通过使用已确定的临床变量和遗传信息,改善过度缩性心肌病的突然心脏死亡风险分层
Ali Amr1,2, Jan Koelemen1,2, Christoph Reich1,2
1Institute for Cardiomyopathies & Center for Cardiogenetics, Department of Medicine III, University of Heidelberg, Im Neuenheimer Feld 410, 69120, Heidelberg, Germany.
概括
这项研究验证了高性心肌病 (HCM) 患者突然心脏死亡 (SCD) 风险模型. 整合遗传信息可以改善风险预测,并有助于临床决策,特别是在边界病例中.
科学领域:
- 心脏病学 心脏病学
- 遗传学 遗传学 是一个
- 预防医学 预防医学
背景情况:
- 欧洲和美国的心脏病学会有不同的突发心脏病死亡 (SCD) 风险模型,用于增高性心肌病 (HCM).
- 目前的风险分层方法需要在不同患者队列中进行验证.
- 基因型信息可以提高SCD风险预测在HCM.
研究的目的:
- 在德国HCM队列中验证现有的SCD风险预测模型.
- 评估将基因型信息整合到SCD风险分层中的影响.
- 改善临床决策,以预防HCM患者的SCD.
主要方法:
- 招募了283名没有先前SCD或心律失常事件的成年HCM患者.
- 分析了使用ESC和AHA/ACC指南的5年SCD风险估计.
- 将遗传发现集成到多变量考克斯比例危险模型中.
主要成果:
- 在49%的患者中发现了致病变体.
- 与ESC (AUC 0.74) 和AHA/ACC (AUC 0.70) 模型相比,基因型综合模型显示AUC (0.76) 和灵敏度 (0.86) 的改善.
- 修改后的基因型模型将ICD植入所需治疗数量 (NNT) 从13个 (ESC) 降至9个.
结论:
- 目前的SCD风险模型显示可接受的性能,但可能会错过高风险个体.
- 将遗传发现纳入风险分层是可行的,并增强决策能力,特别是对于边界风险群体.
- 需要进一步完善风险模型,以确定所有高风险HCM患者.
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