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1 1 Supplementary Online Content Friedman DJ, Piccini JP, Wang T, et al. Association between left atrial appendage occlusion and readmission for thromboembolism among patients with atrial fibrillation undergoing concomitant cardiac surgery. JAMA. doi: /jama eappendix. Statistical Supplement etable 1. Cramer Phi and Descriptive Statistics Describing Balance Among the IPW Cohorts for the Primary Analyses etable 2. Association Between S-LAAO vs No S-LAAO and Falsification End Points in the Unadjusted and IPW Cohorts etable 3. Multivariate Adjusted Association Between S-LAAO and Outcomes Among Overall Population and After Stratification by Discharge Anticoagulation Status etable 4. Adjusted 3-Year Rates of the Study Outcomes With Adjusted Hazard and Subdistribution Hazard Ratios etable 5. Proportion of Eligible Patients Receiving Standard of Care Treatments by Hospitals With 50% vs <50% S-LAAO Use, for Hospitals Contributing 5 or More Patients to the Analysis etable 6. Cramer Phi and Standardized Difference Statistics Describing Balance Among the Discharge Anticoagulation Status IPW Cohorts for the Secondary Analyses This supplementary material has been provided by the authors to give readers additional information about their work.

2 2 eappendix. Statistical Supplement Descriptive Statistics Baseline characteristics of the study population by treatment group (S-LAAO versus no S-LAAO) were described using proportions for categorical variables and means with standard deviations for continuous variables. These tables were generated using available data with imputation. Differences between groups were tested using the Chisquare test for categorical variables. Continuous variables with a non-normal distribution were analyzed using the Wilcoxon test. Normally distributed continuous variables were analyzed using a two sample t-test and equality of variance was considered. The comparison of baseline characteristics by treatment group (S-LAAO versus no S- LAAO) are depicted in Tables 1 and 2. A similar approach was taken when creating the Supplemental Table 4, proportion of eligible patients receiving standard of care treatments by hospitals with 50% vs. <50% S-LAAO use. Unadjusted Event Rates The Kaplan Meier method was used to calculate the unadjusted event rates for the endpoints of all-cause mortality and the composite endpoint (thromboembolism, hemorrhagic stroke, and death). The cumulative incidence function was used to calculate the unadjusted event rate for the endpoints of thromboembolism, hemorrhagic stroke, and the falsification endpoints (lower extremity fracture and pneumonia) in order to account for the competing risk of death which is high in this population. The unadjusted rates (calculated as per above) were plotted as a cumulative hazard function in Figure 1. Differences in unadjusted outcomes by treatment (S-LAAO versus no S-LAAO) were assessed using Cox Proportional Hazards models for the all-cause mortality and composite endpoints. Differences in unadjusted outcomes by treatment (S-LAAO versus no S-LAAO) were assessed using Fine-Gray models for the outcomes of thromboembolism, hemorrhagic stroke, and the falsification endpoints (lower extremity fracture and pneumonia); Fine-gray models were selected to account for the competing risk of death which is high In this population. The approach described in this section was used consistently for all unadjusted analyses. Risk-adjusted Associations for the Primary Analyses To assess the risk adjusted association between treatment (S-LAAO vs. no S-LAAO) and outcomes, inverse probability weighted (IPW) adjustment was used for the primary analysis of the overall population (n=10,524). A non-parsimonious logistic regression analysis was performed to derive a propensity score for each patient, predicting likelihood of undergoing S-LAAO at the time of cardiac surgery. The following variables were used to generate a propensity score for the primary analysis: age, sex, race, AF subtype, current smoking, BMI, EF, CHF, prior stroke, hypertension, hyperlipidemia, diabetes, coronary artery disease, acute coronary syndrome prior to operation, glomerular filtration rate, lung disease, obstructive sleep apnea, CHA 2 DS 2 -VASc Score, STS Risk Score, warfarin use within the 24 hours prior to surgery, academic hospital status, CABG volume, valve surgery volume, geographic region, operation type, mechanical valve, operation status, non-sternotomy surgical approach, number of diseased vessels, and presence of left main coronary artery disease. These variables represented a comprehensive list of variables that are likely to be associated with treatment decisions and outcomes, including the decision to use or non-use S-LAAO. The adjusted population was weighted based on the inverse of the propensity score. In order to minimize the potential for extreme propensity score values from unduly influencing or biasing the study result, the propensity distributions for each subpopulation (S-LAAO and no S-LAAO populations) were stabilized. Cramer Phi statistics or standardized differences as appropriate were used to assess for differences in observed characteristics in the S-LAAO and no S-LAAO subpopulations after inverse probability weighting. The Cramer Phi test was used to due to the number of multilevel categorical variables. Small differences in the Cramer Phi statistic or the standardized difference (<10%) suggest balance across the measured baseline covariates among patients in the S-LAAO and no S-LAAO groups. Imbalance in unmeasured characteristics in observational research has the potential to lead to substantially bias results. To assess for the possibility of residual confounding, falsification endpoints that were hypothesized to be associated with frailty were utilized: lower extremity fracture and pneumonia. A null association between S-LAAO and these endpoints, which should not be affected by S-LAAO, would suggest that residual confounding is less likely to influence the study results. Falsification testing was performed using unadjusted and IPW adjusted Fine-Gray models (Supplemental Table 2) to account for the competing risk of death. The variable for likelihood of receipt (vs. non-receipt) of S-LAAO (i.e. propensity score)

