| アブストラクト | AIMS: 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. METHODS AND RESULTS: 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). CONCLUSION: 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. |
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| 投稿者 | Zhang, Zengqi; Yoshimura, Hiroyuki; Acosta-Mena, Dionisio; Teixeira, Carina; Finan, Chris; Lip, Gregory Y H; Schmidt, A Floriaan; Providencia, Rui |
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| 組織名 | Institute of Health Informatics Research, University College London, 222 Euston;Road, London NW1 2DA, UK.;Cegedim Rx Ltd, Second Floor, Building 2, Buckshaw Station Approach, Buckshaw;Village, Chorley PR7 7NR, UK.;Institute of Psychiatry, Psychology and Neuroscience, Kings College London,;London, UK.;Institute of Cardiovascular Science, University College London, London, UK.;Liverpool Centre for Cardiovascular Science at University of Liverpool, Liverpool;John Moores University and Liverpool Heart and Chest Hospital, Liverpool, UK.;Danish Center for Health Services Research, Department of Clinical Medicine,;Aalborg University, Aalborg, Denmark.;Department of Cardiology, Lipidology and Internal Medicine with Intensive;Coronary Care Unit, Medical University of Bialystok, Bialystok, Poland.;Barts Heart Centre, Barts Health NHS Trust, St. Bartholomews Hospital, West;Smithfield, London EC1A 7BE, UK. |
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