Association of Preoperative Spirometry with Cardiopulmonary Exercise Capacity and Postoperative Outcomes in Surgical Patients

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1 Association of Preoperative Spirometry with Cardiopulmonary Exercise Capacity and Postoperative Outcomes in Surgical Patients by Ashwin Sankar A thesis submitted in conformity with the requirements for the degree of Master of Science Institute of Health Policy, Management and Evaluation University of Toronto Copyright by Ashwin Sankar 2018

2 Association of Preoperative Spirometry with Cardiopulmonary Exercise Capacity and Postoperative Outcomes in Surgical Patients Abstract Ashwin Sankar Master of Science Institute of Health Policy, Management and Evaluation University of Toronto 2018 Spirometry and cardiopulmonary exercise testing (CPET) are specialized investigations which help stratify risk for postoperative complications. Prior research has found inconsistent associations between forced expiratory volume in 1 second (FEV1), and the ratio of FEV1 to forced vital capacity (FVC) from spirometry with postoperative complications. This thesis examines the hypothesis that FEV1 and FEV1/FVC are associated with peak oxygen uptake (VO2 peak) and ventilatory efficiency from CPET, which confound the association between FEV1 and outcomes. In a cohort of patients undergoing elective inpatient surgery, FEV1 exhibited a linear association of moderate magnitude with VO2 peak. The association of FEV1 with ventilatory efficiency, and FEV1/FVC with VO2 peak and ventilatory efficiency were weak. Following adjustment for clinical risk factors and VO2 peak or ventilatory efficiency, FEV1 was not associated with postoperative outcomes. Thus, previously observed associations between FEV1 and respiratory complications may be partly explained by confounding related to cardiopulmonary fitness. ii

3 Acknowledgments The support extended to me by family, friends, colleagues and mentors have made completing this work the highlight of my academic career to date. To my supervisor, Duminda Wijeysundera thank you for instilling in me the same enthusiasm with which you pursue academic perioperative medicine. Beyond the content, you have taught me that balancing research, clinical work, family and life is possible. Thank you for being so generous with your time, and for shaping how I ask questions and approach research ideas. To my committee, Kevin Thorpe, Andrea Gershon, John Granton I am indebted to each of you, as you have taught me how to use principles in clinical epidemiology to approach clinically relevant questions. Thank you for asking the tough questions that forced me to think critically. To my examiners, Nancy Baxter and Peter Lindenauer thank you for your thoughtful critique and thorough feedback which have immensely enhanced this thesis. To the Chairs of the Department of Anesthesia, Brian Kavanagh and Beverley Orser; and the Program Directors of the postgraduate Anesthesiology program, Mark Levine and Lisa Bahrey I cannot express how grateful I am for the incredible opportunity to pursue academic training alongside clinical residency. Your support has turned my interest in clinical research to a passionate pursuit of a career as a clinician investigator. To my clinical mentors at each of the University of Toronto hospitals thank you for patiently and thoughtfully developing my clinical skills while I concurrently pursued research training. To my close friends and colleagues, of whom there are too many to name thank you for keeping me balanced and grounded. You were instrumental both while celebrating the highs and getting me through the lows of this journey. Finally, to my parents, Nirupa and Sankar, and my sister, Anisha your unconditional love and support have made me who I am today, and for this, I am ultimately grateful. iii

4 Table of Contents Acknowledgments... iii Table of Contents... iv List of Tables... ix List of Figures... x Chapter 1 Background and Rationale Background Context Preoperative spirometry: predictive utility Cardiopulmonary Exercise Testing: an objective measure of functional capacity Relationship of outputs of spirometry and CPET: critical knowledge gaps Characteristics of ideal study to compare spirometry and CPET Study Questions Study Hypotheses Conceptual Framework... 7 Chapter 2 Design and Methods Design and Methods Study Design and Rationale Subject Selection: Inclusion and Exclusion Criteria Study Procedures Objective 1: Outcomes Objective 2: Outcomes Objective 1: Covariates Objective 2: Covariates Statistical Analysis iv

5 2.9 Statistical Analysis: Primary Objective Statistical Analysis: Secondary Objective Power Calculation Chapter 3 Results Results Objective 1: Adjusted association of preoperative FEV1, and FEV1/FVC with VO2 peak and Ventilatory Efficiency from CPET Characteristics of the study cohort Unadjusted association of FEV1, FEV1/FVC, and spirometry category with VO2 peak Unadjusted association of FEV1, FEV1/FVC, and spirometry category with Ventilatory Efficiency Adjusted association of FEV1, FEV1/FVC, and spirometry category with VO2 peak Adjusted association of FEV1, FEV1/FVC and spirometry category with VE/VCO Underlying model assumptions for Objective Objective 2: Adjusted association of preoperative FEV1 and spirometry pattern with outcomes, adjusting for cardiopulmonary fitness Characteristics of the study cohort Unadjusted associations with primary and secondary outcomes Adjusted association of FEV1 and pattern of spirometry findings with primary outcome, POMS respiratory morbidity, accounting for VO2 peak and Ventilatory Efficiency from CPET Adjusted association of FEV1 and pattern of spirometry findings with secondary outcome, PPC, accounting for VE/VCO2 from CPET Adjusted association of FEV1 and pattern of spirometry findings with secondary outcome, major cardiac complications, accounting for VO2 peak and VE/VCO2 from CPET Underlying model assumptions for Objective Chapter 4 Discussion and Conclusions v

6 4 Discussion Conclusions Figures Conceptual Framework for Objective Conceptual Framework for Objective Study Schematic Study participant flow diagram Postoperative Morbidity Survey Density Plots of FEV1 and FEV1/FVC Density Plots of VO2 peak and VE/VCO Unadjusted analysis of FEV1 & FEV1/FVC versus VO2 peak Unadjusted analysis of VO2 peak by spirometry category Unadjusted analysis of FEV1/FVC versus VO2 peak, stratified by spirometry category Unadjusted analysis of FEV1 & FEV1/FVC versus VE/VCO Unadjusted analysis of VE/VCO2 by spirometry category Unadjusted analysis of FEV1/FVC versus VE/VCO2, stratified by spirometry category Adjusted association of VO2 peak versus FEV1, ratio and covariates Contribution of predictor variables to R 2 in adjusted model of VO2 peak Adjusted association of VO2 peak by spirometry category and covariates Adjusted association of VE/VCO2 versus FEV1, ratio and covariates Contribution of predictor variables to R 2 in adjusted model of VE/VCO Adjusted association of VE/VCO2 by spirometry category and covariates Adjusted association of POMS respiratory morbidity with FEV1, VE/VCO2 and covariates Adjusted association of POMS respiratory morbidity with FEV1, VO2 peak and covariates Non-linear association of VE/VCO2 with PPC vi

7 6.23 Adjusted association of PPC with FEV1, truncated VE/VCO2 and covariates Adjusted association of major cardiac complications with FEV1, VO2 peak and covariates Adjusted association of major cardiac complications with FEV1, VE/VCO2 and covariates Tables Inclusion and Exclusion Criteria for the METS study Sample characteristics for Objective Adjusted analysis of VO2 peak with FEV1, FEV1/FVC and covariates Adjusted association of VO2 peak with spirometry category and covariates; results of spirometry category Adjusted association of VE/VCO2 with FEV1, FEV1/FVC and covariates Adjusted association of ventilatory efficiency with spirometry category and covariates; results of spirometry category Sample Characteristics for Objective Univariate associations with primary outcome, POMS respiratory morbidity Univariate associations with secondary outcome, PPC Univariate associations with secondary outcome, major cardiac complications Adjusted association of primary outcome, POMS respiratory morbidity with FEV1, VE/VCO2 and covariates Adjusted association of primary outcome, POMS respiratory morbidity with FEV1, VO2 peak and covariates Adjusted association of primary outcome, POMS respiratory morbidity with spirometry category, VO2 peak and covariates Adjusted association of secondary outcome, PPC with FEV1, truncated VE/VCO2 and covariates Adjusted association of secondary outcome, PPC with spirometry category, truncated VE/VCO2 and covariates Adjusted association of secondary outcome, major cardiac complications with FEV1, VO2 peak and covariates vii

8 7.17 Adjusted association of secondary outcome, major cardiac complications with FEV1, VE/VCO2 and covariates Adjusted association of secondary outcome, major cardiac complications with spirometry category, VO2 peak and covariates References viii

9 List of Tables 7.1 Inclusion and Exclusion Criteria for the METS study Sample characteristics for Objective Adjusted analysis of VO2 peak with FEV1, FEV1/FVC and covariates Adjusted association of VO2 peak with spirometry category and covariates; results of spirometry category Adjusted association of VE/VCO2 with FEV1, FEV1/FVC and covariates Adjusted association of ventilatory efficiency with spirometry category and covariates; results of spirometry category Sample Characteristics for Objective Univariate associations with primary outcome, POMS respiratory morbidity Univariate associations with secondary outcome, PPC Univariate associations with secondary outcome, major cardiac complications Adjusted association of primary outcome, POMS respiratory morbidity with FEV1, VE/VCO2 and covariates Adjusted association of primary outcome, POMS respiratory morbidity with FEV1, VO2 peak and covariates Adjusted association of primary outcome, POMS respiratory morbidity with spirometry category, VO2 peak and covariates Adjusted association of secondary outcome, PPC with FEV1, truncated VE/VCO2 and covariates Adjusted association of secondary outcome, PPC with spirometry category, truncated VE/VCO2 and covariates Adjusted association of secondary outcome, major cardiac complications with FEV1, VO2 peak and covariates Adjusted association of secondary outcome, major cardiac complications with FEV1, VE/VCO2 and covariates Adjusted association of secondary outcome, major cardiac complications with spirometry category, VO2 peak and covariates ix

10 List of Figures 6.1 Conceptual Framework for Objective Conceptual Framework for Objective Study Schematic Study participant flow diagram Postoperative Morbidity Survey Density Plots of FEV1 and FEV1/FVC Density Plots of VO2 peak and VE/VCO Unadjusted analysis of FEV1 & FEV1/FVC versus VO2 peak Unadjusted analysis of VO2 peak by spirometry category Unadjusted analysis of FEV1/FVC versus VO2 peak, stratified by spirometry category Unadjusted analysis of FEV1 & FEV1/FVC versus VE/VCO Unadjusted analysis of VE/VCO2 by spirometry category Unadjusted analysis of FEV1/FVC versus VE/VCO2, stratified by spirometry category Adjusted association of VO2 peak versus FEV1, ratio and covariates Contribution of predictor variables to R2 in adjusted model of VO2 peak Adjusted association of VO2 peak by spirometry category and covariates Adjusted association of VE/VCO2 versus FEV1, ratio and covariates Contribution of predictor variables to R2 in adjusted model of VE/VCO Adjusted association of VE/VCO2 by spirometry category and covariates Adjusted association of POMS respiratory morbidity with FEV1, VE/VCO2 and covariates Adjusted association of POMS respiratory morbidity with FEV1, VO2 peak and covariates Non-linear association of VE/VCO2 with PPC x

11 6.23 Adjusted association of PPC with FEV1, truncated VE/VCO2 and covariates Adjusted association of major cardiac complications with FEV1, VO2 peak and covariates Adjusted association of major cardiac complications with FEV1, VE/VCO2 and covariates xi

12 1 Chapter 1 Background and Rationale 1 Background 1.1 Context Every year, more than 300 million patients worldwide undergo major surgery, which imposes physiological stresses that are associated with significant morbidity and mortality. 1, 2 Following major elective noncardiac surgery, up to 20% of high-risk patients suffer complications, the development of which is strongly associated with adverse outcomes including increased length of stay, mortality, and healthcare resource utilization. 2-5 Preoperative identification of patients at elevated risk of complications can help physicians to accurately inform patients of expected risk, order specialized investigations, optimize chronic conditions, initiate interventions intended to decrease risk, and arrange appropriate levels of postoperative care. 6 Though the role of preoperative cardiac testing has been addressed in clinical practice guidelines published by the American Heart Association (AHA) and American College of Cardiology (ACC), 7 there is a relative paucity of evidence to inform the appropriate preoperative testing of respiratory function. Pulmonary function tests (PFTs) may be especially useful as an approach for identifying patients at risk of postoperative pulmonary complications (PPC). These complications are important. In contemporary cohorts of surgical patients, PPC occurred as frequently as cardiac complications at rates of up to 10%; development of PPC is associated with significant morbidity and mortality Preoperative spirometry: predictive utility PFTs are a specialized investigative modality available to the perioperative physician for assessment of respiratory pathology. 12 They assess the severity of known pulmonary disease, and help diagnose causes of respiratory symptoms such as wheezing and dyspnea. The most commonly used PFT is spirometry, which measures the volume of air exhaled at specific time points during forceful exhalation starting from a point of maximal inhalation. By measuring volumes of air exhaled and flow rates, flow-volume loops are generated; these provide an