3 3 was the only variable entered into the model for the adjusted analyses. Specifically, the variables used to generate the aforementioned propensity score were not added to the model. The risk adjusted association between S-LAAO (vs. no S-LAAO) and the study outcomes were tested using Fine Gray models (for thromboembolism and hemorrhagic stroke) and Cox Proportional Hazards models (all-cause mortality, composite endpoint) as appropriate. The variable for likelihood of receipt (vs. non-receipt) of S-LAAO (i.e. propensity score) was the only variable entered into the model for the adjusted analyses. Specifically, the variables used to generate the aforementioned propensity score were not added to the model. The Markov Chain Monte Carlo method was utilized for imputation. Model results represent a pooled estimate generated by combing the results obtained by running each model in 10 independent imputed datasets. Pre-specified Secondary Analyses The association between S-LAAO and outcomes was assessed in a series of pre-specified secondary analyses after stratification based on whether a patient was discharged with a prescription for oral anticoagulation. The unadjusted incidence rates and statistical comparisons were calculated as described in the section Unadjusted Event Rates. The risk adjusted associations between S-LAAO and outcomes in the subpopulations defined by discharge anticoagulation status were determined using IPW. Within each subpopulation (defined by discharge anticoagulation strategy), a logistic regression analysis was performed to generate a propensity score depicting likelihood of receipt of S-LAAO. Similar to the primary analyses, a non-parsimonious approach was taken; all of the variables used to generate the propensity score for the primary analysis (listed above) plus surgical ablation were used to generate the propensity score for these secondary analyses. These propensity scores were used to generate two separate de novo IPW cohorts using the same methodology described in the section titled, Risk Adjusted Associations for the Primary Analyses." The Cramer Phi and standardized differences statistics were used to assess for covariate balance in the two independent IPW populations. As above, the risk adjusted association between S-LAAO (vs. no S-LAAO) and the study outcomes were tested using Fine Gray models (for thromboembolism and hemorrhagic stroke) and Cox Proportional Hazards models (allcause mortality, composite endpoint) as appropriate. The variable for likelihood of receipt (vs. non-receipt) of S- LAAO (i.e. propensity score) was the only variable entered into the model for the adjusted analyses. The imputation method was the same as for the primary analyses. Sensitivity Analyses In order to lessen the chance that the results of the primary and secondary analyses were spurious or unduly influenced by bias, these results were duplicated using classic multivariable regression and Fine-Gray or Cox Proportional Hazards models. As per above, Fine-Gray models were used for the endpoints that did not include death (thromboembolism and hemorrhagic stroke) and Cox Proportional Hazards models were used for all-cause mortality and the composite endpoint. All analyses were fully adjusted by forcing variables into the model (i.e. selection was not used). When replicating the primary analyses, adjustment was performed using the following variables: age, sex, race, AF subtype, current smoking, BMI, EF, CHF, prior stroke, hypertension, hyperlipidemia, diabetes, coronary artery disease, acute coronary syndrome prior to operation, glomerular filtration rate, lung disease, obstructive sleep apnea, CHA 2 DS 2 - VASc Score, STS Risk Score, warfarin use within the 24 hours prior to surgery, academic hospital status, CABG volume, valve surgery volume, geographic region, operation type, mechanical valve, operation status, nonsternotomy surgical approach, number of diseased vessels, and presence of left main coronary artery disease. When replicating the secondary analyses (stratified by discharge anticoagulation status), all of these variables, plus surgical ablation, were used. The interaction between S-LAAO, discharge anticoagulation status, and outcomes were tested in a fully adjusted Fine Gray model that included the preceding variables and an interaction term. Exploratory Analyses 1. The association between discharge anticoagulation strategy and thromboembolism among patients who underwent S-LAAO was tested. Unadjusted and fully adjusted Fine-Gray models were used to detect differences in thromboembolism by discharge anticoagulation strategy. The following variables were used

