Affiliation:
1. Department of Cardiology Beijing Anzhen Hospital Capital Medical University National Clinical Research Center for Cardiovascular Diseases Beijing China
2. Ping An Health Technology Beijing China
3. Heart Health Research Center Beijing China
4. School of Public Health Peking University Health Science Center Beijing China
5. Department of Cardiology The First Affiliated Hospital of Zhengzhou University Zhengzhou Henan Province China
Abstract
AbstractObjectivePatients 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.MethodsFrom the prospective Chinese Atrial Fibrillation Registry cohort study, we included 4337 AF patients with CHA2DS2‐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.ResultsUp 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 CHA2DS2‐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.ConclusionsCluster analysis identified a distinct clinical phenotype at low risk of stroke among high‐risk [CHA2DS2‐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.
Funder
National Key Research and Development Program of China
National Natural Science Foundation of China
Beijing Municipal Commission of Education
Bristol-Myers Squibb
Pfizer
Johnson and Johnson
Bayer
Subject
Cardiology and Cardiovascular Medicine,General Medicine