Enhancing Atrial Fibrillation Risk Prediction in Tobacco-Exposed Individuals: The Role of Pulmonary Function Tests, Symptom Scores, and Imaging

TL;DR

This study shows that adding lung function tests and imaging to existing models improves predicting atrial fibrillation risk in people with COPD.

Contribution

The study introduces new cardiopulmonary variables that enhance AFib risk prediction in tobacco-exposed individuals.

Findings

DLco, heart volume, emphysema percentage, and mMRC scores were linked to prevalent AFib.

Adding these variables to the CHA2DS2-VASc model improved AFib risk discrimination significantly.

The updated model increased the C-statistic from 0.51 to 0.70 for incident AFib.

Abstract

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.).