用机器学习模型预测心力衰竭或没有心力衰竭的患者的死亡率.
Se Yong Jang1,2, Jin Joo Park1,3, Eric Adler1
1Department of Cardiology, University of California, San Diego, California, USA.
JACC. Advances
|June 28, 2024
概括
马克尔-HF模型准确地预测心力衰竭 (HF) 患者和没有HF患者的1年死亡率,在各种患者群体中展示了广泛的适用性.
科学领域:
- 心脏病学 心脏病学
- 医疗信息学 医疗信息学
- 公共卫生 公共卫生
背景情况:
- 现有的风险预测模型通常针对特定条件,限制了它们在一般患者群体中的使用.
- 马克尔-HF模型最初是为心力衰竭 (HF) 患者开发的.
研究的目的:
- 评估MARKER-HF模型在预测1年死亡率方面的能力.
- 评估其在一个大型的社区医院注册表中的表现,包括患有和没有HF的患者.
主要方法:
- 对41,749名连续接受心声扫描的患者的分析.
- 包括患有 (n=4,640) 和没有HF的患者 (n=37,109).
- 基于心血管疾病,急性冠状动脉综合征,心房动,COPD,CKD,糖尿病,高血压和恶性瘤的非HF患者的亚组分析.
主要成果:
- 标志性HF显示强大的预测性表现为1年死亡率在两个HF (AUC=0.729) 和非HF患者 (AUC=0.770) 的HF.
- 在各种子组中观察到一致的准确性,包括那些患有心血管疾病和常见并发症的人.
- 患有恶性瘤的患者在类似的MARKER-HF得分下表现出更高的死亡率.
结论:
- 马克尔-HF模型有效预测心力衰竭患者的死亡率.
- 它的预测能力扩展到没有心力衰竭的患者,包括患有各种其他疾病的患者.
- 马克尔-HF为广泛的患者提供了一种多功能工具,用于对广泛的患者进行死亡风险评估.
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