机器学习用于紧急部门的风险分层 (MARS-ED):一个随机对照试验
Paul M E L van Dam1, William P T M van Doorn2, Lotte Sevenich3
1Department of Internal Medicine, Division of General Internal Medicine, Section Acute Medicine, Maastricht University Medical Center +, Maastricht, Netherlands. paul.van.dam@mumc.nl.
Nature communications
|December 1, 2025
概括
一个新的机器学习工具,RISK INDEX,使用实验室值准确预测31天的死亡率. 然而,它的预后准确性并没有转化为改善临床决策或急诊室的结果.
科学领域:
- 紧急医疗 紧急医疗
- 临床信息学是一种临床信息学.
- 医疗服务研究 医疗服务研究
背景情况:
- 紧急部门 (ED) 拥挤需要有效的风险分层.
- 现有的工具 (NEWS,APACHE II,SOFA) 在概括性和数据要求方面存在局限性.
- 机器学习为使用常规数据提高预测准确度提供了潜力.
研究的目的:
- 开发和评估风险指数,一种机器学习工具,使用实验室值,年龄和性别预测31天死亡率.
- 与标准护理和传统评分系统相比,评估风险指数的临床影响和预后准确性.
- 确定是否提高预后准确性转化为改善ED的临床决策和患者结果.
主要方法:
- 一个由研究者发起的,开放的,随机的非劣势性试验 (MARS-ED) 在大学医学中心进行了ED.
- 成年患者 (≥18岁) 经过≥4次实验室检测,被随机 (1:1) 分给标准护理或标准护理,再加上获得风险指数.
- 主要结局包括31天死亡率和临床影响的预后准确性,通过接受器操作特征曲线 (AUROC) 下的区域和治疗计划变化进行评估.
主要成果:
- 风险指数显示,与临床直觉和传统得分相比,31天死亡率的预后准确度更高 (AUROC 0.84与0.65-0.76).
- 风险指数预测在约一半的病例中与临床医生的预期有所不同,特别是在经验较少的医生中.
- 尽管具有很高的准确性,但风险指数并没有显著改变治疗计划 (0.16%的变化) 或临床结果,临床医生认为附加值较低.
结论:
- 预后准确性本身是不够的,以确保临床影响在急诊室设置.
- 以用户为中心的设计,可操作性和信任对于成功将机器学习工具集成到临床工作流中至关重要.
- 未来的开发应该专注于创造临床可操作的见解,而不是仅仅关注预测性表现.
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