Zhang Zengqi, Yoshimura Hiroyuki, Acosta-Mena Dionisio, Teixeira Carina, Finan Chris, Lip Gregory Y H, Schmidt A Floriaan, Providencia Rui
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.
Eur Heart J Digit Health. 2025 Apr 5;6(4):797-810. doi: 10.1093/ehjdh/ztaf032. eCollection 2025 Jul.
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.
心房颤动（AF）在临床表现、合并症情况和预后方面具有异质性，此前已提出不同的AF亚表型。心理健康障碍在AF人群中很常见。目前基于发作持续时间的AF分类未能体现该病症的复杂性。机器学习（ML）技术以及对心理健康障碍信息的利用可能会改善对不同且可采取行动的AF亚表型的识别。
我们使用了来自英国临床实践研究数据链的全国性数据（199308例AF患者；年龄75.4±12.6岁；49.2%为女性），并采用无监督ML进行聚类（k均值法）。模型中使用了25种临床特征，包括心理健康障碍（焦虑、抑郁和精神病）的存在情况。在5年时间里对不同聚类的结果进行了评估。我们识别出了具有特定特征和行为的五组不同的AF患者聚类。根据最普遍的特征对聚类进行了标记：（i）老年心脏病患者；（ii）年轻且患有心理健康疾病者；（iii）老年高血压患者；（iv）中年抑郁症患者；以及（v）高龄患者。基线时18%的患者存在心理健康障碍。在比较不同聚类时，观察到不同评估结果的发生率存在显著差异：聚类（v）的死亡率、心力衰竭和痴呆率较高，聚类（iii）的癌症、焦虑或抑郁率较高。
通过无监督聚类，我们识别出了五种不同的、具有临床可操作性的AF亚表型。5年时结果和事件发生率的差异表明，可能需要进一步研究特定的针对性治疗和干预措施。心理健康管理应成为该人群整体或综合护理管理的一部分。
