Interpretable machine learning for early detection of carbapenem-resistant

Background: Carbapenem-resistant Methods: This single-center retrospective cohort study included 401 ICU patients with culture-confirmed Results: CRKP isolates accounted for 15.7% (63/401) of cases. LASSO regression identified nine predictors: procalcitonin (PCT), specimen type, prior MDR infection, prior carbapenem exposure, history of stroke, APACHE II score, white blood cell count, age, and hemoglobin. In the independent validation set, the XGBoost model achieved an AUC of 0.852 (95% CI: 0.745-0.959), with a sensitivity of 0.737, specificity of 0.891, accuracy of 0.867, and an F1-score of 0.636. The model demonstrated good calibration (Brier score: 0.088) and provided a net clinical benefit across a wide range of risk thresholds. SHAP analysis highlighted PCT, specimen source (blood), and prior resistance-related exposures as the most influential predictors. Conclusion: The integration of LASSO feature selection with the XGBoost algorithm, utilizing only routine clinical data, generates a reliable early-warning model for CRKP infection risk prior to the availability of susceptibility reports. This tool shows promising discriminative ability and calibration, offering potential to guide empirical therapy and support antimicrobial stewardship. Future multicenter prospective studies are warranted to validate its generalizability and real-world clinical impact.