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Gue, Y, Correa, E, Thompson, JLP, Homma, S, Qian, M and Lip, GYH (2023) Machine Learning Predicting Atrial Fibrillation as an Adverse Event in the Warfarin and Aspirin in Reduced Cardiac Ejection Fraction (WARCEF) Trial. American Journal of Medicine, 136 (11). 1099-1108.e2. ISSN 0002-9343
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Machine Learning Predicting Atrial Fibrillation as an Adverse Event in the Warfarin and Aspirin in Reduced Cardiac Ejection Fraction (WARCEF) Trial.pdf - Published Version Available under License Creative Commons Attribution. Download (3MB) | Preview |
Abstract
Background: Atrial 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. Methods: Four 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. Results: Logistic 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. Conclusion: The 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.
| Item Type: | Article |
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| Uncontrolled Keywords: | Humans; Atrial Fibrillation; Aspirin; Warfarin; Anticoagulants; Stroke Volume; Cluster Analysis; Logistic Models; Aged; Middle Aged; Female; Male; Heart Failure; Machine Learning; Atrial fibrillation; Heart failure; Machine learning; Racial disparity; Social factors; Humans; Atrial Fibrillation; Aspirin; Machine Learning; Warfarin; Male; Female; Heart Failure; Anticoagulants; Aged; Middle Aged; Cluster Analysis; Stroke Volume; Logistic Models; 11 Medical and Health Sciences; General & Internal Medicine |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science R Medicine > R Medicine (General) R Medicine > RT Nursing |
| Divisions: | Computer Science and Mathematics Nursing and Advanced Practice |
| Publisher: | Elsevier |
| SWORD Depositor: | A Symplectic |
| Date Deposited: | 15 Nov 2024 16:46 |
| Last Modified: | 15 Nov 2024 16:46 |
| DOI or ID number: | 10.1016/j.amjmed.2023.07.019 |
| URI: | https://researchonline.ljmu.ac.uk/id/eprint/24792 |
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