在心力衰竭患者中预测一年死亡率的预测者,使用保存的喷射分数
medRxiv : the preprint server for health sciences
|December 25, 2025
概括
新的模型预测了心力衰竭的1年死亡率与保存的喷射分数 (HFpEF) 患者. 机器学习模型显示,使用通用入学数据进行个性化风险评估具有前景.
科学领域:
- 心脏病学 心脏病学
- 医疗信息学 医疗信息学
- 生物统计学 生物统计学
背景情况:
- 保存喷射分数 (HFpEF) 的心力衰竭是一个日益增长的临床挑战.
- 由于有限的变量和线性假设,HFpEF现有的预后得分可能无法捕捉复杂的风险模式.
- 需要改善HFpEF患者的风险预测模型.
研究的目的:
- 开发和内部验证1年全因死亡率的预测模型,用于因无补偿HFpEF而住院的患者.
- 将高级机器学习模型的性能与标准生存模型进行比较.
- 确定这一患者群体中死亡率的关键预测因素.
主要方法:
- 从2010年到2020年,对7840名HFpEF患者进行了回顾性队列研究.
- 使用基于树的分类器 (极端梯度增强,轻梯度增强) 和时间到事件模型 (Cox,RSF,GBS).
- 采用多重归算缺少的数据和模型调整的交叉验证;使用AUROC,一致性索引和校准进行评估.
主要成果:
- 在该队列中,一年死亡率为31.5%.
- 机器学习模型表现出良好的区分 (AUROC ~0.75) 和校准.
- 渐变增强生存 (GBS) 实现了最高的预测性表现 (一致性指数为0.718);一致的预测因素包括低白蛋白,年龄较大,高NT-proBNP,功能障碍和低血红蛋白.
结论:
- 不同的建模方法产生了可比的预测准确性和一致的风险因素.
- 一个透明的风险预测工具,利用随时可用的入院数据是可行的HFpEF.
- 可以加强患者层面的风险评估,支持精确的健康倡议.
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