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机器学习模型预测严重创伤性脑损伤患者的维持生命治疗的退出

Michael Cobler-Lichter¹, Jessica M Delamater¹, Fernanda J P Teixeira²

¹Division of Trauma & Surgical Critical Care, DeWitt Daughtry Family Department of Surgery, Ryder Trauma Center, University of Miami Miller School of Medicine, FL.

|October 10, 2025

概括

机器学习模型准确地预测了创伤性脑损伤 (TBI) 患者的维持生命治疗 (WLST) 停止. 设施WLST率显著影响决策,独立于临床因素,突出需要精细的预后工具.

科学领域:

神经学神经学
医疗信息学医疗信息学
创伤外科手术创伤外科

背景情况:

终止维持生命治疗 (WLST) 占创伤性脑损伤 (TBI) 死亡的一半以上.
尽管TBI死亡率有所改善,但WLST率保持不变,这表明潜在的预后误解.
对于WLST的决策是复杂的,受临床预后之外的因素的影响,导致中心间的变化.

研究的目的:

开发一种机器学习 (ML) 模型,用于预测严重TBI患者的WLST决策.
为了确定影响WLST决策的关键决定因素,假设设施WLST率作为一个重要因素.

主要方法:

使用美国外科医生学院创伤质量改善项目国家创伤数据库 (2017-2021) 的观察性研究.
包括严重的TBI患者 (缩写伤害尺度-头部≥1,格拉斯哥昏迷尺度<9).
开发了ML模型来预测WLST,优化接收器操作曲线 (AUROC) 下的面积,并使用Shapley值评估决定性影响.

主要成果:

在155,639名严重TBI患者中,20.8%接受了WLST.
ML模型实现了高预测准确度,AUROC从入院时的0.875提高到总停留时间的0.896.
对于WLST的关键预测因素包括年龄,最高的急救部门GCS和设施WLST率.

结论:

ML模型可靠地预测严重TBI中的WLST决策.
机构撤款文化,以设施WLST率来表示,是一个强大的独立预测指标.
完善预测工具至关重要,以减轻偏见,并防止受到机构实践影响的过早WLST决策.

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