Jiang Chao, Li Mingxiao, Hu Yiying, Du Xin, Li Xiang, He Liu, Lai Yiwei, Chen Tiange, Li Yingxue, Guo Xueyuan, Jiang Chenxi, Tang Ribo, Sang Caihua, Long Deyong, Xie Guotong, Dong Jianzeng, Ma Changsheng
Department of Cardiology, Beijing Anzhen Hospital, Capital Medical University, National Clinical Research Center for Cardiovascular Diseases, Beijing, China.
Ping An Health Technology, Beijing, China.
Pacing Clin Electrophysiol. 2023 Oct;46(10):1203-1211. doi: 10.1111/pace.14829. Epub 2023 Sep 22.
Patients with atrial fibrillation (AF) are highly heterogeneous, and current risk stratification scores are only modestly good at predicting an individual's stroke risk. We aim to identify distinct AF clinical phenotypes with cluster analysis to optimize stroke prevention practices.
From the prospective Chinese Atrial Fibrillation Registry cohort study, we included 4337 AF patients with CHA DS -VASc≥2 for males and 3 for females who were not treated with oral anticoagulation. We randomly split the patients into derivation and validation sets by a ratio of 7:3. In the derivation set, we used outcome-driven patient clustering with metric learning to group patients into clusters with different risk levels of ischemic stroke and systemic embolism, and identify clusters of patients with low risks. Then we tested the results in the validation set, using the clustering rules generated from the derivation set. Finally, the survival decision tree was applied as a sensitivity analysis to confirm the results.
Up to the follow-up of 1 year, 140 thromboembolic events (ischemic stroke or systemic embolism) occurred. After supervised metric learning from six variables involved in CHA DS -VASc scheme, we identified a cluster of patients (255/3035, 8.4%) at an annual thromboembolism risk of 0.8% in the derivation set. None of the patients in the low-risk cluster had prior thromboembolism, heart failure, diabetes, or age older than 70 years. After applying the regularities from metric learning on the validation set, we also identified a cluster of patients (137/1302, 10.5%) with an incident thromboembolism rate of 0.7%. Sensitivity analysis based on the survival decision tree approach selected a subgroup of patients with the same phenotypes as the metric-learning algorithm.
Cluster analysis identified a distinct clinical phenotype at low risk of stroke among high-risk [CHA DS -VASc≥2 (3 for females)] patients with AF. The use of the novel analytic approach has the potential to prevent a subset of AF patients from unnecessary anticoagulation and avoid the associated risk of major bleeding.