13 2 assessment of the mechanical properties of the respiratory system. 12 In particular, the volume of air forcefully exhaled in the first second starting from maximal inhalation is called the forced expiratory volume in one second (FEV1); the total volume exhaled is called the forced vital capacity (FVC); and the ratio of FEV1/FVC are important variables reported during spirometry. 13 They help characterize the presence, type and severity of certain types of pulmonary disease, 12, 14 particularly major obstructive lung pathology. The utility of preoperative spirometry is reasonably well established for lung resection surgery, where thresholds based on FEV1 and diffusion capacity are emphasized in American College of Chest Physicians guidelines to assess surgical candidacy. 15 PFTs also stratify risk following aortocoronary bypass surgery, with reduced FEV1 and the FEV1/FVC ratio exhibiting strong associations with postoperative mortality, complications and length-of-stay By comparison, in major extra-thoracic surgery, the utility of outputs of spirometry for perioperative risk stratification is less clear. A critical review of the literature suggested that while spirometry may help identify some patients at higher risk of PPC, the data remain inconsistent. 16 Furthermore, the ability of spirometry to diagnose obstructive lung disease has not translated to perioperative risk stratification. For example, patients identified as having chronic obstructive pulmonary disease (COPD) on PFTs have not consistently been shown to be at elevated risk of perioperative complications. 16 Due to inconsistent associations between spirometry results and postoperative outcomes, a 2006 guideline from the American College of Physicians (ACP) stated that preoperative spirometry was not superior to history and physical examination in predicting PPC, and should be reserved for patients with suspected undiagnosed obstructive pulmonary disease. 19 Guidelines from the European Society of Anaesthesiology (ESA) additionally suggest that changes in clinical management due to findings from preoperative spirometry were not consistently reported, largely limiting their utility. 20 Perhaps in response to these guidelines, rates of preoperative PFT use in Canada has decreased, both overall and in subgroups stratified by risk of PPC; 21 these data suggested that spirometry was not incrementally being used for preoperative risk stratification beyond assessment of pre-existing lung disease. Despite the limited utility of spirometry highlighted in previous guidelines and among numerous studies in extra-thoracic surgery, ongoing research highlights distinct subpopulations of patients

14 3 among whom the utility of spirometry has not been established to date. 19, 20 Guidelines from both ACP and ESA acknowledge, based on systematic reviews, that insufficient evidence exists to make a recommendation for patients with restrictive lung disease undergoing extrathoracic surgery. 19, 20 For example, recent observational studies of obese patients (who can have restrictive lung pathology) undergoing laparoscopic surgery suggest that low FEV1 predicts PPC 22, 23 after adjustment for covariates such as age and smoking. To summarize, the association of spirometry outputs with outcomes remains unclear, particularly after extrathoracic surgery. 16 Additionally, the reasons why this modality may or may not provide useful information for certain procedures and subpopulations remains to be examined. For example, the association of FEV1 with mortality and complications after lung resection surgery and aortocoronary bypass surgery (both surgical subpopulation with degrees of cardiopulmonary impairment limiting exercise capacity) may be explained by the association of PFT results with cardiopulmonary fitness It is plausible that individuals identified as having diminished respiratory function on PFTs tend to have poor fitness, reduced exercise capacity, and limitations in ability to perform activities of daily living. 24 Since fitness is assessed routinely as part of preoperative assessment, 7 the results of PFT may have limited incremental utility should they measure a similar construct. Consistent with this possibility (and despite the association between spirometry outputs and measures of cardiopulmonary fitness not having been extensively studied to-date), previous guidelines have suggested that spirometry findings were not superior to preoperative clinical evaluation for risk stratification for postoperative complications Cardiopulmonary Exercise Testing: an objective measure of functional capacity Exercise capacity is recognized as a predictor of mortality, even in healthy populations. 25 Previously observed associations of limited exercise capacity with increased risk of postoperative complications have given rise to guidelines that emphasize assessment of exercise capacity as a critical component of any perioperative risk stratification strategy. 7 However, clinical estimation of functional capacity is often based on physicians subjective evaluation of patients self-reported history. Deficiencies have been identified with respect to the accuracy of 26, 27 self-reported functional capacity in predicting postoperative complications and death.

15 4 Importantly, self-reported functional capacity may be a specific, but not sensitive, predictor of risk of postoperative complications. 26, 27 These deficiencies have given rise to interest in objective measures of functional capacity, of which cardiopulmonary exercise testing (CPET) is often regarded as the gold-standard non-invasive test. CPET is a test of the cardiopulmonary system at rest and during increasingly loaded exercise; it provides a dynamic measure of cardiorespiratory performance which represents the integrated response to physiological stress. 6, 28, 29 Numerous physiological measurements are captured during CPET including heart rate, blood pressure, continuous electrocardiography (ECG), and inspired and expired gas concentration. Two parameters, the peak oxygen uptake (VO2 peak) calculated using inspired and expired gas concentrations during loaded exercise, and the anaerobic threshold (AT) derived as the presumed onset of anaerobic metabolism, are accepted as indices of cardiopulmonary exercise capacity; higher values for both measures represent greater fitness. 29, 30 In addition, the etiology of exercise limitation can be delineated based on patterns of limitations in other physiologic parameters on CPET. Ventilatory efficiency provides information about the effectiveness of ventilation for a given metabolic rate; it is characterized as the slope of the minute ventilation (VE) versus carbon dioxide production (VCO2) curves at AT. Higher VE/VCO2 slopes indicate poorer efficiency, which is evident in various clinical conditions including COPD and heart failure. Conversely, smaller VE/VCO2 slopes indicate greater efficiency. 31 At a physiologic level, ventilatory efficiency reflects ventilation-perfusion matching at the alveolar level, which is reflective of respiratory dead space, and increasingly appreciated as a marker of respiratory impairment during CPET. 32 CPET-derived parameters have exhibited associations with all-cause mortality among patients with cardiopulmonary disease. 33 These findings have extended to the perioperative context, albeit largely in retrospective studies with small sample sizes where clinicians and outcome adjudicators were not blinded to CPET results. 34, 35 Systematic reviews synthesizing these results have concluded that while CPET markers are largely associated with outcomes following major non-cardiopulmonary surgery, differing CPET parameters have exhibited associations with outcomes for different types of surgery. 32, 35, 36 For example, among patients undergoing major intraabdominal surgery, low AT was associated with increased risk of mortality and major adverse cardiac events, with a threshold AT below 11 ml/kg/min being associated with increased risk. 35 However, among patients undergoing repair of abdominal aortic aneurysms,

16 5 increased VE/VCO2 indicating poor ventilatory efficiency was associated with mortality, with values above a threshold of 42 exhibiting associations with increased risk. 37 Increased VE/VCO2 has also been associated with increased risk of PPC following lung resection surgery. 37 Finally, among patients undergoing colon resection surgery, VO2 peak was associated with increased risk, with values below 15 ml/kg/min exhibiting associations with increased morbidity based on length of stay, and postoperative complications based on the postoperative morbidity survey (POMS). 32, 35, 36 These findings underscored the need for a prospective blinded study of patients undergoing CPET prior to major non-cardiac surgery to delineate its ability to quantify perioperative risk. The Measurement of Exercise Tolerance before Surgery (METS) study was a large international multicentre prospective cohort study of major elective moderate-to-high risk surgical patients among whom blinded measures of cardiopulmonary fitness, including CPET, were assessed as predictors of postoperative mortality and morbidity. 38 The goal of the study was to inform clinical risk stratification by examining which testing modalities accurately identified patients at risk of death and complications following major surgery. This thesis is a sub-study of the METS study cohort, with a focus on assessing the association of spirometry with (i) CPET and (ii) postoperative outcomes. 1.4 Relationship of outputs of spirometry and CPET: critical knowledge gaps While both PFTs and CPET are used clinically in the perioperative context, there is paucity of clinical research characterizing the relationship of spirometry findings with CPET For example, Na et al. examined this relationship among patients with COPD, where patients were assigned a COPD severity grade based on FEV1 and FEV/FVC cutoffs in the GOLD classification scheme; these severity grades were compared against the VO2 peak-based cutoffs of COPD severity based on CPET. This study revealed large disagreement between severity of COPD assigned based on spirometric versus CPET parameters, as only 42% of patients were assigned the same grade of COPD between the two tests. 42 In a separate study of symptomatic smokers, Di Marco and colleagues found that CPET findings, such as the breathing reserve and ventilatory efficiency, may be diminished among patients with even borderline spirometry

17 6 results. 43 Notably, these studies comparing spirometry with CPET were unblinded with small sample sizes, precluding meaningful comparisons between the two tests. There is also limited evidence with respect to the comparative ability of both tests to predict outcomes following major surgery. Among patients being evaluated for lung resection surgery, which is usually indicated for cancer among patients with significant cardiopulmonary comorbidities, cutoffs based on preoperative FEV1 (with thresholds less than 2 L associated with increased risk of postoperative complications and mortality); postoperative FEV1 (with < 30% associated with increased of PPC); and VO2 peak (< 15 ml/kg/min associated with increased risk of PPC and mortality) are routinely used to assess surgical candidacy and stratify risk of postoperative complications. 15 There is limited evidence comparing the ability of outputs from spirometry and CPET to predict outcomes following extra-thoracic surgery. In one study of colorectal surgery patients, however, there were no differences in FEV1 and FEV1/FVC between patients who developed postoperative cardiopulmonary complications versus those who did not, while the AT was significantly different in the no-complication (mean 13.8, SD 3) versus complication (mean 10.9, SD 3) groups, suggesting that AT may more accurately predict complications than those spirometry derived measures. 44 However, this study was conducted in a small sample (n = 69) of patients undergoing one type of surgery, with significant proportion of recruited patients being unable to complete the CPET protocol. Additional work is therefore necessary to compare the relative ability of CPET and spirometry to predict outcomes after major surgery, and importantly, to address the question of whether cardiopulmonary fitness may confound the relationship between spirometry and postoperative outcomes. 1.5 Characteristics of ideal study to compare spirometry and CPET An ideal study comparing outputs from spirometry with measures of cardiopulmonary fitness from CPET would have several key characteristics. The study would include patients undergoing surgery and at risk of complications, these individuals would all undergo preoperative spirometry and CPET based on standardized protocols. The test results would be blinded from clinicians and outcome assessors. Finally, patients would be followed-up and outcomes would be measured in a standardized fashion. The METS study met each of these criteria and therefore presented a unique opportunity to compare spirometry parameters with cardiopulmonary fitness based on

18 7 measures from CPET. 38 In this substudy of the METS study cohort of major surgical patients, I compared the associations between spirometry parameters with measures of cardiopulmonary fitness from CPET. Additionally, I determined the association of FEV1 and pattern of spirometry results with postoperative outcomes, while adjusting for measures of cardiopulmonary fitness from CPET. 1.6 Study Questions 1. What is the association between FEV1 and FEV1/FVC from spirometry with VO2 peak and ventilatory efficiency from CPET among patients undergoing major inpatient noncardiac surgery? 2. What is the association between FEV1 from spirometry with postoperative cardiac and respiratory adverse outcomes in patients undergoing major inpatient noncardiac surgery? a. How do VO2 peak and ventilatory efficiency from CPET affect the association between spirometry and outcomes? 1.7 Study Hypotheses 1. Reductions in FEV1 and FEV1/FVC are associated with reductions in VO2 peak and diminished ventilatory efficiency (higher VE/VCO2 slope) 2. Reductions in FEV1 are associated with increased risk of postoperative respiratory and cardiac adverse outcomes a. VO2 peak and ventilatory efficiency are confounders in the association between FEV1 and outcomes 1.8 Conceptual Framework A two-part conceptual framework was developed to: first, describe the relationship between FEV1 and FEV1/FVC from spirometry with VO2 peak and ventilatory efficiency (based on VE/VCO2 slope) from CPET (Figure 6.1), and next, to describe the relationship between spirometry and outcomes (Figure 6.2).

