Evaluation of the ABC pathway in patients with atrial fibrillation: A machine learning cluster analysis

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. But the possible differences in its effectiveness across the diverse range of patients in China have not been systematically evaluated. We aim to comprehensively evaluate multiple clinical characteristics of patients, and probe 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 45 baseline variables for cluster analysis. We evaluated the management patterns and adverse outcomes of identified phenotypes. We assessed the effectiveness of adherence to the ABC criteria at reducing adverse outcomes of phenotypes. Results: Cluster analysis identified AF patients into three distinct groups. The clusters include Cluster 1: old patients with the highest prevalence rates of atherosclerotic and/or other comorbidities (n = 964), Cluster 2: valve-comorbidities AF in young females (n = 407), and Cluster 3: low comorbidity patients with paroxysmal AF (n = 644). The clusters showed significant differences in MACNE, all-cause death, stroke, and cardiovascular death. 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). For three clusters, adherence to the different 'A'/'B'/'C' criterion alone showed differential clinic impact. Conclusion: Our study suggested specific optimization strategies of risk stratification and integrated management for different groups of AF patients considering multiple clinical, genetic and socioeconomic factors.