinent indices (e.g., duty cycle, fB, P̅DIA,) were not different day-to-day (Table 4.2 & 4.3). Fatigue was also unlikely as participants were tested at least 48 hours apart but within a week of the first experimental day to minimize any differences that might arise from muscle conditioning or lifestyle influences. As such, systemic influences are minimal, and TEM is likely a valid error measurement in this study. 90 CEUS-derived blood flow values are dependent on the dilution and infusion protocol used,252 the microbubble agent,253 ultrasound settings,252,254 and ultrasound machine.199,230,253,254 As such, reliability results (SEM, TEM, LOA) are ultrasound machine specific and may not apply to other ultrasound machines using different contrast detection algorithms and settings. While this does not diminish the importance of assessing error and reliability in our current study, it does limit its inter-ultrasound applicability at present. However, absolute blood flows (in ml/g) can be determined by comparing the microbubble signal intensity of tissue to that of the blood pool201,251 thereby permitting comparison between ultrasound machines in the future. Experimental trials were indexed to %MIP which accounts for overall respiratory muscle pressure generation without isolating Q̇DIA responses. As such, indexing to %PDIMAX would have been preferrable as i) diaphragmatic pressures and Q̇DIA are directly related,103,163 ii) PDIMAX is measured and is greatest at FRC255 which is closer to our operational lung volumes than MIPs measured at RV, and iii) visual feedback for participants would be contingent on changes in P̅DIA without influence of accessory muscle recruitment (e.g., sternocleidomastoid). Notably there was considerable intra-individual variability in diaphragm pressures generated for the same trial on a day-to-day basis (see Appendix A) which conceivably reduced test-retest reliability. 5.6.4 Sample Size Consideration Statisticians advocate that precision of error estimates are adequate if sample sizes in test-retest reliability studies are at least 40 participants241 while other statisticians advocate for >50 participants.247 These statisticians do note that such sample sizes can be challenging to achieve if the measurement is invasive or complicated as was the case in our study. However, having multiple retest measures in a reliability study can mitigate this problem.241 A sample size of 16 91 with four retest measure was adequate to determine reliability and is in line with previous CEUS reliability studies (15-29 participants).198,199,208,220,246 5.7 Utility and Future Applications The results of this study indicate non-invasive measurement of Q̇DIA via CEUS is a viable method in humans and could be used for widespread applications in basic science, human performance, and clinical medicine. Herein, several future applications for future investigation are outlined. 5.7.1 Basic Science Preliminary work should focus on fundamental understanding of blood flow to the respiratory muscles in humans. As explored in Figure 1.1 and 1.2, basic relationships between Q̇DIA and duty cycle, P̅DIA, TTIDIA, fB, and VT would be informative to establish a basis of knowledge in humans. CEUS could also be used on other respiratory muscles (e.g., parasternal intercostal) to delineate respiratory muscle blood flow distribution. Lastly, investigating the influence of sex and aging249 on respiratory muscle blood flow regulation would provide further foundational understanding of human respiratory physiology. From a translational perspective, human physiology textbooks estimate Q̇DIA in humans to be ~10-15 ml/min/100g with normal resting respiratory muscle blood flows accounting for 2% of Q̇.163 These estimates are based on animal studies8–10,78 and whether they are accurate is unknown. CEUS can measure absolute blood flows (in ml/g) by comparing the microbubble signal intensity of tissue to that of the blood pool.201,251 With absolute blood flows, it would be 92 possible to determine what proportion of blood flows to the diaphragm and other respiratory muscles and determine whether textbook estimates align with human CEUS measurements. 5.7.2 Human Performance Locomotor and respiratory muscles compete for a finite blood supply during exercise.193 Animal work shows concurrent increases in Q̇DIA and locomotor blood flows up to V̇O2max at the expense of splanchnic blood flows135 while human work has shown both locomotor muscle blood flow steal183,185,191 and respiratory muscle blood flow steal.243,256,257 This is further confounded by training status playing a role with blood flow distribution.14,18,59,91 However, no study has simultaneously measured locomotor and diaphragm blood flows in humans.193 Our measure of Q̇DIA may be able to address this important question with a major challenge being able to apply our technique to an exercising human. 5.7.3 Clinical Medicine Mechanical ventilation (MV) reduces Q̇DIA in animals4,8,50,57,81 and prolonged MV in humans and animals induces diaphragm atrophy, contractile dysfunction, oxidative stress, and mitochondrial dysfunction.55 These MV-induced reductions in Q̇DIA have been linked to both endothelium-dependent and endothelium-independent mechanisms258 and are further exacerbated by positive end-expiratory pressures,55 hyperoxia,57 and time spent on the ventilator.50 Further, electrical stimulation is ineffective in augmenting Q̇DIA after prolonged MV, suggesting reduced dilatory capacity and/or enhanced contractile responses in resistance vessels that arise from time on MV.50,258 Future work investigating Q̇DIA responses to MV in humans is warranted. 93 Therapeutic applications may have utility in clinical medicine if Q̇DIA impacts diaphragm function. Specifically, ultrasound augments tissue perfusion and the intravascular presence of microbubbles in conjunction with high-powered ultrasound has been shown to amplify muscle blood flow.201 The acoustic cavitation of microbubbles within the microcirculation produces high focal shear. This phenomenon relies on shear-dependent increases in ATP which act through purinergic signaling pathways.254 Knowing that Q̇DIA responds to ATP-mediated vasodilation14,34,45 and there are endothelium-dependent pathways present,258 this may be a future way to increase localized Q̇DIA or locally deliver pharmacological intervention. 