19 8 Objective 1 of this thesis involves a cross-sectional analysis of the association between spirometry outputs (FEV1 and FEV1/FVC) and CPET measures (VO2 peak and ventilatory efficiency). Since spirometry is a measure of the mechanical properties of the respiratory system, while CPET measures the combined cardiopulmonary response to the aerobic stress of exercise, 6, 28, 29 decreases in FEV1 and FEV1/FVC (indicating limitations on spirometry) may be reflected in decreases in cardiopulmonary fitness on CPET (lower VO2 peak and diminished ventilatory efficiency, based on higher VE/VCO2 slope). Similar findings were observed in previous small cross-sectional studies, where VO2 peak on CPET was generally worse among patients with more severe obstructive lung disease (based on the spirometry-based GOLD criteria) However, differences were noted in the severity of COPD assigned based on VO2 peak versus FEV1, raising the question of whether other factors may be impacting their relationship. 42 Both spirometry and CPET are influenced by other conditions, such as age, sex, and comorbid disease (e.g., coronary artery disease, smoking). 13, 29 In addition, abnormal CPET parameters (including ventilatory efficiency) have previously been identified among smokers with only borderline spirometry results, 43 suggesting that CPET may allow for earlier identification of pulmonary pathology than measures from spirometry. These characteristics were therefore considered as confounders in the association between outputs of spirometry with CPET measures. With respect to Objective 1, the adjusted association between spirometry and CPET will help assess the relationship between these tests while controlling for confounders. Objective 2 of this thesis examines the association of preoperative FEV1 and pattern of spirometry findings with postoperative outcomes. The FEV1 outcomes relationship may also be confounded by age, sex, and comorbid conditions (e.g., coronary artery disease), which are each associated with reductions in pulmonary function and increased risk of postoperative adverse outcomes; 15 these factors will be adjusted for in this analysis. Prior research has shown that while reductions in lung function identified by spirometry have been associated with mortality and morbidity following certain procedures such as lung resection surgery and aortocoronary bypass, these associations have not been consistently identified following extra-thoracic surgery The reasons for these inconsistencies have not been examined; however, an important contributor may be cardiopulmonary fitness. It is plausible that because fitness is clinically measured as part of preoperative evaluation, spirometry has limited further incremental prognostic value. The ability of VO2 peak and ventilatory efficiency from CPET to either confound or effect-modify

20 9 the association between FEV1 and outcomes will be explored in Objective 2. Potential confounding will be examined by the inclusion of VO2 peak or ventilatory efficiency as a covariate in the FEV1 outcomes association, while effect modification will be examined by further adding an interaction term between FEV1 with either VO2 peak or ventilatory efficiency when modeling the FEV1 outcomes association.

21 10 2 Design and Methods 2.1 Study Design and Rationale Chapter 2 Design and Methods The primary data source for this thesis is the Measurement of Exercise Tolerance before Surgery (METS) study. 38, 45 This was an international multicentre prospective cohort study with the aim of identifying the most accurate means of assessing preoperative cardiopulmonary fitness and its association with postoperative outcomes among intermediate-to-high risk patients undergoing major elective non-cardiac surgery. This thesis is a sub-study of the METS study. In brief, the design of the METS study was as follows. 38 Participants were recruited in institutional preoperative anesthetic clinics and surgical wards by trained research personnel following a structured informed consent discussion. Consenting participants underwent spirometry and CPET within 90 days before surgery using standardized protocols 29 under physician supervision, the results of which were blinded from patients, perioperative clinicians and outcome adjudicators. Every patient underwent routine pre-, intra-, and post-operative care at the discretion of the anesthetic, surgical and medical teams in a manner reflective of routine clinical practice. Patients had routine in-hospital surveillance with daily ECG and troponin measurements up to postoperative day 3. The cohort was followed for 30 days postoperatively for postoperative complications and up to a year for the outcome of death all of which were assessed by blinded outcome assessors. As the goals of this thesis were to examine the relationships between FEV1 and FEV1/FVC (from preoperative spirometry) with VO2 peak and ventilatory efficiency (from preoperative CPET), and postoperative outcomes among surgical patients, an observational design was chosen as no intervention was being assigned to patients (Figure 6.3). The primary study objective (Objective 1) was addressed in a nested cross-sectional study using outputs of preoperative spirometry (FEV1, FEV1/FVC) and measures of cardiopulmonary fitness (VO2 peak and ventilatory efficiency on preoperative CPET) from the METS study. The secondary study objective (Objective 2) to evaluate the relationship between FEV1 and outcomes was addressed in a nested

22 11 cohort study using prospectively collected pre-, intra- and post-operative data on complications up to 30 days following surgery. 2.2 Subject Selection: Inclusion and Exclusion Criteria The METS study recruited intermediate-to-high risk patients undergoing elective inpatient noncardiac surgery, who warranted additional screening as per ACC/AHA guidelines. 7 Patients must have met the following inclusion criteria: age 40 years, undergoing major inpatient (requiring an overnight hospital stay) elective non-cardiac surgery using general and/or regional (spinal, epidural, peripheral nerve blockade) anesthesia. In addition, to be at elevated risk for cardiac complications and mortality, at least one of the following clinical factors must have been present (Table 7.1): 7 intermediate-to-high risk surgery (i.e., intra-peritoneal, intra-thoracic or major vascular procedures); coronary artery disease; heart failure; cerebrovascular disease; diabetes mellitus requiring insulin or oral hypoglycemic therapy; preoperative renal insufficiency defined as an estimated glomerular filtration rate <60 ml/min/1.73 m 2 based on the Modification of Diet in Renal Disease equation; peripheral arterial disease; hypertension; smoker (history of smoking within 1 year before surgery); or age 70 years. Patients were excluded from the METS study for (Table 7.1): known or suspected pregnancy; inadequate time for completion of CPET; planned use of CPET independent of study purposes; planned endovascular approach to surgery; presence of an automated implantable cardioverter-defibrillator; active unstable cardiac conditions according to ACC/AHA guidelines; 7 absolute contraindications to CPET according to the American Thoracic Society guidelines; 29 and if they had conditions that preclude CPET (e.g. lower extremity amputation). In the interest of safety, patients were also excluded from the study if their FEV1 was less than 30% predicted on spirometry. Also, any patients experiencing chest pain suggestive of angina or ischemic ECG changes during CPET were immediately referred for additional follow-up and treatment; their records were unblinded, and any changes to their clinical course were noted. METS study data were collected from May 2013 to March For this thesis, the diagram detailing participant flow through the study is presented in Figure 6.4. The nested cross-sectional study (Objective 1 of the thesis) included all METS study participants who completed both spirometry and CPET (n = 1260). The nested cohort study (Objective 2) included all METS study participants (n = 1200) who completed spirometry, CPET and underwent surgery with the exception of lung resection surgery, which is associated with

23 12 systematically different risk of complications compared to the remaining cohort. Also, the surgical resection of lung tissue has important implications in that preoperative spirometry does 9, 46 not reflect postoperative lung function. 2.3 Study Procedures Patients underwent the following standardized testing protocol within 90 days before their scheduled surgery as part of the METS study. Participants underwent spirometry, where inspiratory and expiratory flow-volume loops were generated. FEV1 and FVC were recorded both as crude estimates and as percentage of predicted values, and were defined as follows (primary exposures): FEV1: volume of gas exhaled during the first second of a forced expiration, starting from a position of full inspiration; expressed in L and as a % predicted (following correction for age, sex, and height based on the reference standards used clinically in each participating institution) 2. FVC: volume of gas that is exhaled during a forced expiration, starting from a position of full inspiration and ending at complete expiration; expressed in L and as a % predicted (following correction for age, sex, and height based on the reference standards used clinically in each participating institution) 3. FEV1/FVC ratio: ratio of FEV1 to FVC; expressed as a percentage Based on predictive equations of spirometry-based lung function parameters from large population-based studies, 48, 49 the lower limit of normal values of FEV1, FVC and FEV1/FVC ratio were determined, and the patterns of spirometry results were categorized as normal, obstructive, restrictive or mixed based on guidelines from the American Thoracic Society and the European Respiratory Society. 13 In brief, FEV1/FVC ratios below lower limits of normal indicated potential obstruction, while FVC values below lower limits of normal indicated potential restriction. Patients with FEV1/FVC ratios above the lower limits of normal were classified as normal if their FVC exceeded the lower limit of normal, or restrictive if their FVC was less than the lower limit of normal. Patients with FEV1/FVC ratios below the lower limits of normal were classified as obstructive if their FVC was above the lower limit of normal, or mixed if the FVC was below the lower limit of normal. 13 In categorizing patterns of

24 13 results on spirometry, I was limited to information that was collected in the METS study protocols. Though patients in clinical practice with reductions in FVC may be referred for full PFTs to delineate total lung capacity (TLC) and characterize restriction, this information was not collected. As a result, definitions of restriction based upon FVC and the FEV1/FVC ratio were used in this thesis. Previous research has shown moderate-to-strong correlation between FVC and TLC (r = 0.66), and that less than 3% of patients with FVC greater than lower limits of normal have restrictive lung pathology based on TLC measurements. 50 While some limitations have been noted when defining restriction based on fixed FEV1/FVC thresholds and percent predicted values of FVC, the chosen cut-offs have high specificity (96-98%) with acceptable sensitivity (greater than 70%). 50, 51 Furthermore, defining patterns of findings on spirometry based on the lower limits of normal is increasingly favoured, and a growing body of evidence suggests the use of these definitions based on well established standards for more accurately classifying both obstructive and restrictive pathology, particularly among older patients where 52, 53 fixed cut-offs and percent predicted thresholds may overestimate the severity of pathology. Following spirometry, patients underwent CPET using an electromagnetically braked cycle ergometer in accordance with published guidelines. 29 The CPET protocol began with unloaded pedaling for 3 minutes, following which participants pedaled at 60 revolutions per minute against an incremental load. The load started at 10 watts and increased gradually by 10-watt increments in 1-minute intervals. They exercised until they reached their limit of tolerance (at which they could not pedal at 60 revolutions per minute, despite encouragement from a trained technician). During this time, participants underwent breath-by-breath measurement of minute ventilation, oxygen uptake and carbon dioxide production from expired gas. The following variables were measured during CPET: Peak oxygen uptake (VO2 peak): defined as the average VO2 during the last 20 seconds of the incremental phase of exercise before attaining the limit of tolerance; measured in ml O2 / kg / min 2. Ventilatory efficiency: defined as the slope of the minute ventilation (VE) to CO2 production (VCO2) curves at AT. The AT was determined using the modified V-slope method, and confirmed by patterns of changes in ventilator equivalencies and end-tidal gas measurements. Ventilatory efficiency, being a slope, has no units; however higher values represent poorer efficiency, and lower values represent greater efficiency.

25 Objective 1: Outcomes For the first thesis objective, the co-primary outcomes were VO2 peak and VE/VCO2. VO2 peak was chosen as a global measure of cardiopulmonary fitness; higher values represent greater fitness. VE/VCO2 slope (as a measure of ventilatory efficiency) was chosen as a respiratory measure, with lower VE/VCO2 slope values indicating greater respiratory efficiency and fitness. Both parameters are commonly utilized, reliably measured and well-established physiologic markers obtained during protocolized CPET. 29, 30 CPET parameters were determined by local site investigators using full-page graphs of plotted CPET data. 2.5 Objective 2: Outcomes For the second thesis objective, the primary outcome was the respiratory organ dysfunction score of the validated postoperative morbidity survey (POMS). These POMS scores were prospectively assigned to patients on postoperative days 3 and POMS is based on welldefined criteria for assessing major organ system function (Figure 6.5). Prior work examining this instrument has revealed: high inter-rater agreement (kappa values of ); concurrent construct validity in that it is correlated with other indices of patient health such as the American Society of Anesthesiologists Physical Status (ASA-PS) classification and the Physiological and Operative Severity Score for the enumeration of Mortality and morbidity (POSSUM); and criterion validity in that it is correlated with hospital length of stay. 54, 55 Respiratory morbidity is defined by POMS as the de novo requirement for supplemental oxygen or other respiratory support, which approximates contemporary definitions of respiratory failure; 56 respiratory failure is increasingly used in trials of respiratory interventions and in perioperative medicine as the 8, 56 respiratory outcome of interest. The secondary outcomes were postoperative pulmonary complications (PPC) and major cardiac complications. PPC were defined as the composite of: requirement of mechanical ventilation within 30 days after surgery (including any episode of ventilation > 4 hours in PACU); respiratory failure defined as any condition requiring intubation of the trachea and mechanical ventilation after completion of surgery, emergence from anesthesia, successful extubation (if intubated during surgery), and spontaneous ventilation for > 1 hour after surgery; and pneumonia, defined as any condition with documented hypoxemia or fever with clinical, radiologic or serologic evidence of a respiratory infection. 9, 14, 57 Though PPC have been

