Evaluate prognostic accuracy of SOFA component score for mortality among adults with sepsis by machine learning method

作者：Xiaobin Pan, Jinbao Xie, Lihui Zhang, Xincai Wang, Shujuan Zhang, Yingfeng Zhuang, Xingsheng Lin, Songjing Shi, Songchang Shi, Wei Lin · 发表于：BMC Infectious Diseases · 年份：2023 · DOI：10.1186/s12879-023-08045-x · 被引用次数：37 · 研究领域：Sepsis Diagnosis and Treatment、Inflammation biomarkers and pathways、Neonatal and Maternal Infections

INTRODUCTION: Sepsis has the characteristics of high incidence, high mortality of ICU patients. Early assessment of disease severity and risk stratification of death in patients with sepsis, and further targeted intervention are very important. The purpose of this study was to develop machine learning models based on sequential organ failure assessment (SOFA) components to early predict in-hospital mortality in ICU patients with sepsis and evaluate model performance. METHODS: Patients admitted to ICU with sepsis diagnosis were extracted from MIMIC-IV database for retrospective analysis, and were randomly divided into training set and test set in accordance with 2:1. Six variables were included in this study, all of which were from the scores of 6 organ systems in SOFA score. The machine learning model was trained in the training set and evaluated in the validation set. Six machine learning methods including linear regression analysis, least absolute shrinkage and selection operator (LASSO), Logistic regression analysis (LR), Gaussian Naive Bayes (GNB) and support vector machines (SVM) were used to construct the death risk prediction models, and the accuracy, area under the receiver operating characteristic curve (AUROC), Decision Curve Analysis (DCA) and K-fold cross-validation were used to evaluate the prediction performance of developed models. RESULT: A total of 23,889 patients with sepsis were enrolled, of whom 3659 died in hospital. Three feature variables including rena...