94 Chapter 6: Conclusion Minimally invasive assessment of Q̇DIA in humans has remained elusive with the prevailing sentiment being that there is an “inability” or “impossibility” to attain such measures. With CEUS measurements, Q̇DIA was linearly related to respiratory muscle work which agrees with Q̇DIA responses in animal models and other skeletal muscle responses in humans. Further, measures of Q̇DIA had good-to-excellent day-to-day reliability and the linear relationship between Q̇DIA and respiratory muscle work was reproducible on a day-to-day basis. Finally, there was good-to-excellent inter-observer reliability for diaphragm and liver measurements which agree with CEUS reliability studies in other skeletal muscles and previous liver work. These findings demonstrate for the first time that CEUS is a minimally invasive, feasible, valid, and reliable way of measuring Q̇DIA in healthy humans. The findings presented in the current thesis advance our understanding of Q̇DIA in humans and offer a brand-new methodology to interrogate complex respiratory physiology in health and disease.95 References 1. Krogh A. The supply of oxygen to the tissues and the regulation of the capillary circulation. J Physiol. 1919;52(6):457. 2. Rous P, Gilding HP. STUDIES OF TISSUE MAINTENANCE: I. The Changes with Diminished Blood Bulk. J Exp Med. 1929;50(2):189-211. 3. Anrep G, Cerqua S, Samaan A. The effect of muscular contraction upon the blood flow in the skeletal muscle, in the diaphragm and in the small intestine. Proc R Soc London Ser B, Contain Pap a Biol Character. 1934;114(788):245-257. 4. Mott JC. The circulation of the thoracic cage in the dog and its reaction to haemorrhage. J Physiol. 1953;121(1):80. 5. Bullard RW, Funkhouser GE. Estimated regional blood flow by rubidium 86 distribution during arousal from hibernation. Am J Physiol Content. 1962;203(2):266-270. 6. Biscoe TJ, Bucknell A. The arterial blood supply to the cat diaphragm with a note on the venous drainage. Q J Exp Physiol Cogn Med Sci Transl Integr. 1963;48(1):27-33. 7. Rudolph AM, Heymann MA. The circulation of the fetus in utero: methods for studying distribution of blood flow, cardiac output and organ blood flow. Circ Res. 1967;21(2):163-184. 8. Robertson CH, Foster GH, Johnson RL. The relationship of respiratory failure to the oxygen consumption of, lactate production by, and distribution of blood flow among respiratory muscles during increasing inspiratory resistance. J Clin Invest. 1977;59(1):31-42. 9. Robertson CH, Pagel MA, Johnson RL. The distribution of blood flow, oxygen consumption, and work output among the respiratory muscles during unobstructed hyperventilation. J Clin Invest. 1977;59(1):43-50. 10. Robertson CH, Eschenbacher WL, Johnson RL. Respiratory muscle blood flow distribution during expiratory resistance. J Clin Invest. 1977;60(2):473-480. 11. Rochester DF, Pradel-Guena M. Measurement of diaphragmatic blood flow in dogs from xenon 133 clearance. J Appl Physiol. 1973;34(1):68-74. 12. Rochester DF. Measurement of diaphragmatic blood flow and oxygen consumption in the dog by the Kety-Schmidt technique. J Clin Invest. 1974;53(5):1216-1225. 13. Rochester DF, Bettini G. Diaphragmatic blood flow and energy expenditure in the dog. Effects of inspiratory airflow resistance and hypercapnia. J Clin Invest. 1976;57(3):661-672. 14. Manohar M. Vasodilator reserve in respiratory muscles during maximal exertion in ponies. J Appl Physiol. 1986;60(5):1571-1577. 15. Manohar M. Blood flow to the respiratory and limb muscles and to abdominal organs during maximal exertion in ponies. J Physiol. 1986;377(1):25-35. 96 16. Manohar M. Costal vs. crural diaphragmatic blood flow during submaximal and near-maximal exercise in ponies. J Appl Physiol. 1988;65(4):1514-1519. 17. Manohar M. Diaphragmatic perfusion heterogeneity during exercise with inspiratory resistive breathing. J Appl Physiol. 1990;68(5):2177-2181. 18. Manohar M. Inspiratory and expiratory muscle perfusion in maximally exercised ponies. J Appl Physiol. 1990;68(2):544-548. doi:10.1152/jappl.1990.68.2.544 19. Parks CM, Manohar M. Distribution of blood flow during moderate and strenuous exercise in ponies (Equus caballus). Am J Vet Res. 1983;44(10):1861-1866. 20. Guenette JA, Vogiatzis I, Zakynthinos S, et al. Human respiratory muscle blood flow measured by near-infrared spectroscopy and indocyanine green dye. J Appl Physiol. 2008;9788847056:169-181. doi:10.1007/978-88-470-5647-3_12 21. Fogarty MJ, Sieck GC. Evolution and functional differentiation of the diaphragm muscle of mammals. Compr Physiol. 2019;9(2):715. 22. Poole DC, Sexton WL, Farkas GA, Powers SK, Reid MB. Diaphragm structure and function in health and disease. Med Sci Sports Exerc. 1997;29(6):738-754. 23. Gliemann L, Hansen CV, Rytter N, Hellsten Y. Regulation of skeletal muscle blood flow during exercise. Curr Opin Physiol. 2019;10:146-155. 24. Bark H, Supinski GS, Lamanna JC, Kelsen SG. Relationship of changes in diaphragmatic muscle blood flow to muscle contractile activity. J Appl Physiol. 1987;62(1):291-299. 25. Sexton WL, Poole DC. Effects of emphysema on diaphragm blood flow during exercise. J Appl Physiol. 1998;84(3):971-979. 26. Jansky L, Hart JS. Cardiac output and organ blood flow in warm-and cold-acclimated rats exposed to cold. Can J Physiol Pharmacol. 1968;46(4):653-659. 27. Laughlin MH, Armstrong RB. Muscular blood flow distribution patterns as a function of running speed in rats. Am J Physiol Circ Physiol. 1982;243(2):H296-H306. 28. Boczkowski J, Vicaut E, Aubier M. In vivo effects of Escherichia coli endotoxemia on diaphragmatic microcirculation in rats. J Appl Physiol. 1992;72(6):2219-2224. 29. Leon A, Boczkowski J, Dureuil B, Vicaut E, Aubier M, Desmonts J-M. Diaphragmatic microcirculation during halothane and isoflurane exposure in pentobarbital-anesthetized rats. J Appl Physiol. 1992;73(4):1614-1618. 30. Kuwahira I, Gonzalez NC, Heisler N, Piiper J. Changes in regional blood flow distribution and oxygen supply during hypoxia in conscious rats. J Appl Physiol. 1993;74(1):211-214. 31. Musch TI. Elevated diaphragmatic blood flow during submaximal exercise in rats with chronic heart failure. Am J Physiol Circ Physiol. 1993;265(5):H1721-H1726. 32. Boczkowski J, Vicaut E, Danialou G, Aubier M. Role of nitric oxide and prostaglandins in the regulation of diaphragmatic arteriolar tone in the rat. J Appl Physiol. 1994;77(2):590-596. 33. Martin CM, Sibbald WJ. Modulation of hemodynamics and organ blood flow by nitric 97 oxide synthase inhibition is not altered in normotensive, septic rats. Am J Respir Crit Care Med. 1994;150(6):1539-1544. 34. Chang HY, Chen CR, Hussain SN. Diaphragmatic microcirculation measured by laser-Doppler flowmetry in the rat. J Appl Physiol. 1995;78(4):1225-1233. 35. Sexton WL, Poole DC. Costal diaphragm blood flow heterogeneity at rest and during exercise. Respir Physiol. 1995;101(2):171-182. 36. Mulder MF, van Lambalgen AA, van den Bos GC. The fall of cardiac output in endotoxemic rats cannot explain all changes in organ blood flow: a comparison between endotoxin and low venous return shock. Shock. 1996;5(2):135-140. 37. Chang H, Chen C, Hsiue T. Comparative effects of l‐NOARG and l‐NAME on basal blood flow and ACh‐induced vasodilatation in rat diaphragmatic microcirculation. Br J Pharmacol. 1997;120(2):326-332. 38. Armstrong RB, Laughlin MH. Blood flows within and among rat muscles as a function of time during high speed treadmill exercise. J Physiol. 1983;344(1):189-208. 