26 15 46, 58 heterogeneously defined in the literature, the endpoint is commonly used, even in large trials. While a limitation of composite outcomes is that they weight events of differing severity similarly, 46, 56 the components of this secondary endpoint are exclusively those of high severity. Major cardiac complications were defined as the composite outcome of non-fatal myocardial injury and/or death within 30 days after surgery. 30-day mortality in perioperative medicine is established as an end-point that reflects the burden of the perioperative period. 2, 3 Myocardial injury is increasingly appreciated as being a common and severe postoperative complication that 5, 59 is highly associated associations with mortality. These outcomes were ascertained based on in-hospital follow-up by trained research personnel who reviewed medical records during participants hospitalization; research staff contacted participants by phone following discharge for an assessment of 30-day outcomes. Notably, all outcome assessors were blinded to spirometry and CPET results. 2.6 Objective 1: Covariates For the first thesis objective, the covariates used for risk adjustment included the following variables, chosen a priori based on clinical sensibility: 1. Demographic characteristics: age on date of recruitment, sex, and BMI (calculated for each patient based on height and weight measured prior to CPET). 2. Preoperative comorbidities: the following cardiac and pulmonary comorbidities were included as covariates, due to expected associations of these variables with both exposures (FEV1 and FEV1/FVC) and outcomes (VO2 peak and ventilatory efficiency). Each were obtained preoperatively based on review of patient history and relevant 5, 6, 60, 61 medical records. a. Coronary artery disease, defined as: any history of angina; myocardial infarction; positive exercise, nuclear or echocardiographic stress test; resting wall motion abnormalities on echocardiogram; coronary angiography with evidence of 50% vessel stenosis; or ECG with pathological Q-waves in two contiguous leads. b. Smoking history, defined as any history of smoking in the year prior to surgery

27 16 c. Atrial fibrillation, defined as any episode of atrial fibrillation within the year prior to surgery d. Hypertension, defined as physician diagnosis of hypertension preceding surgery 2.7 Objective 2: Covariates For the second thesis objective examining the association of FEV1 with postoperative outcomes, the following variables were included a priori based on clinical sensibility for risk adjustment, 5, 6, 60, 61 due to associations of these variables with both exposure and outcomes of interest. 1. Demographic variables: age, sex and BMI; as stated in Section Preoperative comorbidities, each were obtained preoperatively based on review of patient history and relevant medical records. a. Coronary artery disease, as defined in Section 2.6 b. Smoking history, as defined in Section Upper abdominal and/or thoracic surgery, defined as any intra-thoracic, intra-peritoneal or retro-peritoneal procedure was obtained from patient history and based on documentation of the planned surgical procedure 2.8 Statistical Analysis Summary statistics were generated for the cohort; categorical variables were described using counts and frequencies, and continuous variables were described using means with standard deviations and medians with interquartile ranges. The distributions of all continuous variables were plotted. Descriptive statistics were generated to summarize demographics (such as age, sex, and body mass index), patient comorbidities, and type of surgical procedure. The independent variables for both analyses were FEV1 and FEV1/FVC from spirometry. The distributions of spirometry parameters FEV1, FVC, and FEV1/FVC ratio were plotted using density plots. The proportion of patients within each pattern of spirometry results (obstructive, restrictive, mixed and normal) was evaluated. Similarly, the distributions of VO2 peak and

28 17 ventilatory efficiency from CPET were also graphed using density plots, and summary statistics were generated. All statistical analyses were conducted using the R statistical package, version (R Core Team [2016], Statistical Analysis: Primary Objective Scatter plots were was used to graphically examine the unadjusted association of each of FEV1 and FEV1/FVC against each of VO2 peak and ventilatory efficiency. The magnitude of association was described using the Spearman ρ rank correlation coefficient. For this analysis, Spearman s ρ of indicated very weak; indicated weak; indicated moderate; indicated strong; and indicated very strong correlation between indices. 62 To assess for possible non-linearity, continuous spirometry variables were expressed as 4-knot restricted cubic splines; the linearity of the association was examined graphically and using the ANOVA test. Also, box-plots were generated of VO2 peak and ventilatory efficiency by pattern of spirometry results (normal, restrictive, obstructive or mixed). Finally, scatter plots of FEV1/FVC ratio versus each of VO2 peak and ventilatory efficiency were plotted within groups defined by pattern of spirometry, and a linear line-of-best-fit was generated for each spirometry category. Multivariable modelling was used to determine the adjusted association between spirometry outputs and CPET parameters, with separate multivariable linear regression models generated for each of VO2 peak and ventilatory efficiency. The covariates for inclusion into the models were identified from the literature a priori based on clinical sensibility. Multicollinearity was assessed based on the Variance Inflation Factor (VIF), with VIF values > 10 indicating possible multicollinearity. All continuous predictor variables were expressed as 4-knot restricted cubic splines to account for possible non-linearity in their association with outcomes. The adjusted association of FEV1 and FEV1/FVC was examined graphically, and the model summary based on the ANOVA test was used to assess the adjusted association of each predictor with outcome. The percent variation in the outcome variable accounted for by the model predictors was assessed using the coefficient-of-determination R 2 statistic. The contribution of each model predictor variable to R 2 was evaluated.

29 18 The adjusted association of spirometry with CPET was also examined by expressing spirometry categorically based on patterns of spirometry findings (i.e. restrictive, obstructive, mixed or normal) for adjusted models of each of VO2 peak and ventilatory efficiency. The adjusted association of spirometry pattern with each of VO2 peak and ventilatory efficiency was graphically expressed. Underlying model assumptions were verified, including: normality of residuals based on histograms and Q-Q plots of model residuals; the presence of influential outliers based on Cook s D statistic, with an absolute threshold of 2; heteroscedasticity based on residual plots by each independent variable. Non-linearity was accounted for by using splines in modeling continuous variables, and independence in outcomes was assumed via knowledge of the cohort Statistical Analysis: Secondary Objective The secondary objective of this thesis was to evaluate the association of FEV1 with specific postoperative outcomes, while accounting for VO2 peak and ventilatory efficiency. The primary outcome of interest was respiratory morbidity based on POMS, while the secondary outcomes were PPC and major cardiac complications. Unadjusted associations between demographics, comorbidities, surgical factors, FEV1 and FEV1/FVC and CPET outputs (VO2 peak and ventilatory efficiency) with each of the primary (POMS respiratory morbidity) and secondary (PPC and major cardiac complications) outcomes were assessed using the Chi-square statistic (or Fisher s exact test where expected cell counts were < 5) for categorical variables and the Wilcoxon rank sum test for continuous variables. POMS respiratory morbidity The primary adjusted analysis employed a multivariable logistic regression model of the primary outcome, POMS respiratory morbidity, against the main exposure, FEV1, since it is clinically used to classify respiratory disease severity. 39 The covariates were selected a priori based on clinical sensibility: age, sex, BMI, comorbidities (coronary artery disease and smoking status) and surgical site (upper abdominal and/or thoracic). 5, 6, 60, 61 To evaluate potential confounding versus effect modification by cardiopulmonary fitness (ventilatory efficiency was chosen in this analysis as a respiratory outcome was being examined), two models were generated: the first

30 19 included ventilatory efficiency as a covariate to examine confounding by CPET; the other further included an interaction between ventilatory efficiency and FEV1 to account for potential effect modification. These models were compared using the Likelihood Ratio test, and ventilatory efficiency was modeled as either a covariate or as an effect modifier. FEV1 and ventilatory efficiency were expressed as 4-knot restricted cubic splines to account for potential non-linearity in association with the probability (logit transformation) of the outcome. The total number of degrees of freedom among the predictor variables were limited by the observed outcomes based on the 10:1 rule to limit bias in estimated model coefficient values. The model was used to determine the adjusted association of FEV1 and ventilatory efficiency with POMS respiratory morbidity, and the ANOVA test was used to evaluate the association of each predictor variable with outcome. The association of FEV1 with POMS respiratory morbidity was also assessed while instead using VO2 peak as the CPET parameter of interest. Finally, the same association was examined expressing spirometry in categories (i.e. obstructive, restrictive, mixed or normal pattern) based on the lower limits of normal definition. 13 PPC The model predicting PPC as the outcome of interest was limited due to low outcome event frequency. A preliminary model was developed including only FEV1 and ventilatory efficiency (each expressed as 3-knot restricted cubic splines) as predictors in the model. Thresholds were generated to account for any non-linear associations of FEV1 or ventilatory efficiency with the probability (logit transformation) of PPC. To estimate the adjusted association of FEV1 with PPC, the following predictors were used in the model: FEV1, ventilatory efficiency (transformed based on identified thresholds), age, sex and site of surgery. Model assumptions were verified. The adjusted association of PPC was also examined while expressing spirometry in categories of pattern of spirometry findings. In this model, only spirometry category, age and surgical site were included as predictors to prevent overspecification. Model assumptions were also verified. Major cardiac complications

31 20 The adjusted association of FEV1 with major cardiac complications was determined using VO2 peak as the CPET parameter of interest since a cardiac outcome was being modeled. FEV1 and VO2 peak were expressed as 4-knot restricted cubic splines; and the same covariates were chosen for inclusion as were included in the adjusted analysis of POMS respiratory morbidity. Effect modification versus confounding by VO2 peak was examined. Model outputs were validated and assumptions verified. In a supplemental analysis, the modeling analysis was repeated with ventilatory efficiency substituted as the CPET parameter of interest; interaction versus confounding by ventilatory efficiency was examined, and the appropriate model was generated. Model assumptions were verified. Finally, the adjusted association of spirometry with outcomes, while accounting for CPET was also undertaken modeling spirometry categorically based on patterns of results. Model assumptions Each logistic regression model was evaluated for: model fit and discrimination, model prediction, and influential observations. Model discrimination was characterized using the optimism-corrected c-statistic following bootstrap validation. Model calibration was assessed by plotting fitted versus observed likelihoods of events among 1,000 bootstrap samples. The following underlying model assumptions were verified: influential observations were identified using casewise diagnostics and plots of model DFFITS with an absolute value > 0.4; overspecification was assessed noting model fit and separation of data-points. Independence in outcomes was assumed due to knowledge of the dataset Power Calculation Power calculations were carried out using the POWER procedure on SAS Version 9.4 (Cary, NC, USA). Analyses pertaining to Objective 1 included 1260 patients who completed protocolized spirometry and CPET. For a multivariable linear regression model, I could detect an incremental R 2 of modeling a 4-knot spline function for the key predictor variable, FEV1, while adjusting for 15 other coefficients with 90% power at an alpha of 0.05 with n = 1260 observations.

32 21 For analyses pertaining to Objective 2, 1200 met study criteria, of whom n = 129 had the primary outcome (POMS respiratory morbidity). For a logistic regression model with 12 binary coefficients with N = 1200 of whom n = 129 had the primary event, an odds ratio of 1.4 can be detected for the key predictor with 90% power at an alpha of 0.05.

