Early prediction of colonization by carbapenemase-producing enterobacterales at ICU admission using machine learning.

Colonization by carbapenemase-producing Enterobacterales (CPE) on admission to an intensive care unit (ICU) poses a serious threat to infection control. Early detection is critical but remains challenging in real-world settings. We aimed to develop interpretable machine learning models for predicting CPE colonization at ICU admission to support clinical decision-making for early isolation of CPE carriers. We conducted a retrospective cohort study of adult ICU admissions at a tertiary hospital in South Korea from January 2022 to December 2023. CPE colonization was defined by rectal swab culture within 48 h of admission. Forty-two candidate variables were extracted from electronic medical records, and ten machine learning algorithms were evaluated. Of 4,915 ICU admissions, 453 (9.2%) were colonized with CPE at admission. Twelve predictors were retained for model development, including antibiotic exposure, device use, and medical condition. Logistic regression at a threshold of 0.45 achieved the best-balanced performance with a sensitivity of 0.73, an ROC-AUC of 0.77, and a negative predictive value of 0.96. A web-based CPE prediction tool was developed based on the model; this enables clinicians to enter the 14 selected variables at ICU admission and instantly obtain an estimated risk of CPE colonization. Our machine learning-based tool for predicting CPE colonization at ICU admission appears to hold promise as a rule-out aid for CPE carriage.