4 4 for adjustment: age, sex, race, AF subtype, current smoking, BMI, EF, CHF, prior stroke, hypertension, hyperlipidemia, diabetes, coronary artery disease, acute coronary syndrome prior to operation, glomerular filtration rate, lung disease, obstructive sleep apnea, CHA 2 DS 2 -VASc Score, STS Risk Score, warfarin use within the 24 hours prior to surgery, academic hospital status, CABG volume, valve surgery volume, geographic region, operation type, mechanical valve, operation status, non-sternotomy surgical approach, number of diseased vessels, and presence of left main coronary artery disease. 2. The potential for an interaction between S-LAAO, surgical ablation, and thromboembolism was assed using fully adjusted Fine-Gray models that included an interaction term for the interaction between S- LAAO and surgical ablation. The entire study population was used for this analysis. The following variables were used for adjustment: age, sex, race, AF subtype, current smoking, BMI, EF, CHF, prior stroke, hypertension, hyperlipidemia, diabetes, coronary artery disease, acute coronary syndrome prior to operation, glomerular filtration rate, lung disease, obstructive sleep apnea, CHA 2 DS 2 -VASc Score, STS Risk Score, warfarin use within the 24 hours prior to surgery, academic hospital status, CABG volume, valve surgery volume, geographic region, operation type, mechanical valve, operation status, nonsternotomy surgical approach, number of diseased vessels, and presence of left main coronary artery disease. 3. The potential for an interaction between S-LAAO, AF subtype (paroxysmal vs. non-paroxysmal) and thromboembolism was assed using fully adjusted Fine-Gray models that included an interaction term for the interaction between S-LAAO and AF subtype. The entire study population was used for this analysis. The following variables were used for adjustment: age, sex, race, AF subtype, current smoking, BMI, EF, CHF, prior stroke, hypertension, hyperlipidemia, diabetes, coronary artery disease, acute coronary syndrome prior to operation, glomerular filtration rate, lung disease, obstructive sleep apnea, CHA 2 DS 2 - VASc Score, STS Risk Score, warfarin use within the 24 hours prior to surgery, academic hospital status, CABG volume, valve surgery volume, geographic region, operation type, mechanical valve, operation status, non-sternotomy surgical approach, number of diseased vessels, and presence of left main coronary artery disease.

5 5 etable 1. Cramer Phi and Descriptive Statistics Describing Balance Among the IPW Cohorts for the Primary Analyses Patient Characteristic S-LAAO No S-LAAO Cramer Phi a Age (years) 75.8(6.1) 75.8(6.4) Female sex, % Race/ethnicity White, % Black, % Hispanic, % Other, % Paroxysmal AF, % Current smoking, % BMI, kg/m < EF, % < CHF, % Prior stroke, % Hypertension, % Hyperlipidemia, % Diabetes No diabetes Non-insulin, % Insulin, % Coronary artery disease, % Number of diseased vessels None, % One, % Two, % Three, % Left main coronary artery disease, % Acute coronary syndrome prior to operation, % GFR, ml/min/1.73 m , % , % , % <15 including dialysis, % Lung disease, % Obstructive sleep apnea, % CHA 2 DS 2 -VASc Score b 4.0(1.4) 4.0(1.4) STS risk score c <5% % % Pre-operative warfarin d, % Hospital characteristics