33 22 Chapter 3 Results 3 Results 3.1 Objective 1: Adjusted association of preoperative FEV1, and FEV1/FVC with VO2 peak and Ventilatory Efficiency from CPET Characteristics of the study cohort A total of 1260 patients were included in analyses pertaining to Objective 1 of this thesis (study flow diagram, Figure 6.4). Characteristics of this cohort are described in Table 7.2. The cohort was comprised of patients with a median age of 65 years and median BMI of 28 kg/m 2 ; 63% (n= 797) of participants were male. Of note, 14% (n = 181) had a history of smoking in the year preceding the study, and 12% (n = 151) had known obstructive lung disease. Among comorbidities, the most common were hypertension (54%) and diabetes mellitus (18%). The median FEV1 was 2.65 L which corresponded to 95% predicted; similarly, median FVC was 3.51 L which also corresponded to 96% predicted. Based on density plots of FEV1 and FEV1/FVC (Figure 6.6) and interquartile ranges ( for % FEV1 predicted; for % FVC predicted), most participants had spirometry parameters within normal ranges. In categorizing spirometry based on patterns of results, 67% (n = 847) had normal pattern, 14% (n= 171) had an obstructive pattern, 16% (n= 205) had a restrictive pattern. A minority of patients (3%, n= 37) had a mixed obstructive and restrictive pattern of spirometry. With respect to CPET, the median VO2 peak was 19 ml O2/kg/min, and median VE/VCO2 was 31. Density plots of VO2 peak and VE/VCO2 (Figure 6.7) revealed distributions centred around the median; however, right-tailed distributions were observed, despite greater VO2 peak representing greater fitness and higher VE/VCO2 representing diminished ventilatory efficiency Unadjusted association of FEV1, FEV1/FVC, and spirometry category with VO2 peak Scatter plots of FEV1 versus VO2 peak and FEV1/FVC ratio with VO2 peak were used to examine the unadjusted association between these variables (Figure 6.8). A positive correlation of moderate magnitude was observed between FEV1 and VO2 peak (Spearman ρ = 0.48, p <

34 ). When FEV1 was expressed as a 4-knot restricted cubic spline, non-linearity was not detected (p = 0.24), suggesting that the FEV1-VO2 peak association was linear. Every 1L decrease in FEV1 was associated with a decrease in VO2 peak of 3.38 ml / kg / min ( ). A negative correlation, albeit of very weak magnitude (Spearman ρ = -0.09, p < 0.001), was observed between FEV1/FVC and VO2 peak (Figure 6.8). Expressing FEV1/FVC as a 4-knot restricted cubic spline, a local maximum was observed around a ratio of 0.7. Since clinically, lower degrees of obstructive disease (higher FEV1/FVC ratio) would not be associated with lower levels of fitness, the relationship between FEV1/FVC with VO2 peak was further explored to account for patterns of spirometry findings. Spirometry was expressed categorically based on pattern (i.e. obstructive, restrictive, mixed or normal), and the distribution of VO2 peak within each category was examined separately using box-plots (Figure 6.9). This analysis revealed higher median VO2 peak in normal and obstructive spirometry groups compared with restrictive and mixed groups. Also, outliers were observed with high VO2 peak values particularly in the normal spirometry pattern group. When the FEV1/FVC ratio versus VO2 peak relationship was plotted for each group defined by pattern of spirometry results (Figure 6.10), the following patterns emerged: lower FEV1/FVC was associated with lower VO2 peak among mixed and obstructive spirometry groups; lower FEV1/FVC was associated with lower VO2 peak, albeit of smaller magnitude among the restrictive spirometry group; finally, lower FEV1/FVC was associated with higher VO2 peak among patients with normal spirometry. However, outliers with high VO2 peak in the normal spirometry group (Figure 6.9) likely contributed to the association observed in patients with normal spirometry Unadjusted association of FEV1, FEV1/FVC, and spirometry category with Ventilatory Efficiency Scatter plots of each of FEV1 and FEV1/FVC versus VE/VCO2 were plotted (Figure 6.11). These revealed negative correlations, albeit weak in magnitude, between FEV1 and VE/VCO2 (Spearman ρ = -0.26, p < 0.001) and between FEV1/FVC and VE/VCO2 (Spearman ρ = -0.23, p < 0.001). Expressing FEV1 and FEV1/FVC as 4-knot restricted cubic splines revealed linear associations with VE/VCO2 (p = 0.78 and p = 0.85, respectively). Every 1L decrease in FEV1 was associated with an increase in VE/VCO2 of 1.83 ( ). Similarly, every 10% decrease in ratio was associated with an increase in VE/VCO2 of 1.80 ( ). These

35 24 findings were in the expected direction clinically, as decreases in FEV1 and FEV1/FVC were correlated with increases in VE/VCO2, indicating diminished ventilatory efficiency. VE/VCO2 was also compared against each spirometry category using box-plots (Figure 6.12). This revealed higher median VE/VCO2 in mixed and obstructive groups compared with normal and restrictive groups. Outliers with high VE/VCO2 were also observed in this analysis, particularly in the normal spirometry group. When the scatter plot of VE/VCO2 versus FEV1/FVC was plotted considering spirometry category (Figure 6.13), a negative association was observed in all groups, where higher FEV1/FVC ratio was associated with lower VE/VCO Adjusted association of FEV1, FEV1/FVC, and spirometry category with VO2 peak Linear regression was used to determine the adjusted association of FEV1 and FEV1/FVC with VO2 peak. In selecting the exposure variables, I first assessed for multicollinearity. The inclusion of all three spirometry parameters resulted in high likelihood of multicollinearity (Variance Inflation Factors of 54.6, 53.9 and 12.7 for FEV1, FVC and FEV1/FVC, respectively). Inclusion of FEV1 and FVC as predictors resulted in some suggestion of collinearity (Variance Inflation Factors 6.2 and 5.7, respectively). Inclusion of FEV1 and FEV1/FVC revealed Variance Inflation Factors < 1.2 and no collinearity; therefore, these were the chosen exposure variables for this analysis. These variables are also used clinically to describe the type and severity of obstructive lung disease. 63 Both these exposure variables, and other continuous variables (age and BMI) were expressed as 4-knot restricted cubic splines. The remaining covariates were chosen a priori based on clinical sensibility as described in Methods. The adjusted association of VO2 peak with FEV1, FEV1/FVC and covariates are shown in Table 7.3. Following risk adjustment, both FEV1 and FEV1/FVC were associated with VO2 peak; these adjusted associations are graphically represented in Figure There was some suggestion of non-linearity in the adjusted relationships of FEV1/FVC with VO2 peak. These plots revealed lower observed VO2 peak with lower FEV1; this relationship was likely linear (p = 0.66). A decrease in FEV1 of 1L corresponded to a decrease in VO2 peak of 3.49 ml O2 / kg / min ( ). Higher VO2 peak was observed with lower FEV1/FVC; however the higher observed VO2 peak was not consistent and there was some suggestion of non-linearity (p = 0.10). A decrease in FEV1/FVC to 70% from 80% was associated with an increase in VO2 peak of 1.04 ml O2 / kg /

36 25 min ( ). This association of high FEV1/FVC ratio with low VO2 peak is not clinically sensible, as increasing FEV1/FVC clinically indicates less severe obstructive pathology. The adjusted model had an R 2 of 0.34 indicating that 34% of the variation in VO2 peak was explained by the model. The relative contribution of predictor variables to the model R 2 is shown in Figure FEV1 had the largest contribution to R 2 and explained more than 8% of variation in VO2 peak; FEV1/FVC had a smaller but significant contribution of 2% (p < for both). The adjusted association of spirometry with VO2 peak was also examined expressing patterns of spirometry results in categories to account for findings related to the relationship of FEV1/FVC with VO2 peak. The same covariates as the previous analysis were included for risk adjustment. This adjusted linear regression model had a model R 2 of 0.32, indicating that 32% of the variation in VO2 peak was explained by the model. In this model, spirometry (in categories) was associated with VO2 peak after adjustment. Mixed (p < 0.001) and restrictive (p < 0.001) spirometry groups were associated with lower VO2 peak values compared with the normal spirometry group after adjustment (Table 7.4 and Figure 6.16). Notably, the obstructive spirometry group had similar adjusted VO2 peak compared with the normal spirometry group. The confidence intervals around predicted VO2 peak was wide for the mixed group, suggesting possible imprecision Adjusted association of FEV1, FEV1/FVC and spirometry category with VE/VCO2 Linear regression was also used to examine the association between spirometry-related variables and VE/VCO2. Spirometry was characterized using FEV1 and FEV1/FVC, similar to analyses pertaining to VO2 peak; and the same clinically sensible covariates were chosen for inclusion into the model. All continuous variables were expressed as 4-knot restricted cubic splines to account for potential non-linearity in their association with the outcome. The adjusted associations of FEV1, FEV1/FVC and covariates with VE/VCO2 are shown in Table 7.5. Both FEV1 (p < 0.001) and FEV1/FVC ratio (p < 0.001) were associated with VE/VCO2 after risk adjustment. The adjusted associations of FEV1 and FEV1/FVC with VE/VCO2 are graphically represented in Figure Lower FEV1 and FEV1/FVC were both associated with higher VE/VCO2, which clinically indicates diminished ventilatory efficiency. Following adjustment; the adjusted associations of both variables with VE/VCO2 was linear (p = 0.88 for FEV1 and p =

37 for FEV1/FVC). Every 1L decrease in FEV1 was associated with an increase in VE/VCO2 of 1.07 ( ); similarly, every 10% reduction in FEV1/FVC ratio was associated with an increase in VE/VCO2 of 0.62 ( ). The adjusted model of VE/VCO2 had an R 2 of 0.18, indicating that 18% of variation in VE/VCO2 was explained by the model. Of the relative contributions to R 2 in this model, age was the biggest contributor accounting for more than 4% of variability in the outcome; FEV1 and FEV1/FVC each accounted for about 1% of variation in VE/VCO2 following adjustment (Figure 6.18). The adjusted association of different spirometry categories with VE/VCO2 was also determined. The same covariates were included as the previous model. This model had an R 2 of 0.16, indicating that 16% of variation in VE/VCO2 was accounted for by this model. Model outputs revealed that spirometry categories were associated with the outcome after adjustment. Among the spirometry categories, obstructive (p < 0.001) and mixed (p = 0.007) spirometry groups were associated with higher VE/VCO2 compared with the normal spirometry group after risk adjustment (Table 7.6 and Figure 6.19). There was no difference in adjusted association of VE/VCO2 between restrictive and normal groups (p = 0.83). The confidence intervals surrounding predicted VE/VCO2 in the mixed spirometry group were wide, indicating potential imprecision Underlying model assumptions for Objective 1 The assumptions underlying the adjusted models of each of VO2 peak and VE/VCO2 were verified. These included: normality of residuals, influential outliers, homoscedasticity, and linearity of association. Outputs of residuals of adjusted models were each generated, and their distribution was plotted using histograms and Q-Q plots. Minimal deviation was observed from normality at the ends of the distributions (in both VO2 peak and VE/VCO2 models). Residuals were also plotted against fitted values for both models, and no concerning patterns emerged. Influential outliers were identified by generating Cook s D statistics for each observation. Using a threshold absolute Cook s D of 2, less than 5 observations for each model were identified as potential influential outliers; due to the small number, the decision was made to keep these observations within both models.

38 27 Straight-line relationships and homoscedasticity were verified by plotting the residuals against the predicted values for each model. No obvious pattern or relationship was observed within plots for either models. The residuals were also plotted against each predictor variables, and no relationship between residuals and each predictor variable was observed. The assumption of nonlinearity was also satisfied as every continuous variable was expressed as restricted cubic splines which allowed for modeling a flexible non-linear association with outcome. 3.2 Objective 2: Adjusted association of preoperative FEV1 and spirometry pattern with outcomes, adjusting for cardiopulmonary fitness Characteristics of the study cohort A total of 1200 patients were included in analyses pertaining to Objective 2 of this thesis (Participant flow diagram, Figure 6.4; Table 7.7). This cohort had a median age of 65 years, was comprised of 63% males and 14% of patients had a smoking history. Hypertension (54%, n= 653), diabetes mellitus (18%, n= 215) and coronary artery disease (11%, n= 135) were the most frequent comorbidities. Patients underwent a range of surgical procedures, including: 34% of the cohort underwent upper abdominal or thoracic procedures, 31% underwent urologic or gynecologic procedures, 23% underwent orthopedic procedures, 2% underwent vascular procedures, and 10% underwent other procedures. Examination of spirometry results revealed similar patterns to Objective 1, with median FEV1, FVC and FEV1/FVC and their respective interquartile ranges within normal limits. In categorizing spirometry results, 68% had a normal spirometry pattern, 16% exhibited a restrictive pattern, 13% had an obstructive pattern and a minority (3%) had a mixed obstructive-restrictive pattern. The median VO2 peak and VE/VCO2 were 18.9 ml/kg/min and 31, respectively. Eleven percent (n= 129) of the study cohort developed the primary study outcome, POMS respiratory morbidity. Fewer patients (4%, n= 48) developed the secondary outcome, PPC. Finally, 12% of the study cohort developed major cardiac complications Unadjusted associations with primary and secondary outcomes Summary statistics were generated to describe strata defined by presence or absence of the primary and secondary outcomes.

