Early risk stratification for carbapenem resistance among

Introduction: To address the delayed identification of carbapenem-resistant Methods: Data from 1,666 patients with Results: LASSO identified seven predictors: sex, age, mean corpuscular volume (MCV), hemoglobin (HGB), platelet-to-lymphocyte ratio (PLR), systemic inflammatory response index (SIRI), and intensive care unit (ICU) admission status. Among the evaluated algorithms, the random forest (RF) model achieved the best discrimination. The training area under the receiver operating characteristic curve (AUC) was 0.993; it achieved an average 5-fold cross-validation AUC of 0.929 ± 0.005. In the internal test set, it achieved an AUC of 0.837 (95% CI: 0.779-0.893), specificity of 0.972, and sensitivity of 0.507, with excellent calibration (Brier score = 0.084). The model retained strong performance externally (AUC: 0.898, specificity: 0.985, sensitivity: 0.600, Brier score: 0.073). SHAP analysis indicated that HGB was the most influential feature, inversely associated with CRPA risk. Decision curve analysis supported the clinical utility across threshold probabilities ranging from 15% to 65%. Discussion: This clinlabomics-based RF model provides a rapid, low-cost adjunct for early CRPA stratification. Given its exceptionally high specificity (>0.97) and modest sensitivity, it functions exclusively as a reliable clinical "rule-in" tool. Positive predictions can confidently guide early targeted therapy and strict infection control. However, negative predictions cannot safely rule out CRPA, emphasizing its role alongside standard empirical practices rather than as a standalone screening instrument.