用心脏超声和病历数据构建新评分，精准预测肥厚型心肌病患者5年心血管风险。
Improving Risk Stratification in Hypertrophic Cardiomyopathy: A Novel Score Combining Echocardiography, Clinical, and Medication Data
- 融合超声、临床与用药数据，用随机森林建模提升风险预测。
- 内部验证AUC达0.85，显著优于现行欧洲指南评分（0.56）。
- 模型可长期追踪风险变化，适合个性化随访管理。
肥厚型心肌病（HCM）需精准风险分层以指导植入式心律转复除颤器（ICD）治疗和随访管理。现有模型如欧洲心脏病学会（ESC）评分判别能力中等。本研究基于电子健康记录（EHR）中的常规超声、临床及用药数据，开发了一种稳健且可解释的机器学习（ML）风险评分，用于预测HCM患者5年复合心血管事件。模型在佛罗伦萨医院的SHARE队列（N=1,201）中训练并内部验证，在雷恩医院独立队列（N=382）中外部验证。最终的随机森林集成模型内部AUC达0.85±0.02，显著高于ESC评分（0.56±0.03）。外部验证中，该模型生存曲线差异显著（Log-rank p = 8.62×10⁻⁴），优于ESC评分（p = 0.0559）。纵向分析显示，无事件患者的风险评分具有时间稳定性。模型高可解释性及持续风险监测能力，为HCM个体化临床管理提供了有力工具。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。