Outcome and life expectancy associated with novel subgroups of atrial fibrillation: a data-driven cluster analysis

Aim This study aimed to develop a refined classification of atrial fibrillation (AF) according to comprehensive risk factors to identify patients at high-risk for poor prognosis and their life expectancy. Method A total of 7,391 participants aged 40-69 with AF at baseline, from UK Biobank, were classified into five clusters, based on seven clustering variables including age and six risk factor categories (metabolic disease, respiratory disease, cardiovascular disease, renal/immune-mediated disease, mental health disease, acute illness). Difference in the risk of death and major complications, as well as reductions in life expectancy, among clusters were estimated. Replication was done in 2,399 participants with newly diagnosed AF within two years after baseline. Results Five distinct AF clusters were identified: acute illness-related, mental health-related, cardiovascular disease-related, immune-and-renal disease-related, and respiratory-and- metabolic disease-related AF. Patients with respiratory-and-metabolic disease-related AF had the highest risk of death, acute myocardial infarction, heart failure, and cerebral ischemic stroke, while those with acute illness-related AF had the lowest corresponding risk. In addition, compared with individuals with acute illness-related AF, those with respiratory-and- metabolic disease-related and mental health-related AF had the top 2 greatest loss of life expectancy. Furthermore, genetic variants for AF had different effect among the five clusters. Replication analysis confirmed the result stability. Conclusion A novel AF classification was developed, which provided insights into varying life expectancy and risks of death and complications among AF subgroups with distinct characteristics. It offers a practical approach for identifying high-risk patients, which might help to tailor precise interventions for AF management.