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Atrial fibrillation (AF) is characterized by heterogeneity in presentation, comorbidity profile and prognosis, with different AF subphenotypes having been previously suggested. Mental health disorders are common in the AF population. The current classification of AF, based on episode duration, fails to capture the complexity of the condition. Machine learning (ML) techniques and utilization of information on mental health disorders might improve identification of different and actionable AF subphenotypes.

We utilized Nationwide UK data from the Clinical Practice Research Datalink (199 308 AF patients; age 75.4 ± 12.6; 49.2% women) and unsupervised ML for clustering (k-means). Twenty-five clinical features were used in the model, including the presence of mental health disorders (anxiety, depression, and psychosis). Outcomes were assessed at 5 years across different clusters. We identified five different clusters of AF patients with specific characteristics and behaviour. Clusters were labelled based on the most prevalent features: (i) elderly and cardiopaths; (ii) young age and mental health disease; (iii) elderly and hypertensive; (iv) middle age and depression; and (v) very elderly. Mental health disorders were present in 18% at baseline. When comparing across the different clusters, significant differences were observed for the rates of the different assessed outcomes: higher mortality, heart failure and dementia in cluster (v), cancer and anxiety or depression in cluster (iii).

Using unsupervised clustering we identified five distinct clinically actionable AF subphenotypes. The differences in outcomes and event rates at 5 years, suggests the possibility of specific tailored therapy and interventions requiring further investigation. Management of mental health should be part of holistic or integrated care management in this population.