基于心电图的人工智能算法有助于预测移植后的长期死亡率
Niv Pencovich1,2, Byron H Smith3, Zachi I Attia4
1Departments of Surgery and Immunology, William J. von Liebig Center for Transplantation and Clinical Regeneration, Mayo Clinic, Rochester, MN.
Transplantation
|April 1, 2024
概括
术前心电图 (ECG) 的人工智能 (AI) 分析可以预测移植 (KT) 后的长期死亡率. 这些AI-ECG评分可以识别高风险患者,即使是没有症状的患者,以便更密切地监测心脏.
科学领域:
- 心脏病学 心脏病学
- 腎臟病學 (nephrology) 是一種醫學專業.
- 人工智能在医学中的应用
背景情况:
- 预测移植 (KT) 后的长期死亡率是一项挑战.
- 基线临床数据在预测后KT结果方面存在局限性.
- 需要新的方法来改善 KT 接受者的风险分层.
研究的目的:
- 评估人工智能 (AI) 支持的手术前心电图 (ECG) 分析,以预测移植患者的长期死亡率.
- 评估AI-ECG算法的实用性,以识别患有KT后死亡风险增加的患者.
- 为了确定AI-ECG预测是否即使在无症状患者中也是有价值的.
主要方法:
- 从6504名KT接受者的手术前心电图分析,遍及梅奥诊所的三个地点 (2006-2021年).
- 利用6个经过验证的AI算法预测诸如心房动,大动脉狭窄和生物年龄等情况.
- 与患者死亡率数据相关联的AI算法输出,根据临床因素进行调整.
主要成果:
- 所有AI-ECG算法都与长期全因死亡率 (P < 0.001) 有显著关联.
- 对心房动和大动脉狭窄的AI算法在调整临床因素后仍然独立预测.
- AI-ECG分析改善了对整体 (C=0.74) 和心脏病死亡 (C=0.751) 的预测,即使在无症状个体中也是如此.
结论:
- 人工智能支持的手术前心电图分析是预测移植后长期死亡率的宝贵工具.
- AI-ECG评分可以识别较高风险的KT患者,可能从增强的心脏监测中受益.
- 这种方法提供了一种非侵入性的方法,可以在移植前的环境中增加风险评估.
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