39 28 Table 7.8 presents the unadjusted association of patient factors, surgical procedure, spirometry and CPET results with the primary outcome (POMS respiratory morbidity). Patients with POMS respiratory morbidity were more likely to have undergone upper abdominal or thoracic procedures (p < 0.001) and have hypertension (p = 0.045). Among the spirometry results, POMS respiratory morbidity was associated with FEV1/FVC; however, the group with respiratory morbidity had marginally higher FEV1/FVC compared to the group without morbidity, and the absolute difference in ratio between the groups was small. Spirometry category was not associated with POMS respiratory morbidity. The POMS respiratory morbidity group had lower VO2 peak compared to patients without POMS respiratory morbidity (p = 0.001); also, patients who developed POMS respiratory morbidity had higher VE/VCO2 compared with patients who did not develop the outcome (p = 0.025). However, the absolute difference in VE/VCO2 between the groups was small. The unadjusted associations with PPC are shown in Table 7.9. PPC more frequently occurred in patients undergoing upper abdominal or thoracic procedures (p < 0.001). None of the spirometry categories or parameters were different by a statistically significant degree between PPC versus no PPC groups. Of CPET-derived parameters, only VE/VCO2 was associated with PPC, with the PPC group having a marginally higher VE/VCO2 compared to the no PPC group (p = 0.035). Finally, unadjusted associations with major cardiac complications are listed in Table Patients with major cardiac complications were more likely to be older (p < 0.001), have comorbidities including coronary artery disease (p < 0.001), cerebrovascular disease (p = 0.02), renal insufficiency (p < 0.001), and hypertension (p = 0.03), and undergo vascular or orthopedic procedures (p < 0.001). Among spirometry results, patients with major cardiac complications were more likely to have lower FEV1 (p = 0.003) and lower FEV1/FVC ratio (p < 0.001); unadjusted differences in FVC bordered on statistical significance (p = 0.058). Among the spirometry categories, major cardiac complications occurred with higher frequency in obstructive and mixed pattern groups (p = 0.03). Finally, patients with major cardiac complications had higher VE/VCO2 (p < 0.001) but the absolute difference in VE/VCO2 compared with patients without major cardiac complications was low.

40 Adjusted association of FEV1 and pattern of spirometry findings with primary outcome, POMS respiratory morbidity, accounting for VO2 peak and Ventilatory Efficiency from CPET Multivariable logistic regression modeling was used to determine the adjusted association of FEV1 with POMS respiratory morbidity, including adjustment for VO2 peak (and subsequently ventilatory efficiency) from CPET. Given the possibility of non-linearity associations of FEV1 and VO2 peak or VE/VCO2 with the probability (logit transformation) of the outcomes, the decision was made to express these parameters as 4-knot restricted cubic splines. In the first adjusted model predicting POMS respiratory morbidity, FEV1 was the primary exposure variable; VE/VCO2 was chosen as the CPET parameter of interest given modeling of a respiratory outcome; and covariates were chosen a priori based on clinical sensibility. To evaluate possible effect modification of the FEV1 outcome relationship by VE/VCO2, a statistical interaction term between FEV1 and VE/VCO2 was evaluated and was found to be not significant (p = 0.42). Table 7.11 shows the logistic regression model evaluating the adjusted association of FEV1, VE/VCO2 and covariates with POMS respiratory morbidity. Following risk adjustment, FEV1 was not associated with POMS respiratory morbidity, while VE/VCO2 (p = 0.041) was associated with the primary outcome. The adjusted associations of FEV1 and VE/VCO2 with POMS respiratory morbidity are graphically represented in Figure Increasing VE/VCO2 was associated with increasing risk of POMS respiratory morbidity, with a 5 unit increase from 25 to 30 being associated with an adjusted odds ratio of the outcome of 2.06 ( ). There was a suggestion of non-linearity in this adjusted association (p = 0.16). With respect to model discrimination, the c-statistic was The adjusted association of FEV1 with POMS respiratory morbidity was also determined using a logistic regression model where VO2 peak was included instead as the CPET parameter of interest (Table 7.12). Similarly, effect modification was examined by testing an interaction term between FEV1 and VO2 peak, which was not statistically significant (p = 0.44). Linear associations were observed between FEV1, VO2 peak and their combination with the probability (logit transformation) of POMS respiratory morbidity (p = 0.88) and both variables are expressed as untransformed continuous variables. The adjusted association of FEV1 and VO2 peak with POMS respiratory morbidity is graphically represented in Figure FEV1 was not associated

41 30 with increased odds of the outcome; however, decreasing VO2 peak was associated with increased adjusted odds of POMS respiratory morbidity (adjusted OR 0.66 ( ); p < 0.001)) for every 5-unit increase in VO2 peak). With respect to model discrimination, the c- statistic was Finally, this association was examined expressing spirometry performance in categories (Table 7.13). VO2 peak, expressed as an untransformed continuous variable given observed linear association with outcome (p = 0.72), was included as the CPET parameter of interest, and the same covariates were included as the analyses above. No significant interaction was observed between spirometry category with VO2 peak (p = 0.82). In this analysis, spirometry category had an association with POMS respiratory morbidity that bordered on statistical significance (p = 0.058). VO2 peak (p < 0.001) was associated with POMS respiratory morbidity. The adjusted association of spirometry categories with POMS respiratory morbidity are presented in Table 7.13, which shows that the restrictive spirometry group was associated with decreased odds of POMS respiratory morbidity following adjustment (aor 0.43 ( ), p = 0.011). The model c-statistic was Adjusted association of FEV1 and pattern of spirometry findings with secondary outcome, PPC, accounting for VE/VCO2 from CPET Logistic regression modeling was used to determine the adjusted association of FEV1 with the secondary outcome, PPC, while adjusting for VO2 peak or VE/VCO2 from CPET. These analyses were limited by the infrequent occurrence of PPCs. First, to evaluate the potential for a nonlinear association between FEV1 and VE/VCO2 with probability of PPC (logit transformation), these exposures were each expressed as 3-knot restricted cubic splines in unadjusted models where PPC was the outcome. There was evidence of non-linearity in the association of VE/VCO2 with the probability (logit transformation) of PPC (p = 0.06; Figure 6.22). Upon examining this association, the following pattern emerged: low VE/VCO2 (clinically indicating greater efficiency) was associated with low odds of the outcome; as VE/VCO2 increased, the odds of PPC increased linearly up to a threshold VE/VCO2 of 35, beyond which no further increases in odds of PPC were observed. Thus, for the adjusted analysis of PPC, VE/VCO2 was included as a continuous variable truncated at 35 (i.e., values above 35 were expressed as 35).

42 31 The regression model predicting PPC thus included the following predictors: FEV1, VE/VCO2, age, sex, and upper abdominal or thoracic surgery. The association of FEV1 and VE/VCO2 with the probability (logit transformation) of PPC is graphically expressed in Figure FEV1 was not associated with PPC (p = 0.47), while VE/VCO2 (p = 0.019) was associated with PPC following adjustment (Table 7.14). A 5-unit increase in VE/VCO2 was associated with an odds ratio of 1.75 ( ). The model c-statistic was Also, the adjusted association of PPC was examined expressing patterns of spirometry findings as categories. Due to potential for overspecification, this model adjusted for VE/VCO2, spirometry category, and surgical site (Table 7.15) and revealed that VE/VCO2 (p = 0.033) was associated with PPC. There was no statistically significant adjusted association of spirometry category with PPC (p = 0.52). This model had a c-statistic of Adjusted association of FEV1 and pattern of spirometry findings with secondary outcome, major cardiac complications, accounting for VO2 peak and VE/VCO2 from CPET Logistic regression modeling was used to examine the adjusted association of FEV1 with major cardiac complications, including adjustment for VO2 peak. Given the possibility of non-linearity in the association of FEV1 and VO2 peak with the probability (logit transformation) of the outcome, these exposures were expressed as 4-knot restricted cubic splines. In the first model predicting major cardiac complications, FEV1 was the primary exposure variable; VO2 peak was included as the CPET parameter of interest; and covariates were chosen a priori based on clinical sensibility. To evaluate possible effect modification of the spirometry outcome relationship by VO2 peak, an interaction term between FEV1 and VO2 peak was tested and found to be not significant (p = 0.78). The adjusted association of FEV1, VO2 peak and covariates with major cardiac complications is shown in Table After adjustment, neither FEV1 (p = 0.74), nor VO2 peak (p = 0.53), were associated with major cardiac complications, as shown in Figure Wide confidence bands surrounding the splines indicate imprecision and a lack of significant association with major cardiac complications. The model c-statistic was The above modeling exercise was repeated, including VE/VCO2 as the CPET parameter of interest, as shown in Table There was no evidence of an interaction between FEV1 and VE/VCO2 (p = 0.44), and linear associations between FEV1 and VE/VCO2 with the outcome was

43 32 observed; both FEV1 and VE/VCO2 in this model were expressed as untransformed continuous variables. Neither FEV1 (p = 0.73) nor VE/VCO2 (p = 0.10) were associated with major cardiac complications, as shown graphically in Figure The model c-statistic was Finally, I modeled major cardiac complications as the outcome, but expressed spirometry in categories and adjusted for VO2 peak. The results of this model are shown in Table Following adjustment; neither of spirometry category (p = 0.23) nor VO2 peak (p = 0.57) were associated. The adjusted association of spirometry category with risk of major cardiac complications is graphically represented in Figure 6.28, where overlapping predicted probabilities of all categories indicate no significant association with outcome. The model c- statistic was Underlying model assumptions for Objective 2 Each logistic regression model was evaluated for model fit including calibration, overspecification, and influential outliers. Following generation of each logistic regression model of interest, each model was validated by generating 100 bootstrap samples and generating calibration plots. The calibration plots were assessed, and no major deviations in predicted versus observed risk estimates were noted. The optimism-corrected c-statistic was reviewed, and appropriate calibration was noted on each of the generated models. Model overspecification was examined with each model generated; attention was heeded towards limiting adjustments with coefficients based on the number of observed events. Model outputs revealed appropriate model convergence for all models. Influential outliers were examined using casewise diagnostics, and plots of DFFITS were generated for each observation. Observations with absolute DFFITS > 0.4 were individually examined for plausibility; less than 5 observations met this criterion, and these observations were left in the models.

44 33 Chapter 4 Discussion and Conclusions 4 Discussion An examination of the relationship between commonly used spirometry measures (i.e. FEV1, FEV1/FVC) with measures of cardiopulmonary fitness from CPET (VO2 peak, ventilatory efficiency) is important to inform their utility in clinical practice. The METS study conducted both spirometry and CPET prospectively among a cohort of patients with comorbid disease undergoing inpatient noncardiac surgical procedures and at risk of complications, 38, 45 allowing for robust comparisons between these commonly used specialized investigations. Summary of findings In characterizing the association between FEV1 and FEV1/FVC from spirometry with VO2 and VE/VCO2 from CPET, linear associations were observed between FEV1 with VO2 peak, and both FEV1 and FEV1/FVC ratio with VE/VCO2. In unadjusted analyses, a positive association of moderate magnitude was observed between FEV1 and VO2 peak; and negative associations of weak statistical magnitude were observed between FEV1 and FEV1/FVC with VE/VCO2. These associations were in the expected direction, as decreasing FEV1 and FEV1/FVC were associated with diminished fitness based on decreasing VO2 peak and higher VE/VCO2 (indicating lower ventilatory efficiency). The unadjusted association of FEV1/FVC with VO2 peak was non-linear; increases in FEV1/FVC ratio beyond 0.7 were associated with lower VO2 peak. Since increasing FEV1/FVC ratio, which indicates less significant obstructive lung pathology, would not be clinically associated with lower cardiopulmonary fitness, the association of FEV1/FVC with VO2 peak was examined among categories of spirometry patterns. 31 This analysis revealed that decreases in VO2 peak with increasing FEV1/FVC ratio were only apparent in patients with normal spirometry pattern and this effect was likely driven by outliers in VO2 peak. Following adjustment for demographics and cardiopulmonary comorbidities, the shapes of association between FEV1 and FEV1/FVC with VO2 peak and VE/VCO2 were unchanged; the adjusted model of VO2 peak explained 34% of its variation, of which FEV1 explained 8%. The same predictor variables explained 16% of the variation in ventilatory efficiency, of which FEV1 and FEV1/FVC ratio each explained 1%.

