解放军医学院学报2026，Vol.47Issue(5)：505-513,9.DOI:10.12435/j.issn.2095-5227.26010704
重症肺炎短期死亡风险预测模型的构建与验证
Development and validation of a short-term mortality risk prediction model for severe pneumonia
摘要
Abstract
Background Patients with severe pneumonia are in critical condition and have a high mortality rate;early intervention in this population can significantly improve prognosis.Objective To identify risk factors for mortality in patients with pneumonia in intensive care units,develop a predictive model for short-term mortality(death within 7 days),and apply this model in clinical practice.Methods Clinical data on 4 247 critically ill pneumonia patients were collected from the MIMIC-Ⅳ database(2008—2022).Data were randomly divided into a training set(n=3 185)and a validation set(n=1 062)in a 3:1 ratio.Five machine learning models were constructed using features selected via LASSO regression:logistic regression,elastic net(Enet),random forest(RF),extreme gradient boosting(XGBoost),and light gradient boosting machine(LightGBM).The optimal model was comprehensively evaluated using receiver operating characteristic(ROC)curves,calibration curves,decision curve analysis(DCA),sensitivity,specificity,and negative predictive value(NPV),with comparative analysis against the SOFA score.Model interpretability was enhanced through a nomogram for individualized risk visualization,and an online web calculator was developed to improve clinical utility.Results The final model incorporated nine key features:pH,age,blood urea nitrogen,oxygen saturation,lactate,activated partial thromboplastin time(APTT),anticoagulant therapy,type of infection,and anti-pathogen treatment.Among the five models,the logistic regression model demonstrated the best overall performance in the validation set(AUC=0.82,95%CI:0.77-0.88;NPV=0.99,95%CI:0.97-0.99),with the calibration curve and DCA confirming good stability and clinical applicability.Logistic regression model had a Brier score of 0.042,a calibration slope of 0.944,and an intercept of 0.027;the Hosmer-Lemeshow goodness-of-fit test indicated good fit(P=0.768),and its predictive performance was significantly superior to that of the SOFA score(DeLong test,P＜0.05).The study further deployed the final model as an online web-based calculator for rapid assessment of short-term mortality risk in patients with severe pneumonia.Conclusion This study constructed and validated a machine learning-based clinical predictive model for short-term mortality risk in severe pneumonia.The model demonstrates high predictive efficacy and clinical utility,supporting early clinical decision-making and risk assessment.关键词
重症监护病房/肺炎/MIMIC-Ⅳ数据库/机器学习/临床预测模型Key words
intensive care unit/pneumonia/MIMIC-Ⅳ database/machine learning/clinical prediction model分类
医药卫生引用本文复制引用
胡海明,赵立娟,李米锶,周鸽儿,乔一珊,常德..重症肺炎短期死亡风险预测模型的构建与验证[J].解放军医学院学报,2026,47(5):505-513,9.基金项目
国家科技重大专项(2025ZD01903600) （2025ZD01903600）
国家十四五重点研发计划青年科学家项目(2021YFC2302300) （2021YFC2302300）
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