患有急性心肌梗塞的患者胃肠道出血的危险因素:多中心回顾性队列研究
Yanqi Kou1, Shicai Ye1, Yuan Tian1,2
1Department of Gastroenterology, Affiliated Hospital of Guangdong Medical University, Zhanjiang, China.
Journal of medical Internet research
|January 30, 2025
概括
一个机器学习模型有效地预测急性心肌梗塞患者的胃肠道出血,识别关键风险因素,如红细胞计数和冠心病,用于早期干预.
科学领域:
- 心脏病学 心脏病学
- 医疗信息学 医疗信息学
- 医疗保健中的机器学习
背景情况:
- 胃肠道出血 (GIB) 是急性心肌梗塞 (AMI) 患者的严重并发症,影响预后.
- 早期识别高风险个体对于改善结果和临床决策至关重要.
研究的目的:
- 开发和验证机器学习 (ML) 模型,以预测AMI患者的住院GIB.
- 确定该人群中GIB的主要风险因素.
- 评估模型对风险分层和决策支持的临床实用性.
主要方法:
- 1910名AMI患者的回顾性队列研究 (培训/测试) 和使用MIMIC-IV数据的外部验证.
- 波鲁塔算法用于预测器识别;训练和评估了7个ML算法.
- 使用AUC,精度,灵敏度,特异性,F1得分和决策曲线分析评估模型性能.
主要成果:
- 随机森林 (RF) 模型实现AUC为0.77 (培训/测试) 和0.75 (验证).
- 关键预测因素包括红细胞计数,血红蛋白,最大肌红蛋白,血红素和冠心病 (CHD).
- 冠心病被确定为GIB的独立风险因素 (OR 2.79).
结论:
- 基于ML的射频模型提供了一个强大的,临床上适用的工具,用于预测AMI患者的住院GIB.
- 该模型利用易于获得的数据进行早期风险分层和个性化预防.
- 在有或没有GIB的患者之间没有观察到短期生存的显著差异.
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