39. Chen CW, Lee CH, Hsiue TR, Chang HY. Vasomotion in rat diaphragm microcirculation at rest and during stepwise arterial pressure reduction. Acta Physiol Scand. 1997;161(3):281-288. 40. Chang HY. Role of nitric oxide in vasodilator response induced by salbutamol in rat diaphragmatic microcirculation. Am J Physiol Circ Physiol. 1997;272(5):H2173-H2179. 41. Chang HY, Chen CW, Hsiue TR, Chen CR. Role of KATP channels on modulating diaphragmatic microvascular flow during hemorrhagic hypotension. Am J Physiol Circ Physiol. 1997;272(1):H272-H278. 42. Chang H. The involvement of ATP‐sensitive potassium channels in β2‐adrenoceptor agonist‐induced vasodilatation on rat diaphragmatic microcirculation. Br J Pharmacol. 1997;121(5):1024-1030. 43. Danialou G, Vicaut E, Aubier M, Boczkowski J. Theophylline dilates rat diaphragm arterioles via the prostaglandins pathway. Br J Pharmacol. 1998;124(7):1355. 44. Danialou G, Vicaut E, Sambe A, Aubier M, Boczkowski J. Predominant role of A1 adenosine receptors in mediating adenosine induced vasodilatation of rat diaphragmatic arterioles: involvement of nitric oxide and the ATP‐dependent K+ channels. Br J Pharmacol. 1997;121(7):1355-1363. 45. Chen C-W, Chang H-Y, Hsiue T-R. Mechanism of adenosine-induced vasodilation in rat diaphragm microcirculation. Am J Physiol Circ Physiol. 2000;279(5):H2210-H2217. 46. Aaker A, Laughlin MH. Differential adenosine sensitivity of diaphragm and skeletal muscle arterioles. J Appl Physiol. 2002;93(3):848-856. 47. Aaker A, Laughlin MH. Diaphragm arterioles are less responsive to α1-adrenergic constriction than gastrocnemius arterioles. J Appl Physiol. 2002;92(5):1808-1816. 48. Smith-Blair NJ, Pierce JD, Clancy RL. The effect of dobutamine infusion on fractional diaphragm thickening and diaphragm blood flow during fatigue. Hear lung. 98 2003;32(2):111-120. 49. Laughlin MH, Armstrong RB. Rat muscle blood flows as a function of time during prolonged slow treadmill exercise. Am J Physiol Circ Physiol. 1983;244(6):H814-H824. 50. Davis 3rd RT, Bruells CS, Stabley JN, McCullough DJ, Powers SK, Behnke BJ. Mechanical ventilation reduces rat diaphragm blood flow and impairs O2 delivery and uptake. Crit Care Med. 2012;40(10):2858. 51. Smith JR, Hageman KS, Harms CA, Poole DC, Musch TI. Effect of chronic heart failure in older rats on respiratory muscle and hindlimb blood flow during submaximal exercise. Respir Physiol Neurobiol. 2017;243:20-26. 52. Smith JR, Hageman KS, Harms CA, Poole DC, Musch TI. Respiratory muscle blood flow during exercise: Effects of sex and ovarian cycle. J Appl Physiol. 2017;122(4):918-924. 53. Smith JR, Ferguson SK, Hageman KS, Harms CA, Poole DC, Musch TI. Dietary nitrate supplementation opposes the elevated diaphragm blood flow in chronic heart failure during submaximal exercise. Respir Physiol Neurobiol. 2018;247:140-145. 54. Butenas ALE, Smith JR, Copp SW, Hageman KS, Poole DC, Musch TI. Type II diabetes accentuates diaphragm blood flow increases during submaximal exercise in the rat. Respir Physiol Neurobiol. 2020;281:103518. 55. Horn AG, Baumfalk DR, Schulze KM, et al. Effects of elevated positive end-expiratory pressure on diaphragmatic blood flow and vascular resistance during mechanical ventilation. J Appl Physiol. 2020;129(3):626-635. 56. Smith JR, Hirai DM, Copp SW, et al. Exercise training decreases intercostal and transversus abdominis muscle blood flows in heart failure rats during submaximal exercise. Respir Physiol Neurobiol. 2021;292:103710. 57. Horn AG, Kunkel ON, Schulze KM, et al. Supplemental oxygen administration during mechanical ventilation reduces diaphragm blood flow and oxygen delivery. J Appl Physiol. 2022. 58. Butenas ALE, Copp SW, Hageman KS, Poole DC, Musch TI. Effects of comorbid type II diabetes mellitus and heart failure on rat hindlimb and respiratory muscle blood flow during treadmill exercise. J Appl Physiol. 2023;134(4):846-857. 59. Armstrong RB, Laughlin MH. Exercise blood flow patterns within and among rat muscles after training. Am J Physiol Circ Physiol. 1984;246(1):H59-H68. 60. Armstrong RB, Laughlin MH. Rat muscle blood flows during high-speed locomotion. J Appl Physiol. 1985;59(4):1322-1328. 61. Rothwell NJ, Stock MJ, Sudera DK. Changes in tissue blood flow and beta-receptor density of skeletal muscle in rats treated with the beta2-adrenoceptor agonist clenbuterol. Br J Pharmacol. 1987;90(3):601. 62. Klabunde RE, Laughlin MH, Armstrong RB. Systemic adenosine deaminase administration does not reduce active hyperemia in running rats. J Appl Physiol. 1988;64(1):108-114. 99 63. Boczkowski J, Vicaut E, Aubier M. A preparation for in vivo study of the diaphragmatic microcirculation in the rat. Microvasc Res. 1990;40(1):157-167. 64. Marshall JM, Metcalfe JD. Effects of systemic hypoxia on the distribution of cardiac output in the rat. J Physiol. 1990;426(1):335-353. 65. Mognoni P, Saibene F, Santambrogio G, Camporesi E. Perfusion of inspiratory muscles at different levels of ventilation in rabbits. Respir Physiol. 1974;20(2):171-179. 66. AXÉN C, JANSON PERO. Diaphragmatic blood flow at various levels of ventilation in the rabbit. Acta Physiol Scand. 1979;106(1):1-3. 67. DeHaan SJ, Kendrick JE. Neural and metabolic control of blood flow to respiratory muscles of rabbits. Proc Soc Exp Biol Med. 1981;167(4):485-492. 68. Kendrick JE, De Haan SJ, Parke JD. Regulation of blood flow to respiratory muscles during hypoxia and hypercapnia. Proc Soc Exp Biol Med. 1981;166(2):157-161. 69. Carroll JF, Huang M, Hester RL, Cockrell H K, Mizelle L. Hemodynamic alterations in hypertensive obese rabbits. Hypertension. 1995;26(3):465-470. 70. Lublin A, Wolfenson D, Berman A. Sex differences in blood flow distribution of normothermic and heat-stressed rabbits. Am J Physiol Integr Comp Physiol. 1995;268(1):R66-R71. 71. Uchiyama A, Fujino Y, Hosotsubo K, Miyoshi E, Mashimo T, Nishimura M. Regional blood flow in respiratory muscles during partial ventilatory assistance in rabbits. Anesth Analg. 2006;102(4):1201-1206. 72. Reis DJ, Moorhead D, Wooten GF. Differential regulation of blood flow to red and white muscle in sleep and defense behavior. Am J Physiol Content. 1969;217(2):541-546. 73. Adachi H, Strauss W, Ochi H, Wagner Jr HN. The effect of hypoxia on the regional distribution of cardiac output in the dog. Circ Res. 1976;39(3):314-319. 74. Pannier JL, Leusen I. Regional blood flow in response to exercise in conscious dogs. Eur J Appl Physiol Occup Physiol. 1977;36(4):255-265. 75. Sanders M, White F, Bloor C. Cardiovascular responses of dogs and pigs exposed to similar physiological stress. Comp Biochem Physiol Part A Physiol. 1977;58(4):365-370. 76. Bellemare F, Wight D, Lavigne CM, Grassino A. Effect of tension and timing of contraction on the blood flow of the diaphragm. J Appl Physiol. 1983;54(6):1597-1606. 77. Reid MB, Johnson Jr RL. Efficiency, maximal blood flow, and aerobic work capacity of canine diaphragm. J Appl Physiol. 1983;54(3):763-772. 