Comparison and validation of multiple machine learning algorithms for predicting MDRO infection in catheter-related bloodstream patients: a multicenter cohort study.

Early identification of patients at high risk for multidrug-resistant organism (MDRO) infection in catheter-related bloodstream infection (CRBSI) is crucial for precise antimicrobial therapy. This study aimed to develop and externally validate a machine learning (ML) model to predict this risk, thereby supporting early clinical intervention. Patients with CRBSI were extracted from the Medical Information Mart for Intensive Care IV database and classified into MDRO and non-MDRO groups based on microbiological culture and antimicrobial susceptibility testing. Missing data from 51 clinical variables were handled using Random Forest-based multiple imputation. Ten predictive features were selected by integrating correlation heatmap analysis, variance inflation factor, and least absolute shrinkage and selection operator regression. Eight ML models, including XGBoost and Random Forest, were constructed and tuned via hyperparameter optimization. The optimal model was selected primarily using the area under the receiver operating characteristic curve (AUC), supplemented by the F1-score, Brier score, accuracy, and recall. Its performance was further evaluated using a confusion matrix and calibration curve. External validation was performed on a real-world multi-center cohort ( IMPORTANCE: Catheter-related bloodstream infection (CRBSI) complicated by multidrug-resistant organism (MDRO) is associated with high mortality and treatment failure. The critical delay in conventional microbiological diagnosis often necessitates empirical broad-spectrum antibiotics, exacerbating antimicrobial resistance. Our study develops and validates an interpretable machine learning model using readily available clinical variables to accurately predict the risk of MDR-CRBSI at an early stage. This tool addresses a pressing clinical need by enabling timely, targeted antimicrobial therapy, thereby potentially improving patient outcomes and supporting antimicrobial stewardship efforts in the global fight against resistance.