A machine learning-driven multitask framework for antibiotic resistance prediction in Escherichia coli, Klebsiella pneumoniae, and Enterobacter cloacae bloodstream infections among hematological malignancy patients.

BACKGROUND: Inappropriate empirical antimicrobial therapy (IEAT) significantly increases mortality in patients with resistant Gram-negative bacteremia. We developed a multitask machine learning framework to predict carbapenem resistance (CR), β-lactam/β-lactamase inhibitor combinations resistance (BL/BLI-R), and third-/fourth-generation cephalosporins resistance (3GC/4GC-R) in HM patients with monomicrobial BSIs caused specifically by Escherichia coli, Klebsiella pneumoniae, or Enterobacter cloacae. METHODS: Using retrospective data from 1,353 HM patients with three specific Enterobacterales BSIs (2017-2023), we trained support vector machines, eXtreme Gradient Boosting, and logistic regression models through 5-fold cross-validation with hyperparameter tuning and conducting internal validation via bootstrap. Model thresholds were optimized via Pareto front analysis to minimized IEAT and carbapenem use while maximizing sensitivity. RESULTS: The models achieved AUCs of 0.81 (95% CI, 0.78-0.85) for CR model, 0.81 (95% CI, 0.77-0.84) for BL/BLI-R model, and 0.72 (95% CI, 0.68-0.76) for 3GC/4GC-R model before optimization. Threshold optimization improved sensitivity for CR prediction increased from 0.10 to 0.77, for BL/BLI-R prediction from 0.19 to 0.94, and for 3GC/4GC-R prediction from 0.54 to 0.98. SHAP analysis identified key predictors including prolonged neutropenia, prior carbapenem exposure, and tumor consolidation stage. Integrating pathogen species enhanced 3GC/4GC-R prediction (AUC 0.72 vs. 0.76). Clinically, by employing the threshold optimization strategy, the model successfully reduced empirical carbapenem use from 71.7% to 32.1% without increasing the rate of IEAT compared to clinical practice (4.0% vs. 6.11%). CONCLUSION: This multitask framework supports resistance prediction in HM patients with monomicrobial BSIs due to three specific Enterobacterales species, mainly used to assist clinical decision-making after pathogen identification and strongly suspected infection with one of the three pathogens. Prospective validation is required before clinical application.