基于人工智能的心电生物标志物用于急性心力衰竭患者的预测结果:前性队列研究
Youngjin Cho1,2, Minjae Yoon1, Joonghee Kim2,3
1Division of Cardiology, Department of Internal Medicine, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Gyeonggi-do, Republic of Korea.
Journal of medical Internet research
|July 3, 2024
概括
分析心电图 (ECG) 的人工智能 (AI) 算法可以预测急性心力衰竭 (HF) 患者的结果. 这种基于人工智能的心电图分数可以作为一种新的,具有成本效益的高频管理生物标志物.
科学领域:
- 心脏病学 心脏病学
- 人工智能的人工智能
- 生物标志物发现发现
背景情况:
- 现有的心力衰竭 (HF) 生物标志物面临成本和常规临床使用的可用性限制.
- 需要可访问和可靠的方法来预测HF患者的结果.
研究的目的:
- 评估人工智能 (AI) 算法对急性心力衰竭 (HF) 患者使用心电图 (ECG) 预测结果的实用性.
- 为了确定AI衍生的心电图分数是否可以作为HF预后的新生物标志物.
主要方法:
- 在韩国两所三级医疗中心,从1254名急性高温肺炎患者的前性收集数据进行了回顾性分析.
- 使用深度学习系统 (定量ECG - QCG) 来分析基线ECG,训练识别关键条件.
- 计算和评估QCG-Critical得分,以预测其医院内心脏病死亡和长期死亡率的预测价值.
主要成果:
- 与幸存者相比,在医院内发生心脏病死亡的患者的QCG-Critical得分显著更高 (P<.001).
- QCG-Critical评分独立预测了医院内心脏病死亡,即使对临床因素和心声学数据进行了调整 (aOR1.68,P<.001).
- 较高的QCG-Critical得分与长期死亡率的增加有关 (aHR为2.69,P<.001).
结论:
- 使用基于AI的QCG-Critical得分预测急性心力衰竭患者的结果是可行的.
- 由人工智能衍生出的QCG-Critical得分显示了作为心力衰竭预后的新和可访问的生物标志物的潜力.
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