78. Viires N, Sillye G, Aubier M, Rassidakis A, Roussos C. Regional blood flow distribution in dog during induced hypotension and low cardiac output. Spontaneous breathing versus artificial ventilation. J Clin Invest. 1983;72(3):935-947. 79. Buchler B, Magder S, Roussos C. Effects of contraction frequency and duty cycle on diaphragmatic blood flow. J Appl Physiol. 1985;58(1):265-273. 80. Buchler B, Magder S, Katsardis H, Jammes Y, Roussos C. Effects of pleural pressure and 100 abdominal pressure on diaphragmatic blood flow. J Appl Physiol. 1985;58(3):691-697. 81. Hussain SN, Roussos C. Distribution of respiratory muscle and organ blood flow during endotoxic shock in dogs. J Appl Physiol. 1985;59(6):1802-1808. 82. Lockhat D, Magder S, Roussos C. Collateral sources of costal and crural diaphragmatic blood flow. J Appl Physiol. 1985;59(4):1164-1170. 83. Magder S, Lockhat D, Luo BJ, Roussos C. Respiratory muscle and organ blood flow with inspiratory elastic loading and shock. J Appl Physiol. 1985;58(4):1148-1156. 84. Bark H, Scharf SM. Diaphragmatic blood flow in the dog. J Appl Physiol. 1986;60(2):554-561. 85. Hussain SN, Graham R, Rutledge F, Roussos CH. Respiratory muscle energetics during endotoxic shock in dogs. J Appl Physiol. 1986;60(2):486-493. 86. Magder S. Pressure-flow relations of diaphragm and vital organs with nitroprusside-induced vasodilatation. J Appl Physiol. 1986;61(2):409-416. 87. Magder S, Erian R, Roussos C. Respiratory muscle blood flow in oleic acid-induced pulmonary edema. J Appl Physiol. 1986;60(6):1849-1856. 88. Scharf SM, Bark H, Einhorn S, Tarasiuk A. Blood flow to the canine diaphragm during hemorrhagic shock. Am Rev Respir Dis. 1986;133(2):205-211. 89. Supinski GS, Bark H, Guanciale A, Kelsen SG. Effect of alterations in muscle fiber length on diaphragm blood flow. J Appl Physiol. 1986;60(5):1789-1796. 90. Hussain SN, Rutledge F, Graham R, Magder S, Roussos C. Effects of norepinephrine and fluid administration on diaphragmatic O2 consumption in septic shock. J Appl Physiol. 1987;62(4):1368-1376. 91. Musch TI, Haidet GC, Ordway GA, Longhurst JC, Mitchell JH. Training effects on regional blood flow response to maximal exercise in foxhounds. J Appl Physiol. 1987;62(4):1724-1732. 92. Bundy RJ, Arnold JS, DiMarco AF, Hussein F, Supinski GS. Effect of amrinone on diaphragm blood flow. J Appl Physiol. 1988;65(4):1506-1513. 93. Hussain SN, Roussos C, Magder S. Autoregulation of diaphragmatic blood flow in dogs. J Appl Physiol. 1988;64(1):329-336. 94. Hussain SNA, Rutledue F, Roussos C, Magder S. Effects of norepinephrine and fluid administration on the selective blood flow distribution in endotoxic shock. J Crit Care. 1988;3(1):32-42. 95. Johnson Jr RL, Reid MB. Effects of metabolic blockade on distribution of blood flow to respiratory muscles. J Appl Physiol. 1988;64(1):174-180. 96. Nichols DG, Scharf SM, Traystman RJ, Robotham JL. Correlation of left phrenic arterial flow with regional diaphragmatic blood flow. J Appl Physiol. 1988;64(5):2230-2235. 97. Rutledge FS, Hussain SN, Roussos C, Magder S. Diaphragmatic energetics and blood flow during pulmonary edema and hypotension. J Appl Physiol. 1988;64(5):1908-1915. 101 98. Supinski G, DiMarco A, KETA L, Hussein F, Altose M. Reversibility of Diaphragm Fatigue by Mechanical Hyperperfusion1-3. Am Rev Respir Dis. 1988;138:604-609. 99. Pope A, Scharf SM, Brown R. Diaphragm metabolism during supramaximal phrenic nerve stimulation. J Appl Physiol. 1989;66(2):567-572. 100. Hussain SN, Roussos C, Magder S. Effects of tension, duty cycle, and arterial pressure on diaphragmatic blood flow in dogs. J Appl Physiol. 1989;66(2):968-976. 101. Rosen CL, Cote A, Haddad GG. Effect of enkephalins on cardiac output and regional blood flow in conscious dogs. Am J Physiol Circ Physiol. 1989;256(6):H1651-H1658. 102. Hu F, Comtois A, Shadram E, Grassino A. Effect of separate hemidiaphragm contraction on left phrenic artery flow and O2 consumption. J Appl Physiol. 1990;69(1):86-90. 103. Hu F, Comtois A, Grassino AE. Contraction-dependent modulations in regional diaphragmatic blood flow. J Appl Physiol. 1990;68(5):2019-2028. 104. Hussain S, Chatillon A, Magder S, Roussos C. The respiratory and vascular responses evoked by chemical activation of small fiber phrenic nerve afferents in dogs. Chest. 1990;97(3):44S-45S. 105. Supinski GS, DiMarco AF, Gonzalez J, Altose MD. Effect of norepinephrine on diaphragm contractility and blood flow. J Appl Physiol. 1990;69(6):2019-2028. 106. Supinski GS, DiMarco AF, Altose MD. Effect of diaphragmatic contraction on intramuscular pressure and vascular impedance. J Appl Physiol. 1990;68(4):1486-1493. 107. Brancatisano A, Amis TC, Tully A, Kelly WT, Engel LA. Regional distribution of blood flow within the diaphragm. J Appl Physiol. 1991;71(2):583-589. 108. Hussain SNA, Ward ME, Gatensby AG, Roussos C, Deschamps A. Respiratory muscle activation by limb muscle afferent stimulation in anesthetized dogs. Respir Physiol. 1991;84(2):185-198. 109. Hussain SN, Magder S. Diaphragmatic intramuscular pressure in relation to tension, shortening, and blood flow. J Appl Physiol. 1991;71(1):159-167. 110. Haberer JP, Audibert G, Saunier CG, Müller C, Laxenaire MC, Hartemann D. Effect of propofol and thiopentone on regional blood flow in brain and peripheral tissues during normoxia and hypoxia in the dog. Clin Physiol. 1993;13(2):197-207. 111. Hsia CC, Ramanathan M, Pean JL, Johnson Jr RL. Respiratory muscle blood flow in exercising dogs after pneumonectomy. J Appl Physiol. 1992;73(1):240-247. 112. Hu F, Comtois A, Grassino AE. Optimal diaphragmatic blood perfusion. J Appl Physiol. 1992;72(1):149-157. 113. Hussain SN, Stewart DJ, Ludemann JP, Magder S. Role of endothelium-derived relaxing factor in active hyperemia of the canine diaphragm. J Appl Physiol. 1992;72(6):2393-2401. 114. Ward ME, Magder SA, Hussain SNA. Oxygen Delivery-independent Effect of Blood Flow on Diaphragm Fatigue1-3. Am Rev Respir Dis. 1992;145:1058-1063. 102 115. Supinski GS, Stofan D, Nashawati E, DiMarco AF. Failure of vasodilator administration to increase blood flow to the fatiguing diaphragm. J Appl Physiol. 1993;74(3):1178-1185. 116. Ward ME, Magder SA, Hussain SN. Role of endothelium-derived relaxing factor in reactive hyperemia in canine diaphragm. J Appl Physiol. 1993;74(4):1606-1612. 117. Comtois A, Sinderby C, Comtois N, Grassino A, Renaud JM. An ATP-sensitive potassium channel blocker decreases diaphragmatic circulation in anesthetized dogs. J Appl Physiol. 1994;77(1):127-134. 118. Derom E, Janssens S, Vanhaecke J, De Bock V, Chen Y, Decramer M. Theophylline alters distribution of blood flow to respiratory muscles. Am J Respir Crit Care Med. 1994;150(4):941-946. 119. Kawagoe Y, Permutt S, Fessler HE. Hyperinflation with intrinsic PEEP and respiratory muscle blood flow. J Appl Physiol. 1994;77(5):2440-2448. 120. Shen W, Lundborg M, Wang J, et al. Role of EDRF in the regulation of regional blood flow and vascular resistance at rest and during exercise in conscious dogs. J Appl Physiol. 