不同类型心力衰竭的预测模型和风险得分:一篇综述
Yue Wei1, Siyu Liu1, Yunying Mu1
1Department of Cardiovascular Internal Medicine, Xiyuan Hospital, China Academy of Chinese Medical Sciences, Beijing, China.
Frontiers in medicine
|December 8, 2025
概括
本系统性审查分析了46个心力衰竭 (HF) 预测模型,发现质量和临床适用性存在显著差异. 未来的研究应该专注于方法的改进,以更准确地评估高频预后.
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:
- 心力衰竭 (HF) 代表着一个重大的全球健康挑战,需要准确的预后评估,以减轻不良事件.
- 现有的HF亚型的预后模型在质量上表现出相当大的变化,并且缺乏广泛临床使用的共识.
- 这项研究解决了对当前高频预测模型进行系统评估的需求.
研究的目的:
- 系统地审查和分析心力衰竭现有的预后预测模型的优点和局限性.
- 为未来的研究提供指导,以开发更准确,更适用于临床的高频预测模型.
主要方法:
- 在PubMed和Embase数据库中进行系统的文献搜索,直到2025年9月19日,寻找高频预报预测模型.
- 使用预测模型偏差风险评估工具进行偏差风险评估.
- 使用C指数和校准等指标评估模型性能.
主要成果:
- 包括来自38项研究的46个预测模型,按HF亚型分类 (减少,保留,轻微减少喷射分数或所有HF患者).
- 偏差风险分析显示,10个模型具有高风险,21个模型具有不清楚的风险,15个模型具有低风险.
- 详细概述每个模型的队列,方法,预测因素,结果,性能和局限性.
结论:
- 模型构建的方法改进,包括队列选择,预测查 (生物标志物,成像,多omics),建模策略和呈现,是至关重要的.
- 增强的预测模型可以显著改善所有心力衰竭患者的临床结果.
- 这一综述为推进心力衰竭预测研究提供了宝贵的参考.
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