45 34 In unadjusted analyses examining associations of FEV1 and FEV1/FVC from spirometry and VO2 peak and ventilatory efficiency from CPET with outcomes, some differences in these parameters existed between complication groups; however, the magnitude of these differences was small. After adjustment for VO2 peak or ventilatory efficiency and patient- and surgicalfactors, FEV1 was no longer associated with either respiratory or cardiac outcomes. The associations observed between FEV1 with VO2 peak and ventilatory efficiency, in addition to the significant adjusted associations VO2 peak and ventilatory efficiency with respiratory complications, suggests that any association of spirometry results with respiratory complications may be explained in part by confounding through cardiopulmonary fitness. Interpretation To our knowledge, this is the first large study to examine the association of spirometry parameters with CPET outputs among patients undergoing inpatient noncardiac surgery. The positive association of moderate magnitude between FEV1 and VO2 peak indicates that clinically, increasing FEV1 is correlated with greater cardiopulmonary fitness, as represented by VO2 peak. These associations persisted after risk adjustment, which highlights overlap in constructs being measured between FEV1 and VO2 peak. Though some overlap between FEV1 and VO2 peak may be expected as respiratory function captured by FEV1 conceivably contributes to cardiopulmonary fitness, these measures demonstrated the strongest correlation among the variables examined. 13, 29 The fact that both FEV1 and VO2 peak are effort-dependent measures may contribute to their association, as patients who are able to exert themselves maximally on exercise testing may also generate the large expiratory forces necessary to forcefully exhale a large volume of air. 13, 29 However, the absence of a strong association may be due to differences in the measures themselves: VO2 peak, which represents the cumulative ability of the cardiac, pulmonary and vascular systems response to aerobic stress, may be far more influenced by cardiovascular function while FEV1, a measure of the mechanical properties of the respiratory system, may be less influenced by it. 13, 29 The observed association between FEV1 and VO2 peak has previously been documented, albeit in patients with pulmonary disease such as cystic fibrosis and COPD Interestingly, correlations of larger magnitude have previously been reported between FEV1 and VO2 peak among these patients; however, this is likely explained by the larger contribution of pulmonary limitations to their cardiopulmonary fitness. Furthermore, cohorts of elderly patients have also demonstrated correlations between FEV1 with

46 35 VO2 peak, further supporting the observed associations between respiratory physiology represented by FEV1 with cardiopulmonary fitness represented by VO2 peak, 64 which this thesis extends to a cohort of surgical patients. However, the prediction of VO2 peak among these elderly patients was greatly enhanced by the addition to other parameters such as hemoglobin, suggesting that FEV1 represents the respiratory component of a multi-dimensional measure in VO2 peak. 67 The observed association between FEV1/FVC with VO2 peak was unexpected, as clinically, increasing FEV1/FVC ratio represents less severe lung obstruction, which would correlate with improving cardiopulmonary function. Expressing spirometry in categories based on patterns allowed for exploring this association further. Since normal FEV1/FVC ratio may represent either a normal pattern or a restrictive pattern with proportionate reductions in both FEV1 and FVC, and reductions in the ratio may represent either obstructive or mixed obstructive-restrictive pathology, categorization was crucial to examining the relationship between FEV1/FVC and CPET. 42 These analyses revealed that decreasing VO2 peak was with increasing FEV1/FVC was only observed in normal spirometry pattern patients, and this association was likely driven by VO2 peak outliers. Prior research has reported increasing VO2 peak with improving FEV1/FVC ratio, albeit in healthy patients. 68 This association has also been observed among patients with obstructive lung disease such as cystic fibrosis, where VO2 peak was correlated with each of 65, 66 FEV1 and FVC, and their ratio. Furthermore, categorization of spirometry results based on patterns revealed that after risk adjustment, normal and obstructive spirometry patterns were associated with higher VO2 peak compared with restrictive and mixed pattern patients. The adjusted associations of patterns of spirometry results with VO2 peak has not been previously described. The typical CPET pattern of lung disease described has involved reductions in exercise capacity (VO2 peak), preserved cardiovascular responses, mild gas exchange abnormalities, and possible ventilatory limitation to exercise. 32 The observed limitations in VO2 peak in patients with a restrictive pattern likely reflects overall reductions in exercise capacity, and the inability of the cumulative cardiocirculatory systems to extract oxygen effectively during aerobic stress. Conversely, these limitations in VO2 peak were not observed among patients with obstructive spirometry pattern following adjustment for patient factors, suggesting that the pattern of limitation on CPET may differ based on pattern of spirometry results.

47 36 The weak associations observed between FEV1 and FEV1/FVC with ventilatory efficiency were unexpected, given that these were both measures of the respiratory system. Indeed, these measures vary in that FEV1 and FEV1/FVC ratio are measures of the mechanical properties of the respiratory system, while ventilatory efficiency is a measure of ventilatory function during exercise testing. 13, 29 As spirometry measurements were obtained during rest, the weak observed associations suggest that they do not adequately capture the respiratory reserve to deal with the increasing ventilatory load during exercise. Further, it must be noted that both FEV1 and by extension, the FEV1/FVC ratio are effort-dependent, while ventilatory efficiency was calculated based on the slope of the ventilation to CO2 production at the AT, which is effort-independent. 32 However, determination of the AT has associated inter-rater variability. As the METS study protocol involved determination of AT locally at each centre, these findings may change should centralization determination of AT (and therefore ventilatory efficiency) be undertaken. 69 Notably, the association between FEV1 and FEV1/FVC with ventilatory efficiency followed the expected direction in both unadjusted and adjusted analyses, in that increases in FEV1 and FEV1/FVC ratio were associated with decreasing VE/VCO2, indicating greater efficiency. Though a weak magnitude of association was observed between the parameters, a similar underlying respiratory construct was being measured. Clinically, limitations in ventilatory efficiency are often observed in symptomatic COPD patients despite preserved FEV1; such patients demonstrate out-of-proportion breathlessness than would be predicted based on FEV1. These findings, in addition to the weak associations with FEV1 and FEV1/FVC in this thesis, suggest that ventilatory efficiency may be affected by further cardiocirculatory factors such as heart failure and pulmonary hypertension, and respiratory function captured by FEV1 and FEV1/FVC reflects a small component of this complex measure of fitness Moreover, the associations of patterns of spirometry results with ventilatory efficiency highlighted novel findings. Normal and restrictive patterns trended towards lower VE/VCO2, indicating greater ventilatory efficiency, compared with obstructive and mixed pattern patients. The finding of diminished ventilatory efficiency among patients with an obstructive pattern on spirometry is not surprising, as obstructive lung diseases such as COPD may be associated with chronic CO2 retention. 73 In addition, these patients may have limited ventilatory reserve, 74 and increasing pulmonary dead space with exercise, which would limit ventilatory efficiency. These findings highlighted that though the associations between individual parameters from spirometry

48 37 with ventilatory efficiency was relatively weak, the pattern of abnormality on spirometry revealed associations with ventilatory efficiency following risk adjustment. In addition to the association between outputs of spirometry with measures of cardiopulmonary fitness on CPET, this thesis examined associations of FEV1 and spirometry patterns with postoperative clinical outcomes, while accounting for cardiopulmonary fitness. No statistically significant interaction was observed between FEV1 and spirometry pattern with VO2 peak and VE/VCO2 from CPET, suggesting that VO2 peak or ventilatory efficiency did not modify the association between outputs of spirometry and outcomes in these patients. 62 The relevant CPET parameter of interest was treated as a potential confounder in adjusted analyses; this allowed for examination of the association between spirometry parameters with outcomes while accounting for CPET results. In unadjusted analyses, some statistical differences were observed in FEV1/FVC from spirometry and both VO2 peak and ventilatory efficiency from CPET with POMS respiratory morbidity, and in ventilatory efficiency from CPET with PPC between complication groups in unadjusted analyses. Following risk adjustment, neither FEV1 nor category of spirometry findings was associated with outcomes. Both VO2 peak and ventilatory efficiency were associated with respiratory outcomes in adjusted analyses, suggesting that any prior association observed between outputs of spirometry with outcomes may be confounded by the existing association between outputs of spirometry with markers of cardiopulmonary fitness from CPET. Though this thesis is the first study to examine the ability of cardiopulmonary fitness to confound the association between outputs of spirometry and clinical outcomes, one previous study of colorectal surgery patients showed that AT from CPET was higher among patients with cardiopulmonary complications, while no differences were evident between complication groups in terms of either FEV1 or FEV1/FVC. 44 This thesis adds novel evidence of associations between ventilatory efficiency and VO2 peak from CPET with respiratory complications after inpatient noncardiac surgery, even after adjustment for patient and surgical level risk factors. While previous research has highlighted potentially increased risk of respiratory complications among patients with restrictive lung disease, 22, 23 our study found a trend towards lower risk of POMS respiratory morbidity among patients with restrictive spirometry. While this analysis was limited by the small number of patients within certain spirometry categories, future study is

49 38 warranted to further delineate the risk profile of patients with restrictive and mixed deficits on spirometry. Also, this analysis was limited in that the traditionally used measure of lung restriction, total lung capacity, was not collected as part of the study protocol. Though assessment of lung restriction using spirometry has generally yielded moderate sensitivity and high specificity for restrictive spirometry pattern, particularly when using definitions based on the lower limits of normal, further work is warranted to confirm these findings particularly using formal PFTs with estimation of lung volumes. 50, 51 Until such study, however, the findings from this thesis provide no conflicting evidence to pre-existing guidelines that do not recommend 19, 20 spirometry for individual patient-level risk stratification for pulmonary complications. Major cardiac complications were also examined as a secondary outcome in this thesis; it was defined to include death and myocardial injury, which in prior research has shown independent associations with 30-day mortality and is gaining popularity in perioperative medicine as an important cardiac outcome. 3, 59, 60 As association between outputs of spirometry, which is a marker of the mechanical properties of the respiratory system, with cardiac complications was largely not expected due to divergence in constructs. 47 Following risk adjustment, neither findings from spirometry nor VO2 peak or ventilatory efficiency from CPET were associated with major cardiac complications. The finding of diminished associations between FEV1 and spirometry patterns with cardiac complications following risk adjustment confirms the notion that measures from spirometry are primarily reflective of a respiratory construct. 47 Though based on the first thesis objective, it may be associated with markers of cardiopulmonary fitness, following adjustment for these factors and clinical risk factors, spirometry parameters were not associated with major cardiac complications, which further highlights its limited utility as a routine test among surgical patients. Finally, neither VO2 peak or ventilatory efficiency were associated with major cardiac complications following risk adjustment, which is a surprising finding given previous finding of significant associations between CPET parameters with postoperative complications. 32, 36, 44 The finding of no adjusted association between VO2 peak and outcomes has been addressed within the main METS study. 45 This thesis confirms those findings and adds that ventilatory efficiency was also not associated with major cardiac complications following risk adjustment. However, this lack of association may be secondary to the choice of CPET parameters for different types of surgery and choice of outcomes examined. Prior research has highlighted the importance of

50 39 different CPET parameter among varying surgical procedures, for example, increased VE/VCO2 indicating poor efficiency was associated with mortality among patients undergoing repair of abdominal aortic aneurysms; ventilatory efficiency has also been associated with increased risk of PPC and mortality following lung resection surgery. In this thesis, ventilatory efficiency as a measure of the required minute ventilation to deal with increasing CO2 during exercise, was not associated with cardiac complications, perhaps due to its respiratory focus. 35, 37 It is plausible that measures from CPET including VO2 peak and ventilatory efficiency have varying predictive capability for different complications in different surgical procedures, and any effect was diluted upon inclusion of the range of surgical procedures in these analyses. Though both VO2 peak and ventilatory efficiency exhibited predictive utility with respiratory complications in this thesis, the lack of observed association with cardiac complications suggests that alternative strategies, such as cardiac-specific risk indices and biomarkers, may more accurately predict the risk of major cardiac complications following surgery. 45 Clinical implication PFTs, including spirometry, have a significant role in respiratory medicine to: 13, 47 delineate the etiology of dyspnea and cough; identify the severity of impairment in conditions such as COPD and asthma; assess for return of pulmonary function following exacerbations in conditions including COPD and cystic fibrosis; and to provide valuable information surrounding lung volume restriction and diffusing capacity which play a crucial role in the management of restrictive lung disorders including idiopathic pulmonary fibrosis. Adding to this arsenal of information obtained from PFTs, this thesis highlights that markers of spirometry (i.e. FEV1, FEV1/FVC and pattern of spirometry findings) are markers of cardiopulmonary fitness, as measured by VO2 peak and ventilatory efficiency. Among patients with recent spirometry results who have not undergone CPET, the associations observed between FEV1 and VO2 peak highlight that FEV1 may, at a basic level, also provide insight into cardiopulmonary fitness. This thesis also helped evaluate the utility of parameters from spirometry as a preoperative risk stratification modality. 14, 16 Importantly, no adjusted association of outputs of spirometry with outcomes were observed regardless of whether spirometry was modeled based on FEV1 or spirometry category. The only possible exception to this was the finding of lower risk of POMS respiratory morbidity among individuals with restrictive spirometry pattern. The finding of no

