中国中西医结合急救杂志2026，Vol.33Issue(1)：66-73,8.DOI:10.3969/j.issn.1008-9691.2026.01.011
基于机器学习构建急性胰腺炎患者并发急性呼吸窘迫综合征风险预测模型
Development and validation of machine learning model for early prediction of acute respiratory distress syndrome in acute pancreatitis patients
摘要
Abstract
Objective To develop a machine learning based risk prediction model for acute respiratory distress syndrome(ARDS)in patients with acute pancreatitis(AP)using routinely available clinical indicators,thereby providing decision support for clinicians.Methods A retrospective cohort study was performed based on the Medical Information Mart for Intensive Care Ⅳ(MIMIC Ⅳ)3.1 database.Patients with AP were identified and stratified according to the occurrence of ARDS.Logistic regression analysis was used to screen candidate predictors.Based on the selected feature subset,8 machine learning models were constructed,including Logistic regression,gradient boosting machine(GBM),adaptive boosting(AdaBoost),random forest,K-nearest neighbors(KNN),neural network,extreme gradient boosting(XGBoost),and support vector machine(SVM).Hyperparameters were optimized according to the characteristics of each algorithm.Model performance was comprehensively evaluated to identify the optimal model,which was subsequently interpreted using Shapley Additive Explanations(SHAP)analysis.Results A total of 1 553 patients with AP were included,among whom 616(39.67%)developed ARDS after intensive care unit(ICU)admission.Logistic regression with backward elimination identified 8 key predictors associated with ARDS in patients with AP:acute physiology and chronic health evaluation Ⅱ(APACHE Ⅱ)score,sequential organ failure assessment(SOFA)score,respiratory rate(RR),serum creatinine(SCr),blood glucose,pulse oxygen saturation(SpO2),and albumin(Alb)[odds ratios(OR)with 95%confidence intervals(95%CI)were 1.061(1.030-1.093),1.185(1.130-1.242),1.047(1.007-1.090),0.962(0.949-0.974),0.587(0.459-0.752),1.559(1.339-1.814),and 1.002(1.000-1.004)],P<0.001,<0.001,0.021,<0.001,<0.001,<0.001,0.049 respectively.Among the 8 machine learning models,the best performance was achieved by the XGBoost and AdaBoost models,with areas under the receiver operator characteristic curve(AUC)and 95%CI of 0.855(0.827-0.883)and 0.844(0.815-0.872),respectively.In the test set,these models maintained stable performance,with AUC values of 0.838(95%CI:0.795-0.881)for XGBoost and 0.832(95%CI:0.789-0.876)for AdaBoost,and both outperforming the other algorithms in both predictive accuracy and stability.SHAP analysis indicated that the most influential predictors included APACHEⅡ score,SOFA score,maximum serum creatinine,minimum albumin,and maximum blood glucose.Conclusions The XGBoost and AdaBoost models constructed using routinely available clinical indicators and scoring systems demonstrated good predictive performance and stability.These models may effectively identify AP patients at high risk of developing ARDS,facilitating early detection,risk stratification,and personalized clinical intervention.关键词
急性胰腺炎/急性呼吸窘迫综合征/机器学习/预测模型/精准医疗Key words
Acute pancreatitis/Acute respiratory distress syndrome/Machine learning/Prediction model/Precision medicine引用本文复制引用
李玉倩,李文哲,王毅,王轶希,于湘友..基于机器学习构建急性胰腺炎患者并发急性呼吸窘迫综合征风险预测模型[J].中国中西医结合急救杂志,2026,33(1):66-73,8.基金项目
国家自然科学基金(82460372) （82460372）
新疆维吾尔自治区研究生科研创新项目(XJ2025G160) National Natural Science Foundation of China(82460372) （XJ2025G160）
Xinjiang Uygur Autonomous Region Graduate Student Scientific Research Innovation Project(XJ2025G160) （XJ2025G160）
