改善肥厚型心肌病的风险分层：结合超声心动图、临床和药物数据的新型评分方法
Improving Risk Stratification in Hypertrophic Cardiomyopathy: A Novel Score Combining Echocardiography, Clinical, and Medication Data
AI总结：
本文提出一种结合超声心动图、临床和药物数据的新型评分方法，用于预测肥厚型心肌病患者5年复合心血管结局，优于现有ESC评分。
摘要：
肥厚型心肌病（HCM）需要准确的风险分层以指导ICD治疗和随访管理。现有模型如欧洲心脏病学会（ESC）评分的区分性能中等。本研究开发了一种稳健且可解释的机器学习（ML）风险评分，利用常规收集的超声心动图、临床和药物数据，通常包含在电子健康记录（EHRs）中，以预测HCM患者5年复合心血管结局。模型使用SHARE注册处（佛罗伦萨医院）的大型队列（N=1,201）进行训练和内部验证，并在外院（N=382）的独立队列上进行外部验证。最终的随机森林集成模型在内部的曲线下面积（AUC）为0.85±0.02，显著优于ESC评分（0.56±0.03）。关键的是，外部验证集的生存曲线分析显示，ML评分在风险分离方面优于ESC评分（Log-rank p=8.62×10^(-4) 对比 ESC评分 p=0.0559）。此外，纵向分析显示，所提出的风险评分在无事件患者中随时间推移保持稳定。该模型的高可解释性和其在纵向风险监测中的能力代表了HCM个性化临床管理的有前景的工具。
英文摘要：
Hypertrophic cardiomyopathy (HCM) requires accurate risk stratification to inform decisions regarding ICD therapy and follow-up management. Current established models, such as the European Society of Cardiology (ESC) score, exhibit moderate discriminative performance. This study develops a robust, explainable machine learning (ML) risk score leveraging routinely collected echocardiographic, clinical, and medication data, typically contained within Electronic Health Records (EHRs), to predict a 5-year composite cardiovascular outcome in HCM patients. The model was trained and internally validated using a large cohort (N=1,201) from the SHARE registry (Florence Hospital) and externally validated on an independent cohort (N=382) from Rennes Hospital. The final Random Forest ensemble model achieved a high internal Area Under the Curve (AUC) of 0.85 +- 0.02, significantly outperforming the ESC score (0.56 +- 0.03). Critically, survival curve analysis on the external validation set showed superior risk separation for the ML score (Log-rank p = 8.62 x 10^(-4) compared to the ESC score (p = 0.0559). Furthermore, longitudinal analyses demonstrate that the proposed risk score remains stable over time in event-free patients. The model high interpretability and its capacity for longitudinal risk monitoring represent promising tools for the personalized clinical management of HCM.
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