51 40 association of spirometry with respiratory outcomes when accounting for cardiopulmonary fitness is a notable addition to the literature, where inconsistent associations of outputs of spirometry with outcomes have previously been reported among patients undergoing extrathoracic surgery. 14, 16 These results suggest that CPET parameters may be associated with respiratory outcomes, and the extent to which spirometry predicted outcomes in prior studies may be due to its association with cardiopulmonary fitness, as supported by findings for the first thesis objective. These findings have important implications for clinical practice, where risk stratification for pulmonary complications must consider an overall assessment of patient fitness, which in this study was estimated by CPET. Fitness may reflect patients ability to cope with the physiological stresses of the perioperative period, which may impact postoperative recovery including propensity for developing complications. 75 Though an assessment of functional capacity has been emphasized in pre-existing guidelines for predicting major adverse cardiac events and death, 7 our findings suggest that objective measures of cardiopulmonary fitness are an important element for preoperative risk stratification for respiratory complications. Furthermore, though VO2 peak and ventilatory efficiency were associated with respiratory complications, the lack of adjusted association observed between either of these measures or outputs of spirometry with cardiac complications indicate that clinical risk stratification in the perioperative context is complication-specific. Neither measures from spirometry nor VO2 peak or ventilatory efficiency from CPET was consistently associated with each of the outcomes examined in this thesis. Thus, a patient-based approach considering clinical risk factors, the surgical procedure, and synthesizing this information with the results of specialized investigations as they are indicated, will allow for thorough clinical risk stratification of surgical patients. 6 Study Strengths Several strengths are apparent in the methodology used within this thesis. First, the robust conduct of the METS study using standardized inclusion criteria of major inpatient noncardiac surgical patients at risk for complications, the conduct of protocolized and reliable preoperative testing with spirometry and CPET, blinding of results of these investigations from treating clinicians and outcome assessors, and minimal loss of patients to follow-up all allowed for

52 41 examining the thesis objectives within a robust dataset that required minimal data manipulation 38, 45 or cleaning. Furthermore, the METS cohort reflected risks of complications that mirrored contemporary estimates of risk observed in prior inpatient noncardiac surgical cohorts. For example, POMS respiratory morbidity and major cardiac complications occurred in around 10% of the cohort, supporting findings from prior surgical cohorts of similar frequencies of postoperative pulmonary and cardiac complications. 5, 60 Though the secondary pulmonary outcome, PPC, only occurred in 4% of the cohort, it was defined as a composite of the requirement of postoperative mechanical ventilation, pneumonia, and respiratory failure requiring reintubation; each of these severe pulmonary complications occurred at low frequency in this study. However, the contemporary definition of postoperative respiratory failure has evolved to any de novo hypoxemia occurring within 5 days of surgery, 56 which is approximated by the POMS respiratory morbidity endpoint. Also, risk adjustment for postoperative complications included clinical risk factors which were chosen a priori based on clinical sensibility. The adjusted associations observed in this thesis with both respiratory and cardiac complications were in line with findings from previous research. For example, the significant adjusted association of surgical procedure (i.e. upper abdominal and/or thoracic surgery) with respiratory complications is clinically sensible as these procedures are associated with postoperative pain which impedes deep breathing and effective cough, disruption to anatomic structures supporting respiration, postoperative atelectasis and pulmonary splinting; 8, 9 each of these factors may predispose patients to postoperative respiratory impairment. Similarly, the adjusted association of age and coronary artery disease with major cardiac complications is reflective of prior research. Increasing age is a risk factor for atherosclerosis, and limitations in physiologic reserve associated with increasing age may increase the risk of postoperative cardiac events. It is an integral component of widely used surgical risk calculators, including the National Surgical Quality Improvement Program calculator. 76 Similarly, coronary artery disease is widely recognized as a strong predictor of postoperative cardiac complications; it is among the risk factors listed as part of the widely used well-validated Revised Cardiac Risk Index. 77 These findings largely point to adequate risk adjustment for the outcomes examined within this thesis.

53 42 Study Limitations This thesis was subject to certain limitations. First, the requirement to exercise as part of the METS study was likely associated with a self-selection bias, where patients with preferentially higher fitness would more likely participate in the study. This was likely compounded by the exclusion of patients with FEV1 < 30% predicted. Though this criterion was instituted in the interest of patient safety to allow for further patient work-up prior to major surgery, the cohort studied herein had exposure spirometry variables with significantly skewed distributions towards normal values and limited ranges. As such, the findings of this study may not be generalizable to the sicker spectrum of surgical patients. However, in examining the frequency of complications encountered between this cohort and other large recent cohorts of patients undergoing elective surgery, the frequency of complications noted within our study was similar. 5, 60 Despite this limitation, the inclusion of patients who completed both spirometry and CPET allowed for robust comparisons of the two testing modalities, as all patients underwent both spirometry and CPET in a reproducible manner using standardized protocols. Furthermore, the number of patients experiencing complications, our clinical study end-point, limited the choice of primary outcome for the second study objective. Respiratory morbidity, as defined based on POMS a validated measure of short-term postoperative morbidity was chosen as the primary outcome. It must be noted that POMS respiratory morbidity, defined as de novo oxygen requirement following surgery, non-specific in that it may be prescribed for various reasons. However, this choice of outcome was informed by the similar contemporary definition of postoperative respiratory failure, which POMS respiratory morbidity approximates. 54, 56 Of note, despite the choice of primary outcome, PPC was analysed as a secondary outcome, and similar associations were noted as the primary analyses. Also, there is potential for inter-rater variability in the measurements of certain study parameters. 69 For example, among CPET assessment, variability is to be expected with determination of the AT, and by extension ventilatory efficiency, which was measured based on the slope of VE/VCO2 curves at the AT. The METS study was pragmatic in that AT was measured locally at each study site to reflect usual clinical practice. Furthermore, while the conduct of spirometry was protocolized based on guidelines from the ATS with acceptable results of spirometry exhibiting minimal differences in flows and volumes between at least two

54 43 maximal efforts, this study was limited in that categorization of patterns of spirometry results had potential variability based on the reference standards chosen, definitions of impairment, and the availability of parameters. 50, 51, 52, 53 In this study, the lower limits of normal using reference equations from a commonly used reference population were chosen to define limits of FEV1, FVC and the FEV1/FVC ratio used to determine the pattern of limitation on spirometry. Using lower limits of normal is the favoured approach, as fixed cutoffs of FEV1 and FEV1/FVC can 52, 53 overestimate restrictive and obstructive pathology, particularly among older patients. Defining restrictive pathology using FVC, rather than TLC which was unavailable, is a study limitation. However, the chosen definitions have previously shown high specificity (> 95%) and acceptable sensitivity (> 70%) for restrictive lung pathology. 50, 51 Future work is needed to replicate the findings of this thesis, especially as they relate to categories of spirometry findings. Finally, the examination of spirometry, which may be expressed using multiple parameters, and two CPET outputs necessitated multiple statistical comparisons for examining the association of spirometry with CPET, and the examine the adjusted association of spirometry with outcomes while accounting for CPET. No statistical correction for multiple corrections was employed, as all analyses undertaken were planned a priori. Furthermore, the consistency in results observed despite expressing spirometry and CPET parameters differently provides support to the associations observed herein. Implications for future research This thesis examined the associations between findings from spirometry with markers of cardiopulmonary fitness on CPET and postoperative outcomes following surgery among a cohort of inpatient noncardiac surgical patients at risk of postoperative complications. I found that FEV1, FEV1/FVC and categories of spirometry patterns were associated with markers of cardiopulmonary fitness on CPET, i.e. VO2 peak and ventilatory efficiency. Furthermore, markers of cardiopulmonary fitness may have confounded the association between FEV1 and respiratory complications. This thesis was conducted among METS study patients who completed both spirometry and CPET protocols. Though these inclusion criteria produced reproducible protocolized data on both spirometry and CPET, 38, 45 the patients included within the cohort were healthy, with distributions of FEV1, FVC, FEV1/FVC and VO2 peak and VE/VCO2 centred around normal

55 44 values with limited ranges. As such, the results of this thesis reflect a healthy spectrum of surgical patients. Future work is necessary to examine whether these findings are applicable among patients with marked respiratory derangements such as COPD who may be at higher risk of complications, among whom the spirometry and CPET may be more commonly used clinically, and in whom a measure of the comparative utility of these modalities in predicting 40, 63, 72 postoperative complications requires further investigation. Similarly, expressing the results of spirometry based on categories of findings in our present cohort yielded nearly 70% of the cohort as having a normal pattern on spirometry. Though we found risk-adjusted associations between categories of spirometry patterns and both VO2 peak and ventilatory efficiency, these analyses were limited by the number of patients in each category. Further research including a patient sample with respiratory comorbidities is required to assess these associations with greater statistical power. Finally, these analyses would benefit from complete PFTs to identify restrictive lung disease based on TLC with greater sensitivity 50, 51 compared the current study. 5 Conclusions This thesis is the first large study of the relationship between spirometry outputs and CPET parameters among patients undergoing major surgery and at risk of postoperative complications. While FEV1 and FEV1/FVC from spirometry were associated with VO2 peak and ventilatory efficiency from CPET, they accounted for a small proportion of variation in these parameters. Deficits in VO2 peak and ventilatory efficiency were noted among patients with specific spirometry patterns. VO2 peak and ventilatory efficiency were potential confounders in the association between FEV1 and spirometry pattern with outcomes. While some differences were observed between complication groups in terms of FEV1 and FEV1/FVC, these associations diminished after adjustment for cardiopulmonary fitness and other patient and surgical factors. Any association of FEV1 and spirometry category with pulmonary outcomes was explained in part by confounding by cardiopulmonary fitness. Finally, FEV1 and spirometry category were not associated with cardiac outcomes after adjustment for clinical risk factors.

56 45 Broadly, the results of this thesis contextualize the inconsistent associations noted between outputs of spirometry and outcomes in prior research. Previously observed associations between spirometry and outcomes may have been partly accounted for by associations between spirometry with fitness and other patient and surgical factors. As such, findings from this thesis caution against the attribution of large risk profiles to results of singular investigations such as spirometry. Instead, a comprehensive approach that considers each patient and their characteristics, the proposed surgical procedure, and results of investigations to generate a unique risk profile for specific complications is likely warranted. Much further work is needed for thorough preoperative risk stratification, which is the imperative first step towards the goal of improving patient outcomes among high risk surgical populations.

57 46 6 Figures 6.1 Conceptual Framework for Objective 1 BMI = body mass index; CPET = cardiopulmonary exercise testing; FEV1 = forced expiratory volume in 1 second; VO2 peak = peak oxygen uptake; VE/VCO2 = ventilatory efficiency

58 Conceptual Framework for Objective 2 BMI = body mass index; CPET = cardiopulmonary exercise testing; FEV1 = forced expiratory volume in 1 second; VO2 peak = peak oxygen uptake; VE/VCO2 = ventilatory efficiency

59 6.3 Study Schematic 48

60 6.4 Study participant flow diagram 49

61 6.5 Postoperative Morbidity Survey 38 50

62 6.6 Density Plots of FEV1 and FEV1/FVC 51

63 6.7 Density Plots of VO2 peak and VE/VCO2 52

64 Unadjusted analysis of FEV1 & FEV1/FVC versus VO2 peak Spearman s ρ = 0.48 Spearman s ρ = -0.09

65 6.9 Unadjusted analysis of VO2 peak by spirometry category 54

66 6.10 Unadjusted analysis of FEV1/FVC versus VO2 peak, stratified by spirometry category 55

67 Unadjusted analysis of FEV1 & FEV1/FVC versus VE/VCO2 Spearman s ρ = Spearman s ρ = -0.23

68 6.12 Unadjusted analysis of VE/VCO2 by spirometry category 57

69 6.13 Unadjusted analysis of FEV1/FVC versus VE/VCO2, stratified by spirometry category 58

70 6.14 Adjusted association of VO2 peak versus FEV1, ratio and covariates 59

71 6.15 Contribution of predictor variables to R 2 in adjusted model of VO2 peak 60

72 6.16 Adjusted association of VO2 peak by spirometry category and covariates 61

73 6.17 Adjusted association of VE/VCO2 versus FEV1, ratio and covariates 62

74 Contribution of predictor variables to R 2 in adjusted model of VE/VCO2

75 6.19 Adjusted association of VE/VCO2 by spirometry category and covariates 64

76 6.20 Adjusted association of POMS respiratory morbidity with FEV1, VE/VCO2 and covariates 65

77 6.21 Adjusted association of POMS respiratory morbidity with FEV1, VO2 peak and covariates 66

78 6.22 Non-linear association of VE/VCO2 with PPC 67

79 6.23 Adjusted association of PPC with FEV1, truncated VE/VCO2 and covariates 68

80 6.24 Adjusted association of major cardiac complications with FEV1, VO2 peak and covariates 69

81 6.25 Adjusted association of major cardiac complications with FEV1, VE/VCO2 and covariates 70

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