Early Prediction of Mortality due to Carbapenem-Resistant Gram-Negative Bacterial Infection in Intensive Care Units Using Machine Learning
Buket Baddal, Cemile Bağkur, Bardia Arman · Cyprus Journal of Medical Sciences · 2025
BACKGROUND/AIMS:The occurrence of hospital-acquired infections due to carbapenem-resistant Gram-negative bacteria (CR-GNB) is on the rise globally.Studies show that infections with multidrug-resistant Gram-negative bacteria are associated with high mortality mainly in intensive care units (ICUs).This study aims to develop machine learning (ML) algorithms to identify variables correlated with mortality and construct a prediction model for ICU mortality due to CR-GNB infections. MATERIALS AND METHODS:Data from patients admitted to a private hospital between 2016 and 2023 were included.The dataset included patients from the ICU who had a positive culture of CR-GNB after 3 days of admission (n=788).Demographic data, vital signs, important blood test indicators, intubation, and catheterization history were collected.The proposed models included a classifier and a mortality prediction system, utilizing seven ML algorithms: Extreme Gradient Boosting (XGBoost), logistic regression, random forest (RF), k-nearest neighbors, support vector machine, naive bayes, and decision tree (DT).RESULTS: Analysis showed that blood C-reactive protein, urea, creatinine, platelet-large cell ratio, along with patient age and presence of endotracheal intubation, were strong predictors of mortality in ICU patients.In terms of accuracy, XGBoost (96.2%) outperformed RF (93.7%) and DT (91.8%).The area under the receiver operating characteristic curve for these models was 0.98, 0.99, and 0.93, while F1 scores were 0.97, 0.95, and 0.94, respectively.CONCLUSION: ML prediction models can predict patient mortality in ICUs due to CR-GNB and guide medical staff to identify high-risk groups in advance.