MIASurviveMTP:用于在大量输血协议后立即评估和生存预测的机器学习
Michael D Cobler-Lichter1, Jessica M Delamater1, Brianna L Collie1
1Divisions of Trauma, Surgical Critical Care & Burns, Daughtry Family Department of Surgery, University of Miami Miller School of Medicine and Jackson Memorial Hospital Ryder Trauma Center, Miami, Florida, United States of America.
PloS one
|October 24, 2025
概括
人工智能和机器学习模型准确地预测大规模输血 (MT) 和超大规模输血 (UMT) 创伤患者的死亡率. 这些AI/ML工具可以通过早期识别高风险个体来优化资源配置并改善患者的治疗结果.
科学领域:
- 创伤外科 手术 创伤外科
- 医疗信息学 医疗信息学
- 人工智能在医学中的应用
背景情况:
- 早期识别需要大量输血 (MT) 的创伤患者对于资源分配和改善结果至关重要.
- 人工智能 (AI) 和机器学习 (ML) 对MT患者的传统预测模型具有潜在的优势.
- 现有的MT患者预测模型尚未使用先进的AI/ML技术进行彻底评估.
研究的目的:
- 开发和评估AI/ML模型,用于预测MT和超大规模输血 (UMT) 患者的死亡率.
- 用不同的时间点数据 (到达与4小时数据) 来比较AI/ML模型的性能.
- 评估使用综合患者数据在MT管理中的实时临床决策的可行性.
主要方法:
- 来自美国外科医生学院创伤质量改善项目 (TQIP) 数据库 (2017-2021) 的患者的回顾性审查.
- 定义MT (≥5单位红细胞/全血4小时内) 和UMT (≥10单位).
- 开发ML模型以使用患者到达时和到达后4小时可用的变量预测6小时死亡率.
主要成果:
- AI/ML模型在预测MT和UMT患者的死亡率方面表现出很高的准确性.
- 接收器运行特征曲线 (AUROC) 下的区域对于MT预测在到达时为0.901,并以4小时的数据改进到0.943.
- 对于UMT预测的AUROC在到达时为0.858,并在4小时的数据中增加到0.922.
结论:
- AI/ML模型可靠地预测MT和UMT创伤患者的死亡率.
- 这些模型专门训练了MT和UMT群体,在纳入4小时数据时表现出卓越的性能.
- 未来的实施和集成到电子医疗记录中可以增强临床决策和优化大量的输血资源分配.
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