Machine learning-based phenotyping and assessment of treatment responses in heart failure with preserved ejection fraction

作者：Rui Li, Yijun Liu, Zhen Zhao, Conghui Zhang, Wenyue Dong, Qi Yu, Jun Gao, Feng Gu, Brian E. Carlson, Daniel Beard, Scott L. Hummel, Baoxia Chen, Hui Fan, Xiaoyan Gu, Xinwei Hua, Yi‐Da Tang · 发表于：EClinicalMedicine · 年份：2025 · DOI：10.1016/j.eclinm.2025.103462 · 被引用次数：17 · 研究领域：Heart Failure Treatment and Management、Diabetes Treatment and Management、Cardiovascular Function and Risk Factors

Background: Heart failure with preserved ejection fraction (HFpEF) accounts for over half of heart failure cases, yet effective treatments remain limited due to its clinical heterogeneity. This study aimed to identify distinct HFpEF phenotypes using machine-learning based algorithm and to compare treatment responses across different phenogroups. Methods: Our training cohort included 2147 hospitalized patients with HF with left ventricular ejection fraction (LVEF) ≥50% at Peking University Third Hospital (2014-2023). A two-stage DeepCluster model with a fully connected neural network was used to identify HFpEF phenogroups based on 107 demographic and clinical variables from electronic medical record (EMR). Cox proportional hazard models were used to assess patients' prognosis and treatment responses. The phenotyping model was validated internally using leave-one-out cross-validation method and externally with data from the TOPCAT clinical trial (n = 1696) and a well-characterized HFpEF patient cohort from the University of Michigan Health System (UMHS, n = 128). Findings: Three distinct HFpEF phenogroups were identified. Phenogroup 1 (n = 815) had the highest burden of metabolic comorbidities, along with left ventricular hypertrophy, and both systolic and diastolic dysfunction. Phenogroup 2 (n = 608) comprised predominantly females with atrial fibrillation and structural abnormalities in the atria and right ventricle, with mainly diastolic dysfunction. Phenogroup 3 (n = 724) i...