Enhancing Atrial Fibrillation Risk Prediction in Tobacco-Exposed Individuals: The Role of Pulmonary Function Tests,...

Introduction: COPD is associated with an increased AFib-related morbidity and mortality. There are several AFib risk prediction models available, but none have been validated in the COPD population. Our study aims to (1) identify spirometric and radiographic variables that are associated with an increased risk of AFib and (2) determine if these associated variables improve the risk discrimination of established AFib risk prediction models in individuals with COPD. Methods: We evaluated 755 participants from a single center tobacco-exposed cohort at baseline, 2-, 6-, and 10-year follow up. At each study visit, the following were performed: demographic, medical history, and symptom questionnaires, PFT, and CT imaging. We performed logistic regression analysis to determine cardiopulmonary variables associated with prevalent and incident AFib. The multivariable analysis was adjusted for sex, age, number of pack years, BMI, self-reported heart failure, and anti-hypertensive medication use. Exposure variables that were statistically significant in the logistic regression analysis were added in succession to current AFib risk prediction models, CHA ₂ DS ₂ -VASc and CHARGE-AF, to create updated models. C-statistics were calculated for both risk scores alone as well as with each updated model. Results: DLco (OR 0.37, CI 0.16-0.83), heart volume (OR 13.12, CI 2.32-74.17), percentage of emphysema (OR 2.77, CI 1.04-7.40), and mMRC (OR 1.17, CI 1.02-1.35) were associated with prevalent AFib in the multivariable logistic regression analysis. When conducting the discrimination analysis of the AFib risk prediction scores, the addition of these cardiopulmonary variables to CHA ₂ DS ₂ -VASc in incident AFib was found to provide the most profound improvement, from C-statistic 0.51 to 0.70 (p<0.001). Conclusions: We identified cardiopulmonary factors associated with an increased risk of AFib in a tobacco-exposed cohort. The incorporation of lung function, CT parameters, and symptom scores in validated AFib prediction models may improve AFib risk discrimination in our chronic lung disease populations. Trial Registration: This study was supported by the National Institute of Health (NIH) National Heart, Lung and Blood Institute (NHLBI) grants 1R01HL128289 (J.B.) and P50HL084948 (F.C.S.).