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基于血液测试的急性入院患者短期和长期死亡率的预后机器学习模型的开发和验证

Baker Nawfal Jawad^1,2, Shakir Maytham Shaker³, Izzet Altintas^4,5,6

¹Department of Clinical Research, Copenhagen University Hospital Amager and Hvidovre, Hvidovre, Denmark. Baker.jawad@regionh.dk.

Scientific reports

|March 12, 2024

概括

使用常规血液检查的机器学习模型可以预测紧急入院时的患者死亡风险. 这些模型识别高风险个体,有助于更好的患者管理和结果.

科学领域:

紧急医疗紧急医疗
机器学习机器学习
生物标志物生物标志物

背景情况:

现有的紧急诊所死亡率预测得分有其局限性.
需要准确和临床上适用的工具来预测患者的结果.

研究的目的:

研究机器学习算法在预测短期和长期死亡率方面的有效性.
用例行血液测试来评估紧急入院的死亡风险.

主要方法:

对超过48,000名成年急诊室入院者的回顾性队列研究.
利用PyCaret,一个自动化的机器学习库,来评估15个算法.
使用接收器操作特征曲线 (AUC) 下面的面积来评估预测性能.

主要成果:

八个机器学习算法显示出出色的预测性能 (AUC 0.85-0.93).
短期死亡率的关键预测因素包括乳酸脱酶 (LDH),白细胞计数,血液尿素 (BUN) 和体内平均血红蛋白度 (MCHC).
长期死亡率的预测因素包括年龄,LDH,可溶性尿素酶等离子素激活体受体 (suPAR),白蛋白和BUN.

结论:

在急诊室入院时进行的常规血液检查可以识别高死亡风险的患者.
机器学习模型为预测短期和长期死亡率提供了强大的工具.
这些发现支持使用血液生物标志物用于紧急护理中的风险分层.

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