1994;77(1):165-172. 121. Vanelli G, Chang HY, Gatensby AG, Hussain SN. Contribution of potassium channels to active hyperemia of the canine diaphragm. J Appl Physiol. 1994;76(3):1098-1105. 122. Vanelli G, Hussain SN. Effects of potassium channel blockers on basal vascular tone and reactive hyperemia of canine diaphragm. Am J Physiol Circ Physiol. 1994;266(1):H43-H51. 123. Ward ME, Hussain SN. Diaphragmatic pressure-flow relationship during hemorrhagic shock: role of nitric oxide. J Appl Physiol. 1994;77(5):2244-2249. 124. Ward ME, Hussain SNA. Effect of inhibition of nitric oxide release on the diaphragmatic oxygen delivery-consumption relationship. J Crit Care. 1994;9(2):90-99. 125. Ward ME, Hussain SNA. Regulation of baseline vascular resistance in the canine diaphragm by nitric oxide. Br J Pharmacol. 1994;112(1):65-70. 126. Hussain SN. Role of nitric oxide in endotoxin-induced metabolic and vascular dysregulation of the canine diaphragm. Am J Respir Crit Care Med. 1995;152(2):683-689. 127. Janssens S, Derom E, Vanhaecke J, Decramer M. Theophylline increases oxygen consumption during inspiratory resistive loading. Am J Respir Crit Care Med. 1995;151(4):1000-1005. 128. Ide T, Kochi T, Iijima K, Mizuguchi T. Distribution of diaphragm blood flow during sevoflurane anaesthesia in dogs. Can J Anaesth. 1996;43(1):44-49. 129. Ward ME. Interaction between hypoxia and hypercapnia in regulating canine diaphragm arteriolar diameter. J Appl Physiol. 1996;80(3):802-809. 130. Ward ME, Hussain SNA. Effect of α-adrenoreceptor stimulation on the diaphragmatic oxygen delivery-consumption relationship. J Crit Care. 1996;11(1):19-26. 131. Ward ME. Effect of inhibition of nitric oxide synthesis on the diaphragmatic 103 microvascular response to hypoxia. J Appl Physiol. 1996;81(4):1633-1641. 132. Hsia CCW, Takeda S-I, Wu EY, Glenny RW, Johnson Jr RL. Adaptation of respiratory muscle perfusion during exercise to chronically elevated ventilatory work. J Appl Physiol. 2000;89(5):1725-1736. 133. Johnson Jr RL, Hsia CCW, Takeda S-I, Wait JL, Glenny RW. Efficient design of the diaphragm: distribution of blood flow relative to mechanical advantage. J Appl Physiol. 2002;93(3):925-930. 134. Bark H, Supinski G, Bundy R, Kelsen S. Effect of Hypoxia on Diaphragm Blood Flow, Oxygen Uptake, and Contractility1-3. Am Rev Respir Dis. 1988;138:1535-1541. 135. Musch TI, Friedman DB, Pitetti KH, et al. Regional distribution of blood flow of dogs during graded dynamic exercise. J Appl Physiol. 1987;63(6):2269-2277. 136. Hales JRS, Dampney RAL. The redistribution of cardiac output in the dog during heat stress. J Therm Biol. 1975;1(1):29-34. 137. Fixler DE, Atkins JM, Mitchell JH, Horwitz LD. Blood flow to respiratory, cardiac, and limb muscles in dogs during graded exercise. Am J Physiol Content. 1976;231(5):1515-1519. 138. Forsyth RP, Hoffbrand BI, MELMON KL. Redistribution of cardiac output during hemorrhage in the unanesthetized monkey. Circ Res. 1970;27(3):311-320. 139. Hales JRS. Radioactive microsphere measurement of cardiac output and regional tissue blood flow in the sheep. Pflügers Arch. 1973;344(2):119-132. 140. Molteni RA, Melmed MH, Sheldon RE, Jones MD, Meschia G. Induction of fetal breathing by metabolic acidemia and its effect on blood flow to the respiratory muscles. Am J Obstet Gynecol. 1980;136(5):609-620. 141. Matalon S, Nesarajah MS, Farhi LE. Pulmonary and circulatory changes in conscious sheep exposed to 100% O2 at 1 ATA. J Appl Physiol. 1982;53(1):110-116. 142. Bell AW, Hales JR, King RB, Fawcett AA. Influence of heat stress on exercise-induced changes in regional blood flow in sheep. J Appl Physiol. 1983;55(6):1916-1923. 143. Bell AW, Hales JR, Fawcett AA, King RB. Effects of exercise and heat stress on regional blood flow in pregnant sheep. J Appl Physiol. 1986;60(5):1759-1764. 144. Davidson D, Stalcup SA, Mellins RB. Systemic hemodynamics affecting cardiac output during hypocapnic and hypercapnic hypoxia. J Appl Physiol. 1986;60(4):1230-1236. 145. Alpan G, Mauray F, Clyman RI. The effects of the patent ductus arteriosus on diaphragmatic blood flow and function. Pediatr Res. 1990;28(5):437-445. 146. Pang LM, Kim YJ, Bazzy AR. Blood flow to respiratory muscles and major organs during inspiratory flow resistive loads. J Appl Physiol. 1993;74(1):428-434. 147. Hales JRS, Bennett JW, Fawcett AA. Effects of acute cold exposure on the distribution of cardiac output in the sheep. Pflügers Arch. 1976;366(2-3):153-157. 148. Soust M, Walker AM, Wilson FE, Berger PJ. Origins and regional distribution of blood 104 flow to the respiratory muscles in conscious sheep. Respir Physiol. 1987;67(3):283-294. 149. Nichols DG, Howell S, Massik J, et al. Relationship of diaphragmatic contractility to diaphragmatic blood flow in newborn lambs. J Appl Physiol. 1989;66(1):120-127. 150. Soust M, Walker AM, Berger PJ. Blood flow to the respiratory muscles during hypercapnic hyperpnoea in the newborn lamb. Respir Physiol. 1989;76(1):93-105. 151. Berger PJ, Soust M, Smolich JJ, Walker AM. Respiratory muscle blood flow in the fetal lamb during apnoea and breathing. Respir Physiol. 1994;97(1):111-121. 152. Laughlin MH, Klabunde RE, Delp MD, Armstrong RB. Effects of dipyridamole on muscle blood flow in exercising miniature swine. Am J Physiol Circ Physiol. 1989;257(5):H1507-H1515. 153. Mayock DE, Standaert TA, Woodrum DE. Effect of inspiratory resistive loaded breathing and hypoxemia on diaphragmatic function in the piglet. J Appl Physiol. 1992;73(5):1888-1893. 154. Yavuz Y, Rønning K, Lyng O, Grønbech JE, Mårvik R. Effect of carbon dioxide pneumoperitoneum on tissue blood flow in the peritoneum, rectus abdominis, and diaphragm muscles. Surg Endosc Other Interv Tech. 2003;17(10):1632-1635. 155. Hering R, Zinserling J, Wrigge H, et al. Effects of spontaneous breathing during airway pressure release ventilation on respiratory work and muscle blood flow in experimental lung injury. Chest. 2005;128(4):2991-2998. 156. Mayock DE, Standaert TA, Murphy TD, Woodrum DE. Diaphragmatic force and substrate response to resistive loaded breathing in the piglet. J Appl Physiol. 1991;70(1):70-76. 157. McCONAGHY FF, Hodgson DR, Rose RJ, Hales JRS. Redistribution of cardiac output in response to heat exposure in the pony. Equine Vet J. 1996;28(S22):42-46. 158. Aubier M, Murciano D, Menu Y, Boczkowski J, Mal H, Pariente R. Dopamine effects on diaphragmatic strength during acute respiratory failure in chronic obstructive pulmonary disease. Ann Intern Med. 1989;110(1):17-23. 159. Louvaris Z, Rodrigues A, Dacha S, et al. High-intensity exercise impairs extradiaphragmatic respiratory muscle perfusion in patients with COPD. J Appl Physiol. 2021;130(2):325-341. 160. De Troyer A, Boriek AM. Mechanics of the respiratory muscles. Compr Physiol. 2011;1(3):1273-1300. 161. Society AT. ATS/ERS Statement on respiratory muscle testing. Am J Respir Crit Care Med. 2002;166:518-624. 