MACHINE LEARNING PREDICTING ATRIAL FIBRILLATION AS AN ADVERSE EVENT IN THE WARFARIN VERSUS ASPIRIN IN REDUCED CARDIAC EJECTION FRACTION (WARCEF) TRIAL (Preprint)

<sec> <title>UNSTRUCTURED</title> Introduction Atrial fibrillation (AF) and heart failure (HF) commonly coexist due to shared pathophysiological mechanisms. Prompt identification of patients with HF at risk of developing AF would allow clinicians the opportunity to implement appropriate monitoring strategy and timely treatment, reducing the impact of AF on patient’s health. Methods Four machine learning (ML) models combined with logistic regression and cluster analysis were applied to patient level data from Warfarin and Aspirin in Patients with Heart Failure and Sinus Rhythm (WARCEF) study to identify factors which predict development of AF in patients with HF. Out of the (n = 2219) patients included for analysis, (n = 215, 9.7%) presented AF as an adjudicated adverse event during the 6 years (mean [±SD], 3.5±1.8) follow-up period. Results Logistic regression applied to patients of a white racial ethnicity shows that white divorced patients have a 1.75-fold higher risk of AF than white patients reporting other marital statuses (95% CI 1.19–2.57, p-value = 0.002). By contrast, similar analysis for the non-white racial ethnicity patients suggests that non-white patients who live alone have a 2.58-fold higher risk of AF than those not living alone (95% CI 1.45–4.59, p-value &lt; 0.001). ML analysis also identified “marital status” and “live alone” as relevant predictors of AF. Apart from previously well-recognised factors (e.g., age, heart failure), the ML algorithms and cluster analysis identified 2 clearly distinct clusters, namely white and non-white ethnicities. This should serve as reminder of the impact of social factors on health. The findings indicate the need to explore the impact of social factors on the under-represented non-white ethnicity group of patients and the potential impact that social interventions, or the lack thereof, have on them. Conclusion The use of ML can prove useful in identifying novel cardiac risk factors. Our analysis has highlighted that “social factors”, such as living alone, may disproportionately increase the risk of AF in the under-represented non-white patient group with HF. The study also highlights the need for more studies focusing on stratification of multiracial cohorts to better uncover the heterogeneity of AF mechanisms across different racial groups. </sec>