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Development and Validation of an Interpretable Machine Learning Model for Predicting Carbapenem-Resistant Klebsiella
Mingda Wu1,2, Naijin Wei1, Zheng Zhang1
1School of Clinical Medicine, Changchun University of Chinese Medicine, Changchun, Jilin, People's Republic of China.
Objective:
This study aimed to develop and validate an interpretable machine learning model for early prediction of carbapenem-resistant Klebsiella pneumoniae (CRKP) infection using routine peripheral blood indicators, providing a rapid risk assessment tool before antimicrobial susceptibility results.
Methods:
A retrospective study was conducted on 285 patients with culture-confirmed Klebsiella pneumoniae infection (242 carbapenem-susceptible Klebsiella pneumoniae (CSKP), 43 CRKP), with all isolates obtained from sputum specimens, from June 2024 to June 2025. Given the significant class imbalance between the CSKP (n = 242) and CRKP (n = 43) groups, key features were screened by LASSO regression and Boruta algorithm. Six machine learning models were constructed, with synthetic minority oversampling technique (SMOTE) for class imbalance processing and stratified 5-fold cross-validation for optimization. Model performance was evaluated by area under the curve (AUC), accuracy, calibration curve, decision curve analysis (DCA), and interpreted by SHapley Additive exPlanations (SHAP).
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.