通过机器学习对经过跨导管边缘到边缘修复的功能性腹患者的表型定型:MITRA-AI研究
Fabrizio D'Ascenzo1,2, Filippo Angelini1,2, Corrado Pancotti3
1Division of Cardiology, Cardiovascular and Thoracic Department, 'Citta della Salute e della Scienza' Hospital, Corso Bramante n 88, Turin 10126, Italy.
European heart journal. Digital health
|May 21, 2025
概括
机器学习确定了在严重的功能性额头吐 (FMR) 中进行过导管边缘到边缘修复 (TEER) 的不同患者组. 这种方法可以准确预测心血管死亡和心力衰竭住院情况,改善了TEER患者的选择.
科学领域:
- 心脏病学 心脏病学
- 医疗信息学 医疗信息学
- 人工智能在医学中的应用
背景情况:
- 严重的功能性腹反 (FMR) 是一个重大的临床挑战.
- 过导管端到端修复 (TEER) 为FMR提供了一个治疗选择,但最佳的患者选择至关重要.
- 确定TEER成功的预测因素仍然是一个未满足的临床需求.
研究的目的:
- 利用机器学习 (ML) 发现临床,心声和血液动力学数据中隐藏的模式.
- 为了确定与FMRTEER后的结果相关的不同患者表型.
- 在外部队列中验证ML衍生的风险分层.
主要方法:
- 对822名为FMR接受TEER治疗的患者的注册表 (MITRA-AI) 的分析 (2009-2020年).
- 利用ML识别基于临床和心声学参数的患者群.
- 主要终点:心血管死亡或心力衰竭住院治疗1年后的复合结果.
- 使用Mitrascore队列进行外部验证.
主要成果:
- 确定了四个不同的风险集群,事件发生率在20%至42%之间.
- 高风险表型的特点是左心室较大,射出率较低,缺血病因.
- 永久性心房动与低风险组内心力衰竭住院增加有关.
- 在外部验证队列中,ML衍生的表型显示出一致的风险分层.
结论:
- 在接受TEER的FMR患者中,ML分析成功地确定了有意义的临床表型.
- 这些表型在心血管死亡率和心力衰竭住院率上显示出显著的差异.
- 这些通过外部验证证实的发现可以帮助优化TEER患者的选择.
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