Clinical phenotype classification to predict risk and optimize the management of patients with atrial fibrillation using the Atrial Fibrillation Better Care (ABC) pathway: a report from the COOL-AF registry

Abstract

Background: Phenotypic classification is a method of grouping patients with similar phenotypes. Aim: We aimed to use phenotype classification based on a clustering process for risk stratification of patients with non-valvular atrial fibrillation (AF) and second, to assess the benefit of the Atrial Fibrillation Better Care (ABC) pathway. Methods: Patients with AF were prospectively enrolled from 27 hospitals in Thailand from 2014 to 2017, and followed up every 6 months for 3 years. Cluster analysis was performed from 46 variables using the hierarchical clustering using the Ward minimum variance method. Outcomes were a composite of all-cause death, ischemic stroke/systemic embolism, acute myocardial infarction and heart failure. Results: A total of 3405 patients were enrolled (mean age 67.8 ± 11.3 years, 58.2% male). During the mean follow-up of 31.8 ± 8.7 months. Three clusters were identified: Cluster 1 had the highest risk followed by Cluster 3 and Cluster 2 with a hazard ratio (HR) and 95% confidence interval (CI) of composite outcomes of 2.78 (2.25, 3.43), P < 0.001 for Cluster 1 and 1.99 (1.63, 2.42), P < 0.001 for Cluster 3 compared with Cluster 2. Management according to the ABC pathway was associated with reductions in adverse clinical outcomes especially those who belonged to Clusters 1 and 3 with HR and 95%CI of the composite outcome of 0.54 (0.40, 073), P < 0.001 for Cluster 1 and 0.49 (0.38, 0.63), P < 0.001 for Cluster 3. Conclusion: Phenotypic classification helps in risk stratification and prognostication. Compliance with the ABC pathway was associated with improved clinical outcomes.