6 6 Academic hospital, % Annual hospital CABG volume <200, % , % , % Annual hospital valve surgery volume <50, % , % , % Geographic region West South Central, % South Atlantic, % Plains, % Pacific, % New England, % Mountain, % Mid-Atlantic, % Great Lakes, % East South Central, % Procedural characteristics Operation type Isolated CABG, % CABG and MV replacement, % Isolated MV replacement,% CABG and MV repair, % Isolated MV repair, % Isolated AV procedure, % CABG and AV procedure,% Other aortic valve/aorta surgery with or without CABG, % Mechanical valve, % Operation status urgent, % Surgical atrial ablation, % Non-sternotomy surgical approach, % Abbreviations: AV, aortic valve; BMI, body mass index; CABG, coronary artery bypass grafting; CHF, congestive heart failure; EF, ejection fraction; GFR, glomerular filtration rate; MV, mitral valve; S-LAAO, surgical left atrial appendage occlusion; STS, Society of Thoracic Surgeons a The Cramer Phi statistic assesses for clinically significant differences between groups and may be more appropriate that standardized differences when there are multilevel categorical variables, which was frequently the case with this analysis. An absolute value of <0.10 suggests no clinically significant difference between groups. By this assessment, there were no clinically significant differences in adjustment variables between the S-LAAO and no S-LAAO groups in the primary IPW model. b The CHA 2DS 2-VASc (Congestive heart failure; Hypertension; Age 75 years or older; Diabetes mellitus; Stroke, TIA, or thromboembolism; Vascular disease; Age 65 to 74 years; Sex Category [female]) score is a 0 to 9 point risk score where a higher point score indicates higher risk for stroke. The point score is calculated as follows: 1 point each for congestive heart failure, hypertension, diabetes, peripheral arterial disease, age years, female sex, and 2 points for age 75+ years and prior stroke or TIA. c The STS risk score is a validated operation specific risk score that uses 35 baseline variables to predict the risk of perioperative mortality. d Dosed within 24 hours of surgery

7 7 etable 2. Association Between S-LAAO vs No S-LAAO and Falsification End Points in the Unadjusted and IPW Cohorts Unadjusted IPW-adjusted Outcome Rate a shr (CI) p- value shr (CI) p- value Lower extremity fracture 2.3% (87) vs. 2.6% (168) 0.88 ( ) ( ) 0.72 Pneumonia 6.5% (243) vs. 6.8% (448) 0.95 ( ) ( ) 0.29 Abbreviations: CI, confidence interval; IPW, inverse probability-weighted; shr, subdistribution hazard ratio; slaao, surgical left atrial appendage occlusion. a Rate (%) of outcome at 3 years using cumulative incidence function for S-LAAO vs. no S-LAAO groups, respectively. Numbers in parentheses reflects number of patients who experienced the endpoint by 3 years.

8 8 etable 3. Multivariate Adjusted Association Between S-LAAO and Outcomes Among Overall Population and After Stratification by Discharge Anticoagulation Status Outcome Overall Population a Discharged with Anticoagulation b Discharged without Anticoagulation b (HR, CI, p-value) (HR, CI, p-value) (HR, CI, p-value) Thromboembolism 0.72 ( ) p< ( ) p= ( ) p=.006 Hemorrhagic stroke 0.84 ( ) p= ( ) p= ( ) p=.83 Death 0.87 ( ) p= ( ) p= ( ) p=.26 Composite c 0.83 ( ) p< ( ) p= ( ) p=.90 Abbreviations: CI, confidence interval; HR, hazard ratio; slaao, surgical left atrial appendage occlusion a Fully adjusted model using multivariate Cox proportional hazard or Fine Gray models as appropriate. Models were adjusted for: age, sex, race, AF subtype, current smoking, BMI, EF, CHF, prior stroke, hypertension, hyperlipidemia, diabetes, coronary artery disease, acute coronary syndrome prior to operation, glomerular filtration rate, lung disease, obstructive sleep apnea, CHA 2DS 2-VASc Score, STS Risk Score, warfarin use within the 24 hours prior to surgery, academic hospital status, CABG volume, valve surgery volume, geographic region, operation type, mechanical valve, operation status, non-sternotomy surgical approach, number of diseased vessels, and presence of left main coronary artery disease b Adjusted for all of the aforementioned variables and surgical ablation c Readmission for thromboembolism, hemorrhagic stroke, or death.

