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已发表论文
基于常规诊断后临床数据的脓毒症患者早期死亡率预测的可解释机器学习模型：一项多中心研究
Authors Sun W , Zhang L, Mou D, Zhao B, Che Z, Li Y, Wang S
Received 11 July 2025
Accepted for publication 19 October 2025
Published 28 October 2025 Volume 2025:18 Pages 15003—15015
DOI https://doi.org/10.2147/JIR.S553042
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 2
Editor who approved publication: Dr Anh Ngo
Wenwu Sun,1,* Lijuan Zhang,2,* Dan Mou,3,* Bing Zhao,4,* Zaiqian Che,4 Yang Li,1 Shu Wang3
1Department of Emergency Medicine, Daping Hospital, Army Medical University, State Key Laboratory of Trauma and Chemical Poisoning, Chongqing, 400042, People’s Republic of China; 2Intensive Care Unit, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430014, People’s Republic of China; 3Department of Intensive Care Medicine, Chongqing University Central Hospital, Chongqing Emergency Medical Center, Chongqing, 400016, People’s Republic of China; 4Department of Emergency, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, People’s Republic of China
*These authors contributed equally to this work
Correspondence: Shu Wang, Department of Intensive Care Medicine, Chongqing University Central Hospital, Chongqing Emergency Medical Center, Chongqing, 400016, People’s Republic of China, Tel/Fax +86 13996583283, Email wangshusww@163.com Yang Li, Department of Emergency Medicine, Daping Hospital, Army Medical University, State Key Laboratory of Trauma and Chemical Poisoning, Chongqing, 400042, People’s Republic of China, Tel/Fax +86 02368819999, Email dpliyang@tmmu.edu.cn
Background: Early identification of high-risk patients is crucial for improving outcomes. This study aims to develop and validate a machine learning (ML) model to predict early 7-day mortality in sepsis patients based on routine clinical data obtained immediately after diagnosis.
Methods: Data were collected from four tertiary hospitals across diverse regions in China. Seven ML algorithms were employed to construct the prediction model. Model performance was evaluated using Area Under the Receiver Operating Curve (AUROC), calibration curves, Decision Curve Analysis (DCA), and clinical application. The SHapley Additive exPlanations (SHAP) method was used to interpret the model and identify key predictors.
Results: Among 8729 patients, 752 (8.6%) died within 7 days after admission. The Artificial Neural Network (ANN) model demonstrated superior predictive performance, achieving an AUROC of 0.767 (95% CI: 0.748– 0.787) in training set, outperforming traditional scoring systems such as APACHE II (AUROC: 0.710, 95% CI: 0.698– 0.721) and SOFA (AUROC: 0.718, 95% CI: 0.707– 0.729). This performance was consistent in the test set. Key predictors of early mortality included Glasgow Coma Scale (GCS), blood chloride, and albumin levels. The SHAP analysis provided interpretable insights into the model.
Conclusion: We developed a machine learning model to predict the risk of early 7-day mortality in sepsis patients based on routine clinical data obtained immediately after diagnosis and validated its potential as a clinically reliable tool, achieving an AUROC of 0.767 in the training set. The use of SHAP-based interpretation enhances model interpretability, enabling clinicians to better understand the factors influencing mortality, identify high-risk patients early, and implement timely interventions to improve outcomes.
Keywords: sepsis, early mortality, machine learning, predictive models, SHAP, critical care
1Department of Emergency Medicine, Daping Hospital, Army Medical University, State Key Laboratory of Trauma and Chemical Poisoning, Chongqing, 400042, People’s Republic of China; 2Intensive Care Unit, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430014, People’s Republic of China; 3Department of Intensive Care Medicine, Chongqing University Central Hospital, Chongqing Emergency Medical Center, Chongqing, 400016, People’s Republic of China; 4Department of Emergency, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, People’s Republic of China
*These authors contributed equally to this work
Correspondence: Shu Wang, Department of Intensive Care Medicine, Chongqing University Central Hospital, Chongqing Emergency Medical Center, Chongqing, 400016, People’s Republic of China, Tel/Fax +86 13996583283, Email wangshusww@163.com Yang Li, Department of Emergency Medicine, Daping Hospital, Army Medical University, State Key Laboratory of Trauma and Chemical Poisoning, Chongqing, 400042, People’s Republic of China, Tel/Fax +86 02368819999, Email dpliyang@tmmu.edu.cn
Background: Early identification of high-risk patients is crucial for improving outcomes. This study aims to develop and validate a machine learning (ML) model to predict early 7-day mortality in sepsis patients based on routine clinical data obtained immediately after diagnosis.
Methods: Data were collected from four tertiary hospitals across diverse regions in China. Seven ML algorithms were employed to construct the prediction model. Model performance was evaluated using Area Under the Receiver Operating Curve (AUROC), calibration curves, Decision Curve Analysis (DCA), and clinical application. The SHapley Additive exPlanations (SHAP) method was used to interpret the model and identify key predictors.
Results: Among 8729 patients, 752 (8.6%) died within 7 days after admission. The Artificial Neural Network (ANN) model demonstrated superior predictive performance, achieving an AUROC of 0.767 (95% CI: 0.748– 0.787) in training set, outperforming traditional scoring systems such as APACHE II (AUROC: 0.710, 95% CI: 0.698– 0.721) and SOFA (AUROC: 0.718, 95% CI: 0.707– 0.729). This performance was consistent in the test set. Key predictors of early mortality included Glasgow Coma Scale (GCS), blood chloride, and albumin levels. The SHAP analysis provided interpretable insights into the model.
Conclusion: We developed a machine learning model to predict the risk of early 7-day mortality in sepsis patients based on routine clinical data obtained immediately after diagnosis and validated its potential as a clinically reliable tool, achieving an AUROC of 0.767 in the training set. The use of SHAP-based interpretation enhances model interpretability, enabling clinicians to better understand the factors influencing mortality, identify high-risk patients early, and implement timely interventions to improve outcomes.
Keywords: sepsis, early mortality, machine learning, predictive models, SHAP, critical care