162. Dominelli PB, Wiggins CC, Roy TK, Secomb TW, Curry TB, Joyner MJ. The Oxygen Cascade During Exercise in Health and Disease. In: Mayo Clinic Proceedings. Vol 96. Elsevier; 2021:1017-1032. 163. Magder S, Malhotra A, Hibbert KA, Hardin CC. Cardiopulmonary Monitoring: Basic Physiology, Tools, and Bedside Management for the Critically Ill. Springer Nature; 2021. 105 164. Dominelli PB, Render JN, Molgat‐Seon Y, Foster GE, Romer LM, Sheel AW. Oxygen cost of exercise hyperpnoea is greater in women compared with men. J Physiol. 2015;593(8):1965-1979. 165. Poole DC, Sexton WL, Behnke BJ, Ferguson CS, Hageman KS, Musch TI. Respiratory muscle blood flows during physiological and chemical hyperpnea in the rat. J Appl Physiol. 2000;88(1):186-194. 166. Knill R, Andrews W, Bryan AC, Bryan MH. Respiratory load compensation in infants. J Appl Physiol. 1976;40(3):357-361. 167. Bureau MA, Begin R. Postnatal maturation of the respiratory response to O2 in awake newborn lambs. J Appl Physiol. 1982;52(2):428-433. 168. Wait JL, Staworn D, Poole DC. Diaphragm thickness heterogeneity at functional residual capacity and total lung capacity. J Appl Physiol. 1995;78(3):1030-1036. 169. Poole DC, Pittman RN, Musch TI, Østergaard L. August Krogh’s theory of muscle microvascular control and oxygen delivery: a paradigm shift based on new data. J Physiol. 2020;598(20):4473-4507. 170. Buckberg GD, Luck JC, Payne DB, Hoffman JI, Archie JP, Fixler DE. Some sources of error in measuring regional blood flow with radioactive microspheres. J Appl Physiol. 1971;31(4):598-604. 171. Kaihara S, Van Heerden PD, Migita T, Wagner Jr HN. Measure-ment of distribution of cardiac output. J Appl Physiol. 1968;25(6):696-700. 172. Hales JRS. Effects of exposure to hot environments on the regional distribution of blood flow and on cardiorespiratory function in sheep. Pflügers Arch. 1973;344(2):133-148. 173. Hales JRS. Determination of the effect of climatic stress on regional brain and spinal cord blood flow, using radioactive microspheres. 1971. 174. Utley J, Carlson EL, Hoffman JIE, Martinez HM, BUCKBERG GD. Total and regional myocardial blood flow measurements with 25µ, 15µ, 9µ, and filtered 1-10µ diameter microspheres and antipyrine in dogs and sheep. Circ Res. 1974;34(3):391-405. 175. Comtois A, Gorczyca W, Grassino A. Anatomy of diaphragmatic circulation. J Appl Physiol. 1987;62(1):238-244. 176. Ward ME. Dilation of rat diaphragmatic arterioles by flow and hypoxia: roles of nitric oxide and prostaglandins. J Appl Physiol. 1999;86(5):1644-1650. 177. Fox IJ. A tricarbocyanine dye for continuous recording of dilution curves in whole blood independent of variations in blood oxygen saturation. Proc Mayo Clim. 1957;32:478-484. 178. Benya R, Quintana J, Brundage B. Adverse reactions to indocyanine green: a case report and a review of the literature. Cathet Cardiovasc Diagn. 1989;17(4):231-233. 179. Landsman ML, Kwant G, Mook GA, Zijlstra WG. Light-absorbing properties, stability, and spectral stabilization of indocyanine green. J Appl Physiol. 1976;40(4):575-583. 180. Boushel R, Langberg H, Olesen J, et al. Regional blood flow during exercise in humans 106 measured by near-infrared spectroscopy and indocyanine green. J Appl Physiol. 2000;89(5):1868-1878. 181. Guenette JA, Vogiatzis I, Zakynthinos S, et al. Human respiratory muscle blood flow measured by near-infrared spectroscopy and indocyanine green. J Appl Physiol. 2008;104(4):1202-1210. 182. Vogiatzis I, Athanasopoulos D, Boushel R, et al. Contribution of respiratory muscle blood flow to exercise‐induced diaphragmatic fatigue in trained cyclists. J Physiol. 2008;586(22):5575-5587. 183. Vogiatzis I, Athanasopoulos D, Habazettl H, et al. Intercostal muscle blood flow limitation in athletes during maximal exercise. J Physiol. 2009;587(14):3665-3677. 184. Henderson WR, Guenette JA, Dominelli PB, et al. Limitations of respiratory muscle and vastus lateralis blood flow during continuous exercise. Respir Physiol Neurobiol. 2012;181(3):302-307. 185. Ramsook AH, Peters CM, Leahy MG, et al. Near‐infrared spectroscopy measures of sternocleidomastoid blood flow during exercise and hyperpnoea. Exp Physiol. 2020;105(12):2226-2237. 186. Habazettl H, Athanasopoulos D, Kuebler WM, et al. Near-infrared spectroscopy and indocyanine green derived blood flow index for noninvasive measurement of muscle perfusion during exercise. J Appl Physiol. 2010;108(4):962-967. 187. Athanasopoulos D, Louvaris Z, Cherouveim E, et al. Expiratory muscle loading increases intercostal muscle blood flow during leg exercise in healthy humans. J Appl Physiol. 2010;109(2):388-395. 188. Guenette JA, Henderson WR, Dominelli PB, et al. Blood flow index using near-infrared spectroscopy and indocyanine green as a minimally invasive tool to assess respiratory muscle blood flow in humans. Am J Physiol Integr Comp Physiol. 2011;300(4):R984-R992. 189. Louvaris Z, Vogiatzis I, Aliverti A, et al. Blood flow does not redistribute from respiratory to leg muscles during exercise breathing heliox or oxygen in COPD. J Appl Physiol. 2014;117(3):267-276. 190. Vogiatzis I, Habazettl H, Aliverti A, et al. Effect of helium breathing on intercostal and quadriceps muscle blood flow during exercise in COPD patients. Am J Physiol Integr Comp Physiol. 2011;300(6):R1549-R1559. 191. Vogiatzis I, Athanasopoulos D, Habazettl H, et al. Intercostal muscle blood flow limitation during exercise in chronic obstructive pulmonary disease. Am J Respir Crit Care Med. 2010;182(9):1105-1113. 192. Louvaris Z, Habazettl H, Wagner H, Zakynthinos S, Wagner P, Vogiatzis I. Near-infrared spectroscopy using indocyanine green dye for minimally invasive measurement of respiratory and leg muscle blood flow in patients with COPD. J Appl Physiol. 2018;125(3):947-959. 107 193. Sheel AW, Boushel R, Dempsey JA. Competition for blood flow distribution between respiratory and locomotor muscles: implications for muscle fatigue. J Appl Physiol. 2018;125(3):820-831. doi:10.1152/japplphysiol.00189.2018 194. Shahgholi L, Baria MR, Sorenson EJ, et al. Diaphragm depth in normal subjects. Muscle Nerve. 2014;49(5):666-668. 195. Lindner JR. Contrast echocardiography: current status and future directions. Heart. 2021;107(1):18-24. 196. El Kaffas A, Sigrist RMS, Fisher G, et al. Quantitative three-dimensional dynamic contrast-enhanced ultrasound imaging: first-in-human pilot study in patients with liver metastases. Theranostics. 2017;7(15):3745. 197. D’Onofrio M, Crosara S, De Robertis R, Canestrini S, Mucelli RP. Contrast-enhanced ultrasound of focal liver lesions. Am J Roentgenol. 2015;205(1):W56-W66. 198. Thomas KN, Cotter JD, Lucas SJE, Hill BG, van Rij AM. Reliability of contrast-enhanced ultrasound for the assessment of muscle perfusion in health and peripheral arterial disease. Ultrasound Med Biol. 2015;41(1):26-34. 199. Kunz P, Kiesl S, Groß S, Kauczor H-U, Schmidmaier G, Fischer C. Intra-observer and device-dependent inter-observer reliability of contrast-enhanced ultrasound for muscle perfusion quantification. Ultrasound Med Biol. 2020;46(2):275-285. 