Development and validation of a machine-learning model for predicting the risk of death in sepsis patients with acute kidney injury

The mortality rate of patients with sepsis-induced acute kidney injury (S-AKI) is notably elevated. The initial categorization of prognostic indicators has a beneficial impact on elucidating and enhancing disease outcomes. This study aimed to predict the mortality risk of S-AKI patients by employing machine learning techniques. The sample size determined by a four-step procedure yielded 1508 samples. The research design necessitated the inclusion of individuals with S-AKI from the Medical Information Mart for Intensive Care (MIMIC)-IV database. The patients were initially admitted to the Intensive Care Unit (ICU) for their hospital stay. Additionally, these patients (aged from 18 to 89 years old) had encountered S-AKI on the day of their admittance. Forty-two predictive factors were analyzed, with hospitalization death as the outcome variable. The training set (4001 cases) consisted of 70% of the participants, and the remaining (1714 cases) participants were allocated to the validation set. Furthermore, an additional validation set (MIMIC-III) consisted of 1757 patients from the MIMIC-III database. Moreover, an external validation set from the Intensive Care Department of Beijing Friendship Hospital (BFH) comprised 72 patients. Six machine learning models were employed in the prediction, namely the logistic, lasso, rpart, random forest, xgboost, and artificial neural network models. The comparative efficacy of the newly developed model in relation to the APACHE II model for predicting mortality risk was also assessed. The XGBoost model exhibited a superior performance with the training set. With the internal validation set and the two external validation sets (MIMIC-III and BFH), the xgboost algorithm demonstrated the highest performance. Meanwhile, APACHE II performed poorly at predicting the mortality risk with the BFH validation set. The mortality risk was influenced by three primary clinical parameters: urine volume, lactate, and Glasgow Coma Scale (GCS) score. Thus, we developed a prediction model for the risk of death among S-AKI patients that has an improved performance compared to previous models and is a potentially valuable tool for S-AKI prediction and treatment in the clinic.