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整合大规模的遗传和临床信息来预测心力衰竭病例

Kuan-Han H Wu¹, Brooke N Wolford², Jiacong Du³

¹Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.

Communications medicine

|November 22, 2025

概括

整合遗传和电子健康记录风险得分显著改善了早期心力衰竭预测. 这种综合方法可以在诊断前几年加强风险识别,从而能够及时进行干预.

科学领域:

遗传学和基因组学在
心血管疾病流行病学
医疗信息学医疗信息学

背景情况:

心力衰竭 (HF) 是全球主要的死亡原因.
早期识别高风险个体对于有效干预至关重要.
现有的预测模型可能无法充分利用各种数据源.

研究的目的:

通过整合来自全基因组关联研究 (GWAS) 的多基因风险得分 (PRS) 和来自电子健康记录 (EHR) 的临床风险得分 (ClinRS) 来提高心力衰竭 (HF) 预测的准确性.
评估PRS和ClinRS组合的性能与单个得分和现有的HF风险模型相比.

主要方法:

从一个大型HF GWAS开发了一个PRS.
创建了一个ClinRS使用自然语言处理和LASSO回归在三个密歇根州医学队列的EHR数据.
使用后勤回归比较了基线模型,PRS,ClinRS和ClinRS+PRS组合模型的预测性能.
根据社区动脉样硬化风险 (ARIC) HF风险评分的验证模型.

主要成果:

与基线模型相比,PRS和ClinRS都显著改善了HF预测,高达诊断前八年.
结合的ClinRS+PRS模型提高了预测性能,在诊断前10年,比单个得分早两年.
在诊断前一年,ClinRS在ARIC HF风险评分上表现优越.

结论:

整合GWAS衍生的PRS和EHR衍生的ClinRS,为在临床诊断前识别心力衰竭风险的个体提供了附加的预测能力.
这种标准化和可扩展的风险预测工具有可能促进医生更早地进行干预,并改善患者的治疗结果.

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