用人工智能启用心电图指导,用于修复的Fallote四重律中的肺置换时间
Joshua Mayourian1, Lynn A Sleeper1, Vedang Diwanji1
1Department of Cardiology, Boston Children's Hospital, Department of Pediatrics, Harvard Medical School, Boston, MA, USA.
American heart journal
|August 31, 2025
概括
人工智能支持的心电图 (AI-ECG) 可以预测经过肺置换 (PVR) 的Fallot (rTOF) 患者修复后的存活率. 对于中等和高风险患者,PVR提供生存益处,而对于低风险患者则没有.
科学领域:
- 心脏病学
- 医疗人工智能
- 出生的心脏病
背景情况:
- 确定肺置换 (PVR) 患者的最佳时间是临床上的挑战.
- 以人工智能为基础的心电图 (AI-ECG) 被探索为辅助PVR计时决策的新工具.
研究的目的:
- 研究PVR前AI-ECG对rTOF患者PVR后生存的预测能力.
- 评估AI-ECG是否可以识别受益于PVR的rTOF患者的子组.
主要方法:
- 在波士顿儿童医院和多伦多总医院接受PVR的rTOF患者的回顾性分析,使用PVR前AI- ECG数据.
- 使用倾向性得分匹配方法将PVR患者与非PVR患者进行比较.
- 根据PVR前AI- ECG死亡概率,患者分为低,中,高风险组.
主要成果:
- 在PVR前的AI-ECG准确地预测了PVR后的死亡率 (c指数为0.77),优于基于成像的模型 (c指数为0.70) 并改善了组合模型的性能 (c指数为0.84).
- 与低风险和中等风险组相比,通过AI- ECG确定的高风险患者的15年死亡率 (83%的存活率) 显著增加 (97-98%的存活率).
- 肺置换与中期和高风险组的整体生存率增加有关,而低风险组则没有.
结论:
- 在rTOF患者中,AI-ECG是预测PVR后生存的有价值工具.
- 根据AI- ECG,在中高风险的rTOF患者中观察到PVR的生存益处.
- AI-ECG可以作为成像的补充生物标志物,以优化rTOF中的PVR时间.
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