200. Fischer C, Krix M, Weber M-A, et al. Contrast-enhanced ultrasound for musculoskeletal applications: A World Federation for Ultrasound in Medicine and Biology position paper. Ultrasound Med Biol. 2020;46(6):1279-1295. 201. Nguyen T, Davidson BP. Contrast enhanced ultrasound perfusion imaging in skeletal muscle. J Cardiovasc Imaging. 2019;27(3):163-177. 202. Lindner JR, Song J, Jayaweera AR, Sklenar J, Kaul S. Microvascular rheology of Definity microbubbles after intra-arterial and intravenous administration. J Am Soc Echocardiogr. 2002;15(5):396-403. 203. Krix M, Krakowski-roosen H, Armarteifio E, et al. Comparison of transient arterial occlusion and muscle exercise provocation for assessment of perfusion reserve in skeletal muscle with real-time contrast-enhanced ultrasound. Eur J Radiol. 2011;78:419-424. doi:10.1016/j.ejrad.2009.11.014 204. Krix M, Weber M-A, Krakowski-Roose H, et al. Assessment of Skeletal Muscle Perfusion Using Contrast-Enhanced Ultrasonography. 2005:431-441. 205. Wei K, Le E, Bin J-P, Coggins M, Thorpe J, Kaul S. Quantification of renal blood flow with contrast-enhanced ultrasound. J Am Coll Cardiol. 2001;37(4):1135-1140. 206. Wei K, Jayaweera AR, Firoozan S, Linka A, Skyba DM, Kaul S. Quantification of myocardial blood flow with ultrasound-induced destruction of microbubbles administered as a constant venous infusion. Circulation. 1998;97(5):473-483. 207. Rim S-J, Leong-Poi H, Lindner JR, et al. Quantification of cerebral perfusion with “real-time” contrast-enhanced ultrasound. Circulation. 2001;104(21):2582-2587. 108 208. Meneses AL, Nam MCY, Bailey TG, et al. Skeletal muscle microvascular perfusion responses to cuff occlusion and submaximal exercise assessed by contrast‐enhanced ultrasound: The effect of age. Physiol Rep. 2020;8(19):e14580. 209. Jones EJ, Atherton PJ, Piasecki M, Phillips BE. Contrast‐enhanced ultrasound repeatability for the measurement of skeletal muscle microvascular blood flow. Exp Physiol. 2023. 210. Goligher EC, Laghi F, Detsky ME, et al. Measuring diaphragm thickness with ultrasound in mechanically ventilated patients: feasibility, reproducibility and validity. Intensive Care Med. 2015;41(4):642-649. 211. Ng KWP, Dietz AR, Johnson R, Shoykhet M, Zaidman CM. Reliability of bedside ultrasound of limb and diaphragm muscle thickness in critically ill children. Muscle Nerve. 2019;59(1):88-94. 212. Boon AJ, Harper CJ, Ghahfarokhi LS, Strommen JA, Watson JC, Sorenson EJ. Two‐dimensional ultrasound imaging of the diaphragm: quantitative values in normal subjects. Muscle Nerve. 2013;47(6):884-889. 213. Boussuges A, Rives S, Finance J, et al. Ultrasound Assessment of Diaphragm Thickness and Thickening: Reference Values and Limits of Normality When in a Seated Position. Front Med. 2021:1982. 214. MacIntyre N, Crapo RO, Viegi G, et al. Standardisation of the single-breath determination of carbon monoxide uptake in the lung. Eur Respir J. 2005;26(4):720-735. doi:10.1183/09031936.05.00034905 215. Stanojevic S, Graham BL, Cooper BG, et al. Official ERS technical standards: Global Lung Function Initiative reference values for the carbon monoxide transfer factor for Caucasians. Eur Respir J. 2017;50(3). 216. Quanjer PH, Stanojevic S, Cole TJ, et al. Multi-ethnic reference values for spirometry for the 3–95-yr age range: the global lung function 2012 equations. 2012. 217. Yoshida R, Tomita K, Kawamura K, et al. Measurement of intercostal muscle thickness with ultrasound imaging during maximal breathing. J Phys Ther Sci. 2019;31(4):340-343. 218. doo Park S. Reliability of ultrasound imaging of the transversus deep abdominial, internal oblique and external oblique muscles of patients with low back pain performing the drawing-in maneuver. J Phys Ther Sci. 2013;25(7):845-847. 219. Boussuges A, Rives S, Finance J, Brégeon F. Assessment of diaphragmatic function by ultrasonography: current approach and perspectives. World J Clin cases. 2020;8(12):2408. 220. Young GM, Krastins D, Chang D, et al. Influence of cuff‐occlusion duration on contrast‐enhanced ultrasound assessments of calf muscle microvascular blood flow responsiveness in older adults. Exp Physiol. 2020;105(12):2238-2245. 221. Milic-Emili J, Mead J, Turner JM, Glauser EM. Improved technique for estimating pleural pressure from esophageal balloons. J Appl Physiol. 1964;19(2):207-211. 222. Churchward-Venne TA, Cotie LM, MacDonald MJ, et al. Citrulline does not enhance 109 blood flow, microvascular circulation, or myofibrillar protein synthesis in elderly men at rest or following exercise. Am J Physiol Metab. 2014;307(1):E71-E83. doi:10.1152/ajpendo.00096.2014 223. Medellin A, Merrill C, Wilson SR. Role of contrast-enhanced ultrasound in evaluation of the bowel. Abdom Radiol. 2018;43(4):918-933. doi:10.1007/s00261-017-1399-6 224. Qureshi AI, Saleem MA, Ahrar A, Raja F. Imaging of the Vasa Nervorum Using Contrast-Enhanced Ultrasound. J Neuroimaging. 2017;27(6):583-588. doi:10.1111/jon.12429 225. Jablonowski L, Stanczak M, Machado P, Fitzgerald K, Reeves G, Forsberg F. Contrast-enhanced ultrasound evaluation of skeletal muscle perfusion in response to left ventricular assist device (LVAD) therapy. IEEE Int Ultrason Symp IUS. 2017:2-4. doi:10.1109/ULTSYM.2017.8092754 226. Dunford EC, Au JS, Devries MC, Phillips SM, MacDonald MJ. Cardiovascular aging and the microcirculation of skeletal muscle: using contrast-enhanced ultrasound. Am J Physiol Circ Physiol. 2018;315(5):H1194-H1199. 227. Lantheus Medical Imaging. DEFINITY® Diluted Bolus Administration. 228. Lindner JR, Womack L, Barrett EJ, et al. Limb Stress-Rest Perfusion Imaging With Contrast Ultrasound for the Assessment of Peripheral Arterial Disease Severity. JACC Cardiovasc Imaging. 2008;1(3):343-350. doi:10.1016/j.jcmg.2008.04.001 229. Davidson BP, Belcik JT, Mott BH, Landry G, Lindner JR. Quantification of residual limb skeletal muscle perfusion with contrast-enhanced ultrasound during application of a focal junctional tourniquet. J Vasc Surg. 2016;63(1):148-153. doi:10.1016/j.jvs.2014.06.107 230. Davidson BP, Belcik JT, Landry G, Linden J, Lindner JR. Exercise versus vasodilator stress limb perfusion imaging for the assessment of peripheral artery disease. Echocardiography. 2017;34(8):1187-1194. doi:10.1111/echo.13601 231. Dominelli PB, Sheel AW. Experimental approaches to the study of the mechanics of breathing during exercise. Respir Physiol Neurobiol. 2012;180(2-3):147-161. 232. Mulreany LT, Weiner DJ, McDonough JM, Panitch HB, Allen JL. Noninvasive measurement of the tension-time index in children with neuromuscular disease. J Appl Physiol. 2003;95(3):931-937. 233. Davidson BP, Hodovan J, O’Neil RM, et al. Limb perfusion during exercise assessed by contrast ultrasound varies according to symptom severity in patients with peripheral artery disease. J Am Soc Echocardiogr. 2019;32(9):1086-1094. 