9 9 etable 4. Adjusted 3-Year Rates of the Study Outcomes With Adjusted Hazard and Subdistribution Hazard Ratios Outcome S-LAAO a No HR/ p-value S-LAAO a shr (CI) Readmission for thromboembolism b,c 4.1% 6.0% 0.67 <.001 ( ) Readmission for hemorrhagic stroke c 0.8% 0.9% ( ) Death d 19.7% 22.3% ( ) Composite d,e 23.0% 27.2% 0.83 ( ) <.001 Abbreviations: HR, hazard ratio; shr, subdistribution hazard ratio; All other abbreviations can be found in Tables 1 and 2. a Rate (%) of outcome at 3 years b Thromboembolic stroke, TIA, or systemic embolism c Point estimate reflects a shr d Point estimate reflects a HR e Thromboembolism, hemorrhagic stroke, or death

10 10 etable 5. Proportion of Eligible Patients Receiving Standard of Care Treatments by Hospitals With 50% vs <50% S-LAAO Use, for Hospitals Contributing 5 or More Patients to the Analysis All Eligible Patients (N=9517) Discharge anticoagulation for patients with 4918/7689 CHADS2 2 a (63.7%) 50% S- LAAO Use (N=3168) 1587/2530 (62.7%) <50% S- LAAO Use (N=6349) 3331/5159 (64.6%) p-value 0.11 Patients undergoing isolated CABG 4833/5566 discharged on a beta blocker b (86.8%) 1571/1797 (87.4%) 3262/3769 (86.6%) 0.37 Use of internal mammary artery among CABG patients with left anterior descending or left main artery disease c 4594/4654 (98.7%) Lipid-lowering for patients with CABG d 5057/5566 (90.9%) 1470/1492 (98.5%) 1630/1797 (90.7%) 3124/3162 (98.8%) 3427/3769 (90.93%) a Excludes patients with re-op for bleeding, tamponade that did not require surgical intervention, and mechanical valve. CHADS 2 was used rather than CHA 2DS 2-VASc to reflect the risk score typically used during the study enrollment period. Includes patients with CABG, mitral surgery ± CABG, and aortic surgery ± CABG b Excludes patients where beta blockers are reported to be contraindicated c For patients with CABG (±valve surgery) after excluding patients with acceptable reason for not using IMA (per STS guidelines). d Excludes patients with reported contraindication to lipid lowering therapy

11 11 etable 6. Cramer Phi and Standardized Difference Statistics Describing Balance Among the Discharge Anticoagulation Status IPW Cohorts for the Secondary Analyses Patient Characteristic Anticoagulation No Anticoagulation Age (years) Female sex Race Paroxysmal AF Current smoking BMI EF CHF Prior stroke Hypertension Hyperlipidemia Diabetes Coronary artery disease Number of diseased vessels Left main disease Acute coronary syndrome prior to operation GFR Lung disease Obstructive sleep apnea CHA 2 DS 2 -VASc score STS risk score Pre-operative warfarin a Academic vs. non-academic hospital Hospital CABG volume Hospital valve surgery volume Geographic region Primary surgical procedure Mechanical valve N/A Elective vs. urgent Non-sternotomy surgical approach Surgical atrial ablation Abbreviations: AF, atrial fibrillation; BMI, body mass index; CABG, coronary artery bypass grafting; CHA 2DS 2- VASc, Congestive heart failure, Hypertension, Age 75 years or older, Diabetes mellitus, Stroke, TIA, or TE, Vascular disease, Age 65 to 74 years, Sex Category; CHF, congestive heart failure; EF, ejection fraction; GFR, glomerular filtration rate; IPW, inverse probability-weighted a Dosed within 24 hours of surgery Supplemental Table 2 demonstrates the covariate balance (assessed by the Cramer Phi and standardized difference statistics) among the S-LAAO and no S-LAAO subgroups in the IPW models that are stratified by discharge anticoagulation strategy. The IPW population for those discharged with and without oral anticoagulation demonstrated balance among the S-LAAO and non S-LAAO groups, except for differences in age, region, and sternotomy rates among those discharged without anticoagulation.

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