Abstract 18371: Evaluation of the Atrial Fibrillation Better Care Pathway in Patients With Atrial Fibrillation: A Machine Learning Cluster Analysis

Abstract

Background: Atrial fibrillation Better Care (ABC) pathway is recommended by guidelines on atrial fibrillation (AF) and exerts a protective role against adverse outcomes of AF patients. We hypothesize that cluster analysis, an unsupervised machine learning technique, could comprehensively evaluate multiple clinical characteristics of patients, and define clusters of ABC criteria efficacy in patients with AF. Methods: We used data from an observational cohort that included 2,016 patients with AF. We utilized 46 baseline variables for cluster analysis and got the optimal clusters through the K-prototypes algorithm. We evaluated the management patterns and adverse outcomes of identified phenotypes. We assessed the effectiveness of the ABC criteria in reducing adverse outcomes of these phenotypes. Results: Cluster analysis identified AF patients into three distinct groups with markedly different clinical characteristics and outcomes. The clusters were as followed: Cluster 1, old patients with atherosclerotic-comorbidities (n = 964); Cluster 2, young females with valve-comorbidities (n = 407), and Cluster 3, paroxysmal AF patients with low comorbidities (n = 644). The clusters showed significant differences in MACNE, all-cause death, stroke, cardiovascular death, and hospital readmission for heart failure. All clusters showed that full adherence to the ABC pathway was associated with a significant reduction in the risk of MACNE (all P< 0.05). Adherence to the different ‘A’/’B’/’C’ criteria alone showed differential clinic impact for three clusters. Conclusion: Cluster analysis of the Chinese AF cohort further elucidated the heterogeneity of AF. We proposed suggestions for optimizing risk stratification and integrated management of AF patients.