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预测患有严重心力衰竭的患者的存活率:在HELP-HF队列中验证风险得分
Mauro Chiarito1,2, Davide Stolfo3, Alessandro Villaschi1,2
1Humanitas Research Hospital IRCCS, Rozzano, Milan, Italy.
European journal of heart failure
|January 21, 2025
概括
严重心力衰竭 (HF) 的风险评分通常表现不佳,特别是在高风险患者中. 机器学习模型显示出有限的改善,需要进一步验证来预测高级高频率的死亡率.
科学领域:
- 心脏病学 心脏病学
- 医疗信息学 医疗信息学
背景情况:
- 在严重心力衰竭 (HF) 中,准确的预后对于指导先进治疗选择至关重要.
- 现有的风险评分被广泛使用,但它们在严重高频率人群中的实际表现需要评估.
研究的目的:
- 评估各种既定风险评分和新型机器学习 (ML) 模型对重度高血压患者1年死亡率的预测准确度.
- 确定在这个脆弱的患者群体中最有效的风险分层工具.
主要方法:
- 在HELP-HF队列中评估MAGGIC,3-CHF,ADHF/NT-proBNP,GWTG-HF得分,高级HF和脆弱的HFA-ESC标准以及INTERMACS资料.
- 使用曲线下的面积 (AUC) 对1年死亡率进行预后准确性的比较.
- 开发和测试基于ML的模型,包括支持矢量机.
主要成果:
- 在一年内,共有265例死亡 (23.1%) 发生.
- MAGGIC,GWTG-HF和ADHF/NT-proBNP得分显示出中度的预后准确性 (AUC≥0.70),而其他得分表现不佳.
- 对于高风险患者,准确性显著下降;ML模型只显示了与现有得分相比的边际改善.
结论:
- 目前的风险评分对于预测严重HF死亡率的准确性有限,特别是在高风险人群中.
- 机器学习模型的表现并没有大大超过传统得分,并且需要在更大的队列中进一步验证.
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