Can machine learning support infection control measures by predicting carbapenemase-producing Enterobacterales colonization at admission?

BACKGROUND: Early identification of patients with carbapenemase-producing Enterobacterales (CPE) colonization is crucial for infection control; however, microbiological testing may delay detection and be costly. Machine learning may enhance predictive analytics for timely identification of at-risk patients. METHODS: Four machine learning models: Decision Tree, Random Forest, Gradient Boosting, and XGBoost, were used to predict CPE colonization within 48 hours of admission using microbiological and demographic data. Model performance was assessed through sensitivity, specificity, and area under the receiver operating characteristic curve (AUROC). Uniform Manifold Approximation and Projection (UMAP) evaluated topological separability of CPE-positive cases and CPE-negative controls. RESULTS: From January 1, 2015 to December 31, 2024, 453,372 fecal specimens were submitted for CPE screening, with 194,917 (43.0%) collected within 48 hours of admission, comprising 3,328 CPE-positive cases (1.7%) and 191,589 CPE-negative controls. The Gradient Boosting classifier showed the best performance, achieving an AUROC of 0.598, sensitivity of 54.4%, and specificity of 59.1%. Demographic factors (age ≥ 75 and male sex), history of hospitalization, and known CPE colonization in the past year, and admission specialty (general medicine and general surgery) were consistently included in all models as top predictors. UMAP revealed significant overlap between CPE-positive and CPE-negative patients, indicating challenges in differentiating the risk profiles. CONCLUSIONS: This study highlights the complexities of using machine learning to predict CPE colonization within 48 hours of admission. The low AUROC values suggest that the models may not effectively predict CPE colonization at the patient level, potentially due to inherent rarity of events and overlapping risk profiles.