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<p>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&#xa0;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.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月30日

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  • Ji Hun Kim,
  • Eunmi Yang,
  • Yun Woo Lee,
  • Han-Sung Kim,
  • Hyeonji Seo

摘要

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.