Machine-learning-based risk assessment tool to rule out empirical use of ESBL-targeted therapy in endemic areas

Abstract

Antimicrobial stewardship focuses on identifying patients who require ESBL-targeted therapy. Rule-in tools have been extensively researched in areas of low endemicity; however, such tools are inadequate for areas with high rates of ESBL, as almost all patients will be selected. To develop a machine learning-based rule-out tool suitable for areas with high levels of resistance. We used gradient boosted decision trees to train and validate a risk prediction model on data from 17,913 (45% ESBL) patients with Escherichia coli and Klebsiella pneumoniae in urine cultures. We evaluated the predictive power of different sets of variables, using Shapley values to evaluate variable contributions. Our model successfully identified patients with low risk of ESBL resistance in ESBL-endemic areas (AUC-ROC 0.72). When used to select the 30% of patients with the lowest predicted risk, the model yielded a negative predictive value ≥ 0.74. We also demonstrated that a model with seven input features can perform nearly as well as our full model. This simplified model is freely accessible as a web application. Our study demonstrates that a risk calculator for antibiotic resistance can be a viable rule-out strategy to reduce ESBL-targeted therapy usage in ESBL-endemic areas. Robust performance of a model with only limited features makes the clinical use of such a tool feasible. In an era with growing rates of ESBL where some experts have called for empirical use of carbapenems as first-line therapy for all patients in high-ESBL-prevalence areas, our tool provides an important alternative.