多模态可视化和可解释的机器学习驱动的标记器使得症状性大动脉狭窄和心力衰竭的早期识别和预后能够预测,在跨导管大动脉置换后保留了喷射部分:多中心队列研究
Jun Wang1,2,3, Jiajun Zhu4, Hui Li1
1Department of Cardiology, The First Affiliated Hospital of Bengbu Medical University, Bengbu, China.
Journal of medical Internet research
|May 1, 2025
概括
这项研究开发了一种机器学习模型,用于预测TAVR治疗大动脉狭窄和HFpEF后患者的不良事件. 该模型使用易于获得的数据来识别高风险个体,以提供个性化治疗.
科学领域:
- 心血管医学 心血管医学
- 医疗信息学 医疗信息学
- 医疗保健中的机器学习
背景情况:
- 有限的研究存在于个性化风险分层使用多模式数据的症状性大动脉狭窄症 (AS) 和心力衰竭与保存喷射率 (HFpEF) 后传导管大动脉置换 (TAVR).
- 准确的风险评估对于优化患者管理和这一复杂人群的结果至关重要.
研究的目的:
- 开发和验证一个预测模型,用于在接受TAVR的重症AS和HFpEF患者的不良结果.
- 通过利用多式联运数据和机器学习技术,提高风险评估绩效.
主要方法:
- 一项多中心队列研究,对326名患有严重AS和HFpEF的患者进行了TAVR.
- 患者被分为训练 (n=195) 和验证 (n=131) 组.
- 一个双相特征选择过程 (LASSO逻辑回归和Boruta算法) 从多式联运数据中确定了关键预测因素. 评估了五种机器学习模型:决策树,K-最近邻居,随机森林,支持向量机器和极端梯度增强.
主要成果:
- 确定的关键预测因素包括年龄,N端前脑性尿酸,禁食血糖,三糖/高密度胆固醇比率,三糖指数,三糖-BMI指数,血的异性质指数和Apolipoprotein B.
- 支持矢量机器模型在预测主要心血管和脑血管不良事件 (MACCE) 中取得了最佳表现,在验证集中AUC为0.756.
- 该模型表现出良好的校准,强大的预测能力和在决策曲线分析中优异的净收益. 沙普利增量解释 (SHAP) 值用于模型的解释性.
结论:
- 一个基于机器学习的多式模式,包含8个可访问的预测因子,准确预测AS和TAVR后HFpEF患者的12个月MACCE风险.
- 该模型为识别高风险个体提供了有价值的工具,促进了个性化的风险分层和治疗策略.
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