根据表型分层的心脏性休克患者的临床过程
Elric Zweck1, Manreet Kanwar2, Song Li3
1The CardioVascular Center, Tufts Medical Center, Boston, Massachusetts, USA; Division of Cardiology, Pulmonology, and Vascular Medicine, University Hospital Dusseldorf, Dusseldorf, Germany.
JACC. Heart failure
|June 24, 2023
概括
证实了三种不同的心脏性休克 (CS) 现型,在不同患者群体中显示出相似的死亡率和临床特征. 这些验证的CS表型可以指导未来的临床试验和个性化治疗策略.
科学领域:
- 心脏病学 心脏病学
- 机器学习在医学中的应用
- 关键护理医学 关键护理医学
背景情况:
- 尽管有进展,心脏性休克 (CS) 的住院死亡率很高 (30-60%),其异质性使其复杂化.
- 之前在心脏性休克工作组 (CSWG) 注册表V1.1中已经确定了三种不同的CS表型 (非拥挤,心脏,心脏代谢).
- 这些表型对临床试验设计和患者管理构成挑战.
研究的目的:
- 在外部验证基于机器学习的CS表型.
- 定义与每个CS表型相关的临床过程和结果.
- 在独立的队列中评估CS表型的可重复性.
主要方法:
- 从CSWG注册表版本2中利用了1,890名全因CS患者.
- 应用最近的中心体方法来识别基于初始集群的CS表型.
- 在796名患者中追溯识别的表型进行分析.
主要成果:
- 对表型I,II和III的住院死亡率分别为23%,41%和52%,与V1发现 (21%,45%,55%) 一致.
- 表型特定的人口,血液动力学和代谢特征是可重现的.
- 机械循环支的使用因表型而异 (58.8%在I,45.7%在II,51.9%在III) 并与心脏上CS (OR 1.82) 的死亡率增加有关.
- 现型II和III,以及SCAI阶段E,独立预测死亡率增加.
结论:
- 该研究支持这些机器学习衍生的CS表型的普遍适用性.
- 这些验证的表型可以为未来CS的临床试验设计提供信息.
- 基于特定的CS表型的定制管理算法是可行的和推的.
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