在ICU患者中开发和验证基于LASSO的预测模型
Xueting Wang1,2, Yuxuan Chen3, Lan Hua1,2
1Department of Critical Medicine, Affiliated Hospital of Zunyi Medical University, Zunyi, China.
Frontiers in medicine
|September 3, 2025
概括
一个新的预测模型可以识别有意外低温风险的重症监护室 (ICU) 患者. 这种经过验证的模型有助于临床医生识别高风险个体以获得更好的患者结果.
科学领域:
- 危急护理医学
- 临床预测模型
- 患者安全
背景情况:
- 在重症监护室 (ICU) 患者中,无意中降温 (IH) 是一个重大风险.
- 早期识别高风险患者对于预防IH和改善结果至关重要.
研究的目的:
- 在ICU患者中开发和验证意外低温的预测模型.
- 为医疗保健专业人员提供一种工具,以识别高风险的IH患者.
主要方法:
- 使用720名ICU患者的数据开发了一个预测模型.
- 使用最小绝对收缩和选择运算符 (LASSO) 回归来选择变量.
- 使用来自两个高等医院的数据进行了内部和外部验证.
主要成果:
- 最终的模型包括六种预测剂:甲醇胺,抗发烧止痛药,RRT,侵入性机械通风,GCS和乳酸.
- 该模型的预测性能很强,内部验证的AUC为0. 852,外部验证的AUC为0. 846.
- 该模型表现出良好的校准和临床实用性,支持其在临床决策中的使用.
结论:
- 开发的IH预测模型对于ICU患者来说是准确,可靠和可通用的.
- 该模型可以有效地帮助医疗保健专业人员识别高风险的低温患者.
- 这种工具有可能改善ICU环境中的患者安全和结果.
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