Machine learning for the prediction of all-cause mortality in patients with sepsis-associated acute kidney injury during hospitalization

作者：Hongshan Zhou, Leping Liu, Qinyu Zhao, Xin Jin, Zhangzhe Peng, Wei Wang, Ling Huang, Yanyun Xie, Hui Xu, Lijian Tao, Xiangcheng Xiao, Wannian Nie, Fang Liu, Li Li, Qiongjing Yuan · 发表于：Frontiers in Immunology · 年份：2023 · DOI：10.3389/fimmu.2023.1140755 · 被引用次数：50 · 研究领域：Acute Kidney Injury Research、Sepsis Diagnosis and Treatment、Chronic Kidney Disease and Diabetes

Background: Sepsis-associated acute kidney injury (S-AKI) is considered to be associated with high morbidity and mortality, a commonly accepted model to predict mortality is urged consequently. This study used a machine learning model to identify vital variables associated with mortality in S-AKI patients in the hospital and predict the risk of death in the hospital. We hope that this model can help identify high-risk patients early and reasonably allocate medical resources in the intensive care unit (ICU). Methods: A total of 16,154 S-AKI patients from the Medical Information Mart for Intensive Care IV database were examined as the training set (80%) and the validation set (20%). Variables (129 in total) were collected, including basic patient information, diagnosis, clinical data, and medication records. We developed and validated machine learning models using 11 different algorithms and selected the one that performed the best. Afterward, recursive feature elimination was used to select key variables. Different indicators were used to compare the prediction performance of each model. The SHapley Additive exPlanations package was applied to interpret the best machine learning model in a web tool for clinicians to use. Finally, we collected clinical data of S-AKI patients from two hospitals for external validation. Results: , minimum creatinine, minimum Glasgow Coma Scale, and diagnosis of diabetes and stroke. The categorical boosting algorithm model presented significantly ...