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在严重/危急的COVID-19患者中开发二次感染的风险预测模型

Yinmei Zhang¹, Mingmei Lin², Zhenchao Wu³

¹Department of Laboratory Medicine, Peking University Third Hospital, Haidian District, No. 49 North Garden Road, Beijing, 100191, People's Republic of China.

BMC infectious diseases

|May 21, 2025

概括

一个机器学习模型有效地使用临床数据预测严重的COVID-19患者的二次感染. 关键预测因素包括平均血小板体积,前素和介质素-8,有助于早期干预.

科学领域:

传染性疾病传染性疾病
关键护理医学关键护理医学
计算生物学计算生物学

背景情况:

严重和危急的COVID-19患者有很高的二次感染风险.
早期识别易受二次感染的患者对于及时干预和改善结果至关重要.
现有的预测方法可能无法完全捕捉该人群中二次感染发展的复杂性.

研究的目的:

开发和验证严重或危急的COVID-19患者中二次感染的预测模型.
确定与二次感染风险相关的关键临床特征和实验室指标.
利用机器学习来提高预测准确性和临床实用性.

主要方法:

对307名严重/危急的COVID-19患者的回顾性分析 (156人患有二次感染,151人没有).
利用Boruta算法进行特征选择,并评估了八个机器学习模型.
选择了最佳的随机森林模型,并使用了SHapley添加式扩展 (SHAP) 进行解释.

主要成果:

确定了九个重要的预测因子:机械通风,前素 (PCT),介乐金-8 (IL-8),介乐金-6 (IL-6),血尿素,葡萄糖,肌酸激酶,乳酸脱酶和平均血小板体积 (MPV).
随机森林模型实现了高性能 (AUC培训:0.981,AUC测试:0.836).
SHAP分析强调MPV,PCT和IL-8是最有影响力的预测因素.

结论:

使用可访问的临床参数开发了一种有效的预测模型,用于重症COVID-19患者的二次感染风险.
该模型促进了早期临床干预和改进的患者管理策略.
机器学习为预测和管理危急疾病中的二次感染提供了一种有希望的方法.

关键词:

实验室指标实验室指标机器学习是机器学习.风险预测模型的风险预测模型.二次感染二次感染严重/危急的COVID-19病例

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