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已发表论文
基于两种机器学习方法的脓毒症合并自身免疫性疾病患者 28 天死亡率预测模型的开发与验证
Authors Wang Z , Xiao X, Li S, He J, Li Y, Huang F , Wang J
Received 11 August 2025
Accepted for publication 3 January 2026
Published 5 February 2026 Volume 2026:19 555953
DOI https://doi.org/10.2147/JIR.S555953
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 2
Editor who approved publication: Dr Xin Du
Zhiyang Wang,1,2,* Xin Xiao,1,* Shifeng Li,1,* Jiachen He,1 Yanou Li,1 Fang Huang,1 Jun Wang1
1Department of Critical Care Medicine, The First Affiliated Hospital of Soochow University, Suzhou, 215006, People’s Republic of China; 2Department of Emergency and Critical Care Medicine, The Second Affiliated Hospital of Soochow University, Suzhou, 215006, People’s Republic of China
*These authors contributed equally to this work
Correspondence: Jun Wang, Email dr_wangjun@suda.edu.cn Fang Huang, Email blue-virus@163.com
Objective: This study aimed to develop a prognostic model for patients with sepsis complicated by autoimmune diseases using machine learning methods and validate the model.
Methods: Data on patients with sepsis and autoimmune diseases were extracted from the MIMIC-IV database. Participants were randomly divided into training set and validation set according to the ratio of 7:3. The predictors were selected by using LASSO regression analysis and the Boruta algorithm which affect the 28-day prognosis of patients. A nomogram was developed based on independent risk factors identified by logistic regression for 28-day prognosis and was internally and externally validated using calibration curves and DCA. Based on nomogram scores, patients were stratified into high- and low-score groups, with KM analysis demonstrating significant differences in mortality between the cohorts.
Results: A total of 1,481 patients from the MIMIC-IV database met inclusion criteria and an external validation set included 57 patients from the Department of Critical Care Medicine of the First Affiliated Hospital of Soochow University. Ten overlapping predictors (Age, Gender, BMI, WBC, BUN, PT, APTT, history of cerebrovascular disease, history of liver disease, and CRRT) were identified by LASSO and Boruta algorithms and were subsequently confirmed as statistically significant independent risk factors through logistic regression. The prediction model built by the ten predictors showed superior predictive performance compared to the SOFA score in training (AUC=0.772), internal validation (AUC=0.771), and external validation cohorts (AUC=0.787). Hosmer-Lemeshow tests and calibration curves indicated strong agreement between predicted outcomes and actual observations across all cohorts, and DCA suggested significant clinical utility. The KM curve shows that the mortality rate of the high-score group is significantly higher than that of the low-score group.
Conclusion: A prognostic model for predicting 28-day mortality in sepsis patients with autoimmune diseases demonstrated robust predictive performance and clinical applicability upon internal and external validation.
Keywords: sepsis, LASSO regression, Boruta algorithm, dynamic nomogram, MIMIC-IV
1Department of Critical Care Medicine, The First Affiliated Hospital of Soochow University, Suzhou, 215006, People’s Republic of China; 2Department of Emergency and Critical Care Medicine, The Second Affiliated Hospital of Soochow University, Suzhou, 215006, People’s Republic of China
*These authors contributed equally to this work
Correspondence: Jun Wang, Email dr_wangjun@suda.edu.cn Fang Huang, Email blue-virus@163.com
Objective: This study aimed to develop a prognostic model for patients with sepsis complicated by autoimmune diseases using machine learning methods and validate the model.
Methods: Data on patients with sepsis and autoimmune diseases were extracted from the MIMIC-IV database. Participants were randomly divided into training set and validation set according to the ratio of 7:3. The predictors were selected by using LASSO regression analysis and the Boruta algorithm which affect the 28-day prognosis of patients. A nomogram was developed based on independent risk factors identified by logistic regression for 28-day prognosis and was internally and externally validated using calibration curves and DCA. Based on nomogram scores, patients were stratified into high- and low-score groups, with KM analysis demonstrating significant differences in mortality between the cohorts.
Results: A total of 1,481 patients from the MIMIC-IV database met inclusion criteria and an external validation set included 57 patients from the Department of Critical Care Medicine of the First Affiliated Hospital of Soochow University. Ten overlapping predictors (Age, Gender, BMI, WBC, BUN, PT, APTT, history of cerebrovascular disease, history of liver disease, and CRRT) were identified by LASSO and Boruta algorithms and were subsequently confirmed as statistically significant independent risk factors through logistic regression. The prediction model built by the ten predictors showed superior predictive performance compared to the SOFA score in training (AUC=0.772), internal validation (AUC=0.771), and external validation cohorts (AUC=0.787). Hosmer-Lemeshow tests and calibration curves indicated strong agreement between predicted outcomes and actual observations across all cohorts, and DCA suggested significant clinical utility. The KM curve shows that the mortality rate of the high-score group is significantly higher than that of the low-score group.
Conclusion: A prognostic model for predicting 28-day mortality in sepsis patients with autoimmune diseases demonstrated robust predictive performance and clinical applicability upon internal and external validation.
Keywords: sepsis, LASSO regression, Boruta algorithm, dynamic nomogram, MIMIC-IV