234. Bates D, Mächler M, Bolker B, Walker S. Fitting linear mixed-effects models using lme4. arXiv Prepr arXiv14065823. 2014. 235. Kuznetsova A, Brockhoff PB, Christensen RHB. lmerTest package: tests in linear mixed effects models. J Stat Softw. 2017;82:1-26. 236. Lenth R, Singmann H, Love J, Buerkner P, Herve M. Emmeans: Estimated marginal means. AKA least-squares means. 2018:1-2. 237. Kassambara A. rstatix: pipe-friendly framework for basic statistical tests. R package 110 version 0.7. 0. Comput software] https//CRAN R-proje ct org/packa ge= rstatix. 2021. 238. Cruickshank T, Flores-Opazo M, Tuesta M, Reyes Á. Reproducibility of maximum respiratory pressure assessment: a systematic review and meta-analysis. Chest. 2022;162(4):828-850. 239. Weir JP. Quantifying test-retest reliability using the intraclass correlation coefficient and the SEM. J Strength Cond Res. 2005;19(1):231-240. 240. Koo TK, Li MY. A guideline of selecting and reporting intraclass correlation coefficients for reliability research. J Chiropr Med. 2016;15(2):155-163. 241. Atkinson G. What is this thing called measurement error? In: Kinanthropometry VIII: Proceedings of the 8th International Conference of the International Society for the Advancement of Kinanthropometry (ISAK). Taylor & Francis; 2003:3-13. 242. Atkinson G, Nevill AM. Statistical methods for assessing measurement error (reliability) in variables relevant to sports medicine. Sport Med. 1998;26:217-238. 243. Harms CA, Wetter TJ, McClaran SR, et al. Effects of respiratory muscle work on cardiac output and its distribution during maximal exercise. J Appl Physiol. 1998;85(2):609-618. 244. Calbet JAL, Gonzalez-Alonso J, Helge JW, et al. Cardiac output and leg and arm blood flow during incremental exercise to exhaustion on the cycle ergometer. J Appl Physiol. 2007;103(3):969-978. 245. Joyner MJ, Casey DP. Regulation of increased blood flow (hyperemia) to muscles during exercise: a hierarchy of competing physiological needs. Physiol Rev. 2015. 246. Gauthier TP, Muhammad A, Wasan HS, Abel PD, Leen ELS. Reproducibility of Quantitative Assessment of Altered Hepatic Hemodynamics With Dynamic Contrast‐Enhanced Ultrasound. J Ultrasound Med. 2012;31(9):1413-1420. 247. Hopkins WG. Measures of reliability in sports medicine and science. Sport Med. 2000;30:1-15. 248. Sheriff DD. Role of mechanical factors in governing muscle blood flow. Acta Physiol (Oxf). 2010;199(4):385-391. 249. Dominelli PB, Molgat-Seon Y. Sex, gender and the pulmonary physiology of exercise. Eur Respir Rev. 2022;31(163). 250. Soldin OP, Mattison DR. Sex differences in pharmacokinetics and pharmacodynamics. Clin Pharmacokinet. 2009;48(3):143-157. 251. Ryu JC, Davidson BP, Xie A, et al. Molecular imaging of the paracrine proangiogenic effects of progenitor cell therapy in limb ischemia. Circulation. 2013;127(6):710-719. 252. Gauthier TP, Chebil M, Peronneau P, Lassau N. In vitro evaluation of the impact of ultrasound scanner settings and contrast bolus volume on time–intensity curves. Ultrasonics. 2012;52(1):12-19. 253. Davidson BP, Hodovan J, Belcik JT, et al. Rest-stress limb perfusion imaging in humans with contrast ultrasound using intermediate-power imaging and microbubbles resistant to 111 inertial cavitation. J Am Soc Echocardiogr. 2017;30(5):503-510. 254. Belcik JT, Mott BH, Xie A, et al. Augmentation of limb perfusion and reversal of tissue ischemia produced by ultrasound-mediated microbubble cavitation. Circ Cardiovasc Imaging. 2015;8(4):e002979. 255. Laporta D, Grassino A. Assessment of transdiaphragmatic pressure in humans. J Appl Physiol. 1985;58(5):1469-1476. 256. Dominelli PB, Archiza B, Ramsook AH, et al. Effects of respiratory muscle work on respiratory and locomotor blood flow during exercise. Exp Physiol. 2017;102(11):1535-1547. 257. Harms CA, Babcock MA, McClaran SR, et al. Respiratory muscle work compromises leg blood flow during maximal exercise. J Appl Physiol. 1997;82(5):1573-1583. 258. Horn AG, Davis 3rd RT, Baumfalk DR, et al. Impaired diaphragm resistance vessel vasodilation with prolonged mechanical ventilation. J Appl Physiol. 2019;127(2):423-431. 112 Appendices Appendix A Individual Responses Herein are individual participant responses for diaphragmatic blood flow and how it changes as a function of respiratory pressures. These data are meant to demonstrate the variability inherent with some individuals as well as the variability in respiratory pressures from day-to-day. 113 A.1 Q̇DIA vs. MIP 114 A.2 Q̇DIA vs. PDIMAX 115 A.3 Q̇DIA vs. PTPDIA (Summed) 116 A.4 Q̇DIA vs. PTPDIA (Averaged) 117 Appendix B Supplementary Sex Differences Data This appendix is meant to shown sex differences data although it was not found to be statistically significant for this study. The statistical model used to evaluate these data were varying slopes linear mixed effects models with fixed effects of trial and day as an interaction, sex as a fixed main effect, and participant identifier as a random effect. Figure 6.1. Destruction-replenishment curves for diaphragm and liver blood flows stratified by sex. 118 Figure 6.2. Linear mixed models for the relationship between Q̇DIA and respiratory pressures when compared by sex. Lines fit to individual participant data and group data for %MIP (A), %PDIMAX (B), PTPPDI average (C), and PTPPDI sum (D). Abbreviations: PTPDIA, pressure-time product of the diaphragm; AU, acoustic units; %PDIMAX, percent of maximum transdiaphragmatic pressure. 119 Table 6.1. Sex differences in CEUS blood flow variables as an average of both days. BL S1 S2 S3 P-Value Sex Diaphragm A - Volume, AU ♂ 15.0 ± 9.1 13.7 ± 4.9 15.0 ± 6.5 15.5 ± 4.5 0.13 ♀ 17.6 ± 6.1 18.0 ± 7.0 18.2 ± 6.9 20.4 ± 5.8 β - Flux, s-1 ♂ 0.15 ± 0.10 0.48 ± 0.25 0.72 ± 0.28 0.85 ± 0.31 0.89 ♀ 0.32 ± 0.24 0.46 ± 0.22 0.73 ± 0.36 0.74 ± 0.39 Aβ - Flow, AU s-1 ♂ 1.7 ± 0.9 6.6 ± 3.9 10.6 ± 5.5 13.5 ± 3.9 0.36 ♀ 5.4 ± 4.1 7.4 ± 3.0 11.7 ± 3.9 13.5 ± 6.1 Liver A - Volume, AU ♂ 36.1 ± 12.2 38.2 ± 10.5 36.0 ± 9.7 35.9 ± 10.0 0.82 ♀ 42.5 ± 11.2 35.5 ± 11.8 40.2 ± 12.3 31.8 ±13.2 β - Flux, s-1 ♂ 0.22 ± 0.09 0.23 ± 0.08 0.24 ± 0.09 0.22 ± 0.12 0.77 ♀ 0.22 ± 0.07 0.23 ± 0.08 0.22 ± 0.08 0.29 ± 0.13 Aβ - Flow, AU s-1 ♂ 8.3 ± 4.9 9.2 ± 4.7 9.1 ± 5.4 8.1 5.5 0.99 ♀ 8.8 ± 2.9 8.4 ± 4.6 8.7 ± 4.0 9.0 ± 5.8 Based on 10 males and 6 females. P-value is based off linear mixed models using TRIALS, DAY, and SEX as fixed factors and participant identifier as a random factor. Abbreviations: ITL, inspiratory pressure threshold loading. 120 Appendix C Instrumentation & Calibrations Figure 6.3. Inspiratory pressure threshold loader and its pressure-flow relationship across the physiological range. Figure 6.4. Time to video intensity plateau with continuous infusion of Definity microbubbles.
Thesis/Dissertation
eng
Health and Exercise Sciences
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Foster, Glen E.
Quantifying diaphragm blood flow with contrast-enhanced ultrasound in healthy humans : feasibility, validity, and reliability
Text