ArticleInfection and drug resistance2026
Development and Validation of an Interpretable Machine Learning Model for Predicting Carbapenem-Resistant
Article in Infection and drug resistance, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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7 authors.
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Abstract
Objective: This study aimed to develop and validate an interpretable machine learning model for early prediction of carbapenem-resistant Methods: A retrospective study was conducted on 285 patients with culture-confirmed Results: Seven core predictors (HGB, LYMPH_p, NEUT_p, MONO, BASO, PCT, AISI) were identified. The random forest model achieved the best performance, with an AUC of 0.863 (95% CI: 0.672-0.980), accuracy of 0.821, sensitivity of 0.750 (95% CI: 0.571-1.000), specificity of 0.834 (95% CI: 0.643-0.841), and Brier score of 0.077. DCA showed clear net benefit in the threshold probability range of 0.1-0.8. SHAP analysis confirmed aggregate index of systemic inflammation (AISI) as the most important predictive factor. Conclusion: The interpretable prediction model based on routine peripheral blood indicators demonstrated good discrimination, calibration, and clinical utility in this single-center retrospective study. It has the potential to assist early risk stratification of CRKP infection and support clinical decision-making. External multicenter prospective validation is warranted before clinical implementation.
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