构建和评估基于机器学习算法进行的持续置换治疗的急性损患者功能恢复的预测模型
Lei Zhong1,2,3, Yafei Liang4, Liang Xu1
1Emergency and Critical Care Center, Intensive Care Unit, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, Hangzhou, Zhejiang, China.
Annals of medicine
|September 25, 2025
概括
机器学习模型预测了急性损伤 (AKI) 接受连续置换疗法 (CRRT) 的危急病患者的功能恢复. 拉索-LR模型显示了临床管理的最佳性能.
科学领域:
- 腎臟病學 (nephrology) 是一種醫學.
- 临界护理医学 临界护理医学
- 医疗信息学 医疗信息学
背景情况:
- 急性损伤 (AKI) 是重症患者常见的并发症.
- 连续脏替代疗法 (CRRT) 经常用于在重症监护室 (ICU) 管理AKI.
- 预测这些患者的功能恢复对于临床决策和资源分配至关重要.
研究的目的:
- 开发和验证机器学习 (ML) 模型,用于预测经过CRRT的重症患者的功能恢复.
- 使用ML技术识别影响功能恢复的关键因素.
- 建立一个可靠的工具,用于预后评估和临床疾病管理.
主要方法:
- 来自MIMIC-IV数据库和湖州中央医院的回顾性数据收集.
- 应用ML算法,包括LASSO回归,以识别预测特征.
- 使用AUROC,校准曲线和DCA开发和评估一个Lasso-LR模型.
- 开发模型的外部验证.
主要成果:
- 分析了接受CRRT的1078名AKI患者的队列,其功能恢复率为18.18%.
- 通过LASSO回归,通过LASSO回归确定了影响功能恢复的六个重要特征.
- 拉索-LR模型实现了0.774 (培训) 和0.748 (外部验证) 的AUROC.
- 根据DCA,基于Lasso-LR模型的诺米克图显示了临床实用性.
结论:
- 拉索-LR模型在评估的ML模型中表现出优异的性能,用于预测CRRT中AKI患者的功能恢复.
- 这种基于ML的模型可以作为一种有价值的工具,用于提高临床疾病管理和预后评估在重症监护机构.
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