真实世界HFpEF纵向表型分析识别早期进展与终末稳定状态
基于多中心电子健康记录，利用自然语言处理识别2223例HFpEF患者，通过潜在类别分析和随机森林模型追踪5年表型演变。研究识别出四个可重复的表型组：年轻低共病、肥胖为主、老年心房功能障碍和心血管-肾脏-代谢组。年轻低共病组转组概率最高，而心血管-肾脏-代谢组与老年心房功能障碍组高度稳定，且前者校正死亡风险最高。
要点
- HFpEF可分四个纵向表型组，其中年轻低共病组转组概率最高，提示早期可进展状态
- 心血管-肾脏-代谢组与老年心房功能障碍组表型高度稳定，前者校正死亡风险最高
- 当前表型状态较基线分类能更优区分死亡风险，支持动态评估HFpEF表型
核心结论
- 基于电子健康记录的纵向自然语言处理表型分析识别出四个可重复的HFpEF表型组，具有不同的演变轨迹，可区分早期进展性疾病状态与稳定的晚期表型。
研究设计与样本
- 利用自然语言处理流程从2010至2022年多中心电子健康记录中识别HFpEF病例。
- 采用潜在类别分析定义基线表型组，并应用年度监督式随机森林分类以时变变量追踪个体5年内的表型转换。
- 使用竞争风险回归量化转换与死亡概率，时变Cox模型评估当前表型状态与死亡风险的关联。
- 在独立外部队列中重新进行潜在类别分析，并与随机森林预测结果比较一致性。
主要发现
- 在2223例患者中识别出四个表型组：年轻低共病占22%，肥胖为主占24%，老年心房功能障碍占32%，心血管-肾脏-代谢占23%。
- 年轻低共病组转换概率最高，为47.7%；心血管-肾脏-代谢组和老年心房功能障碍组转换率分别为3.2%和12.1%。
- 在年轻低共病组中，发生转换者基线心脏结构异常更明显，包括更高的E/e'、左室质量和室壁厚度。
- 校正后死亡风险以心血管-肾脏-代谢组最高，HR为1.53，95%CI 1.22至1.92，p<0.001。
关键数值与推荐
- 研究人群60%为女性，中位年龄75岁，IQR 63至83；左室射血分数60.1%；中位随访4.0年，IQR 2.2至6.1。
- 当前表型状态区分死亡风险的C指数为0.659，基线分类为0.648。
- 独立外部队列样本量为3349例，随机森林预测与独立潜在类别分析标签的C统计量为0.891至0.953。
临床怎么用
- 可借助人工智能驱动的电子健康记录工具更早识别HFpEF，为表型靶向治疗提供依据。
- 对年轻低共病HFpEF患者应关注早期心脏结构异常并监测表型进展。
- 对心血管-肾脏-代谢表型患者应加强死亡风险管理与综合干预。
以上解读依据论文摘要（全文未开放获取或许可不允许翻译转载）。
原文
Background and aims Heart failure with preserved ejection fraction (HFpEF) phenotypes have been characterised as static entities in trial populations yet their temporal dynamics remain unexplored in routine clinical practice. We aimed to identify HFpEF phenogroups from real-world electronic health records (EHRs), characterise their longitudinal stability and determine whether phenogroup trajectories were associated with differences in mortality. Methods A natural language processing (NLP) pipeline identified HFpEF cases from multisite EHRs (2010–2022). Latent class analysis (LCA) defined baseline phenogroups and annual supervised random forest (RF) classification using time-updated values of the same baseline LCA variables tracked individual transitions over five years. Competing risks regression quantified transition versus death probabilities. Time-varying Cox models assessed mortality risk associated with current (vs baseline) phenogroup status. Phenogroups were independently re-derived by LCA in an external cohort and compared with RF predictions. Results Among 2223 patients (60% female, median age 75 years (IQR 63–83); left ventricular ejection fraction (LVEF) 60.1%; follow-up 4.0 years (IQR 2.2–6.1), we identified four phenogroups: young-low comorbidity (22%), obesity-predominant (24%), elderly atrial dysfunction (32%) and cardiovascular-kidney-metabolic (23%). Young-low comorbidity had the highest transition probability (47.7%), while cardiovascular-kidney-metabolic and elderly-atrial dysfunction were highly stable (3.2% and 12.1% transitioned, respectively). Within young-low comorbidity, those who transitioned had greater baseline cardiac abnormalities, including higher E/e', left ventricle mass and wall thickness. Adjusted mortality risk was highest in cardiovascular-kidney-metabolic (HR 1.53, 95% CI 1.22 to 1.92, p<0.001); current phenogroup status discriminated mortality risk modestly better than baseline classification alone (C-index 0.659 vs 0.648). In an independent external cohort (n=3349), RF predictions agreed strongly with independently derived LCA phenogroup labels (C-statistics 0.891–0.953). Conclusion Longitudinal NLP-based phenotyping of real-world EHRs identified four reproducible HFpEF phenogroups with distinct trajectories, distinguishing an early progressive disease state from stable advanced phenotypes. Earlier HFpEF detection using artificial intelligence-driven EHR tools could facilitate phenotype-targeted treatment of patients
引用
- Open Heart：Longitudinal phenotyping of heart failure with preserved ejection fraction identifies early- and end-stage disease states
供执业医师工作参考，不替代临床判断；数值与分级以指南原文为准。