在心脏衰竭患者中重新定义β阻塞剂反应:机器学习集群分析
Andreas Karwath1, Karina V Bunting2, Simrat K Gill3
1Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, UK; Health Data Research UK Midlands Site, Birmingham, UK.
Lancet (London, England)
|September 2, 2021
概括
人工智能识别了心力衰竭的不同患者群体,其中包括心力衰竭患者. 这种方法揭示了特定的子组受益于β- 阻断剂,即使在心房动.
科学领域:
- 心脏病学
- 人工智能
- 药物基因组学
背景情况:
- 尽管目前的治疗方法,但心力衰竭的死亡率仍然很高,左心室喷射率 (LVEF) 降低.
- 对β- 阻断剂的复杂相互作用和个体反应尚不完全理解.
- 需要新的分析方法来个性化心力衰竭患者的治疗策略.
研究的目的:
- 开发和验证人工智能 (AI) 方法,以基于并发症识别不同的患者群体,并预测β阻断剂的疗效.
- 将患有心力衰竭和减少LVEF (HFrEF) 的患者分为不同对β- 阻断剂治疗反应的子组.
- 调查人工智能驱动的贝塔阻断剂在患有鼻节律和心房动的患者中的有效性.
主要方法:
- 利用基于神经网络的变异性自编码器和分层集群数据从9个随机控制试验的β- 阻断剂.
- 评估了所有原因的死亡率,平均随访时间为1. 3年,按心电图心律分层.
- 验证了人工智能模型的集群识别和预后准确性,使用一次试验的方法和跨试验的外部验证.
主要成果:
- 包括15659名HFrEF患者 (LVEF<50%).
- 在鼻腔节律方面,大多数群体显示β阻塞剂对死亡率有好处 (ORs为0. 54- 0. 74),除了症状较轻的老年患者.
- 在心房方面,一组较年轻的死亡风险较低的患者表现出β阻塞剂显著降低死亡率 (OR0. 57, p=0. 023),而其他患者则表现出中性的效果.
结论:
- 在接受β- 阻断剂治疗的HFrEF患者中,人工智能驱动的聚类有效地确定了预后子组.
- 人工智能方法揭示了特定患者的特征,其中贝塔阻断剂在鼻腔节律中的有效性不足.
- 最重要的是,AI确定了一组心房患者,他们从β- 阻断剂中获得了显著的死亡益处,挑战了这一群体中对中性疗效的普遍看法.
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