对心肌梗塞的机器学习与指南推的诊断途径相比
Jasper Boeddinghaus1,2, Dimitrios Doudesis2,3, Pedro Lopez-Ayala1
1Cardiovascular Research Institute Basel (CRIB) and Department of Cardiology (J.B., P.L.-A., L.K., K.W., T.N., R.B., I.S., M.R.G., C.M.), University Hospital Basel, University of Basel, Switzerland.
Circulation
|February 12, 2024
概括
协作诊断和评估急性冠状动脉综合征 (CoDE-ACS) 工具使用机器学习准确识别心肌梗塞 (MI) 的概率,性能优于指导方针推的途径. 这种工具可以始终识别更多低风险患者,从而改善早期心脏病发作的诊断.
科学领域:
- 心脏病学
- 医疗信息学
- 在医疗保健中的机器学习
背景情况:
- 急性冠状动脉综合征 (ACS) 的诊断依赖于临床评估和心脏热素测量.
- 目前的心肌梗塞的诊断途径使用固定的时间点和值,这可能不是最佳的.
- 协作诊断和评估急性冠状动脉综合征 (CoDE-ACS) 是用于评估心脏病发作概率的机器学习工具.
研究的目的:
- 评估CoDE-ACS工具在不同连续心脏素测量时间点的诊断性能.
- 将CoDE-ACS性能与指南推的MI诊断途径进行比较.
- 评估CoDE-ACS在确定1型MI的低概率和高概率得分方面的有效性.
主要方法:
- 一项前性研究在5个国家的12个地点招募了4105名疑似心脏病患者.
- 在0,1和2小时进行了序列高灵敏性心脏素I测量.
- 对欧洲心脏病学会 (ESC) 和高STEACS路径进行了CoDE-ACS诊断性能评估.
主要成果:
- CoDE- ACS 确定56%的患者在呈现时的概率低 (NPV 99.7%,灵敏度为99.0%),排除了更多的患者,而不是ESC 0小时和高STEACS途径.
- 在1或2小时后,CoDE-ACS确定65% - 68%为低概率 (NPV99. 7%) 和18% - 19%为高概率 (PPV64. 9% - 68. 8%).
- 准则路径发现较少的低风险患者 (49% - 71%) 具有不同的PPV和NPV,留下更多的患者进行进一步观察.
结论:
- 无论是连续测量多激素的时间,CoDE-ACS都显示出一致的性能.
- 与指导方针推的途径相比,该工具识别出更高比例的低概率患者.
- 需要进行前性评估,以确定以概率为导向的护理是否能改善早期心脏病发作的诊断.
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