• Ying Gue, Elon Correa, ThompsonJohn L PJLPColumbia University Medical Center, New York, NY., Shunichi Homma, Min Qian, and LipGregory Y HGYHLiverpool Centre for Cardiovascular Science at University of Liverpool, Liverpool John Moores University and Liverpool Heart & Chest Hospital, United Kingdom; The Department of Cardiovascular and Metabolic Medicine, University of Liverpoo.
• Liverpool Centre for Cardiovascular Science at University of Liverpool, Liverpool John Moores University and Liverpool Heart & Chest Hospital, United Kingdom; The Department of Cardiovascular and Metabolic Medicine, University of Liverpool, United Kingdom.
• Am. J. Med. 2023 Nov 1; 136 (11): 10991108.e21099-1108.e2.

BackgroundAtrial fibrillation and heart failure commonly coexist due to shared pathophysiological mechanisms. Prompt identification of patients with heart failure at risk of developing atrial fibrillation would allow clinicians the opportunity to implement appropriate monitoring strategy and timely treatment, reducing the impact of atrial fibrillation on patients' health.MethodsFour machine learning models combined with logistic regression and cluster analysis were applied post hoc to patient-level data from the Warfarin and Aspirin in Patients with Heart Failure and Sinus Rhythm (WARCEF) trial to identify factors that predict development of atrial fibrillation in patients with heart failure.ResultsLogistic regression showed that White divorced patients have a 1.75-fold higher risk of atrial fibrillation than White patients reporting other marital statuses. By contrast, similar analysis suggests that non-White patients who live alone have a 2.58-fold higher risk than those not living alone. Machine learning analysis also identified "marital status" and "live alone" as relevant predictors of atrial fibrillation. Apart from previously well-recognized factors, the machine learning algorithms and cluster analysis identified 2 distinct clusters, namely White and non-White ethnicities. This should serve as a reminder of the impact of social factors on health.ConclusionThe use of machine learning can prove useful in identifying novel cardiac risk factors. Our analysis has shown that "social factors," such as living alone, may disproportionately increase the risk of atrial fibrillation in the under-represented non-White patient group with heart failure, highlighting the need for more studies focusing on stratification of multiracial cohorts to better uncover the heterogeneity of atrial fibrillation.Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.

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