Prognostic analysis of sepsis-related liver injury and development of a prediction model based on machine learning method
- VernacularTitle:脓毒症相关肝损伤预后分析及基于机器学习方法的预测模型建立
- Author:
Yun ZHAO
1
;
Wei JIANG
;
Ruiqiang ZHENG
;
Jiangquan YU
Author Information
- Keywords: sepsis; sepsis-related liver injury; machine learning algorithms; prediction mod-el; classification and regression tree; random forest; support vector machine; naive Bayes method
- From: Journal of Clinical Medicine in Practice 2025;29(7):32-37,42
- CountryChina
- Language:Chinese
- Abstract: Objective To analyze the prognosis of patients with sepsis-related liver injury(SRLI)and establish a prediction model for the occurrence of SRLI after ICU admission in sepsis patients u-sing eight machine learning methods.Methods Patients who met the sepsis diagnostic criteria and had no underlying liver or biliary diseases were included from the MIMIC-Ⅳ database,and were clas-sified into SRLI and non-SRLI groups based on liver enzymes ≥ 5 times the upper limit of normal(ULN)or bilirubin ≥2.Omg/dL.Chi-square test,multivariate Logistic regression analysis,and pro-pensity score matching were used to analyze the mortality risk between the two groups.Eight machine learning algorithms[Logistic regression,classification and regression tree(CART),random forest(RF),support vector machine(SVM),K-nearest neighbors(K-NN),naive Bayes method(NBM),extreme gradient boosting(XGBoost),and gradient boosting decision tree(GBDT)]were employed to construct and validate the SRLI prediction model.Results The chi-square test(P＜0.001),multivariate Logistic regression analysis(P＜0.05),and log-rank test after propensity score matching(P＜0.05)all indicated that SRLI increased the mortality risk of patients.Among the SRLI prediction models,RF algorithm had the highest area under the curve(AUC),with its value of 0.866,followed by GBDT(AUC=0.862),Logistic regression(AUC=0.859),SVM(AUC=0.837),NBM(AUC=0.830),CART(AUC=0.771),XGBoost(AUC=0.764),and K-NN(AUC=0.722).Conclusion SRLI increases the mortality risk of patients.The prediction model construc-ted using the RF algorithm has high diagnostic value.
