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

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.