通过使用数字心电图,预测PreOpNet在进行重大非心脏手术后30天死亡率的外部验证
Arnaud Champetier1,2, Pedro Lopez-Ayala1,2, Christian Puelacher1,3
1Cardiovascular Research Institute Basel (CRIB) and Department of Cardiology, University Hospital Basel, University of Basel, Basel, Switzerland.
NPJ digital medicine
|October 16, 2025
概括
使用ECG的深度学习算法PreOpNet显示,在高风险手术患者中预测死亡和MACE的独立益处有限. 它与现有的风险分数和热素测试相结合时提供增量价值.
科学领域:
- 心脏病学 心脏病学
- 人工智能在医学中的应用
- 手术风险评估手术风险评估
背景情况:
- 术前风险评估对于高风险的手术患者至关重要.
- 指南推的工具已经存在,但正在探索像人工智能这样的新方法.
- 像PreOpNet这样的深度学习算法的性能在欧洲人群中并不成熟.
研究的目的:
- 评估PreOpNet的性能,一个使用12导电心电图的深度学习算法,用于预测30天所有原因死亡和MACE.
- 为了比较PreOpNet与修订的心脏风险指数 (RCRI) 和高灵敏度心脏托洛T (hs-cTnT).
- 评估PreOpNet在与RCRI和hs-cTnT结合使用时的增量预测值.
主要方法:
- 欧洲前性研究 (2014-2019) 招募6098名接受重大非心脏手术的高风险患者.
- 使用PreOpNet算法对12导数数字心电图进行分析.
- 将PreOpNet性能 (AUC) 与RCRI和hs-cTnT进行比较,以预测死亡和MACE.
主要成果:
- 在PreOpNet中,死亡 (AUC 0.707) 和MACE (0.675) 存在中度差异,但风险被高估了.
- 在死亡方面,PreOpNet的表现优于RCRI (AUC为0.644),但不超过MACE (0.662).
- 在这两种结局中,hs-cTnT都优越 (AUC为0.762死亡,0.743MACE).
- 在加上RCRI和/或hs-cTnT时,PreOpNet提供了增量价值.
结论:
- 作为一个独立的测试,PreOpNet对高风险手术患者的手术前风险分层具有有限的实用性.
- 将PreOpNet与RCRI和hs-cTnT相结合,可以提高预后准确度.
- 进一步的研究可能会改进人工智能算法,以改善手术中的心血管风险评估.
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