Development of Machine Learning–Based Models to Predict Mortality in Intensive Care Unit Patients with Sepsis
Abstract
Objective This study aimed to develop and validate a predictive model using machine learning to estimate mortality among intensive care unit patients. Methods The medical records of 874 sepsis patients hospitalized at the Affiliated Hospital of Chengde Medical University from 2021 to 2024 were retrospectively analyzed. Sepsis patients were randomly divided into training and validation sets in a 7:3 ratio. We constructed mortality prediction models for sepsis patients using machine learning algorithms, including extreme gradient boosting (XGBoost), logistic regression (LR), random forest (RF), adaptive boosting (AdaBoost), k-nearest neighbors (KNN), support vector machine (SVM), multilayer perceptron (MLP), and Gaussian naive Bayes (GNB). The predictive performance of the machine learning models was assessed using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). This study was reported in accordance with the RECORD (REporting of studies Conducted using Observational Routinely collected health Data) guidelines. Results A total of 874 patients with sepsis were included, of whom 338 died and 536 survived. Significant differences were observed between the mortality and survival groups with respect to platelet count (PLT), platelet distribution width (PDW), platelet distribution width to count ratio (PCR), mean platelet volume (MPV), monocyte count (Mono), albumin (Alb) level, total bilirubin (Tbil), alanine aminotransferase (ALT), aspartate aminotransferase (AST), lactate dehydrogenase (LDH), serum creatinine (Scr), blood urea nitrogen (BUN), fibrinogen (Fib) level, D-dimer level, lactate (Lac) level, respiratory system infection, gastrointestinal system infection, and APACHEII score. The ROC curve analysis showed that the areas under the curve (AUC) of the XGBoost, LR, RF, AdaBoost, KNN, SVM, MLP and GNB models in predicting the in-hospital mortality rate of sepsis patients were 0.954, 0.880, 0.951, 0.901, 0.859, 0.910, 0.836 and 0.854, respectively. Among the nine algorithms, the XGboost model performed significantly better than the others, achieving an accuracy of 0.879, a sensitivity of 0.734, and an F1 score of 0.821. The calibration curve demonstrated that the XGBoost model showed the best performance among the eight algorithms, with predictions closely matching the observed outcomes. Decision curve analysis indicated that the XGBoost model outperformed both extreme strategies. Conclusion Machine learning models provide a reliable approach for predicting in-hospital mortality in patients with sepsis. Among them, the XGboost model demonstrated the highest predictive performance, aiding clinicians in identifying high-risk patients with sepsis and implementing early interventions to reduce mortality.