Machine learning to predict antibiotic susceptibility in Enterobacterales bloodstream infections compared to clinician prescribing

Background. Patients with Gram-negative bloodstream infections are at risk of serious adverse outcomes without active treatment, but identifying who has antimicrobial resistance (AMR) to target empirical treatment is challenging. Methods. We used XGBoost machine learning models to predict the presence of antimicrobial resistance to seven antibiotics in patients with Enterobacterales bloodstream infection. Models were trained using hospital and community data available at the time blood cultures were obtained from Oxfordshire, UK, between 01-January-2017 and 31-December-2021. Model performance was compared to final microbiology results using test datasets from 01-January-2022 to 31-December-2023 and with clinicians' prescribing. Findings. 4709 infection episodes were used for model training and evaluation; antibiotic resistance rates ranged from 7-67%. In held-out test data, resistance prediction performance was similar for the seven antibiotics (AUCs 0.680 [95%CI 0.641-0.720] to 0.737 [0.674-0.797]). Performance improved for most antibiotics when species data were included as model inputs (AUCs 0.723 [0.652-0.791] to 0.827 [0.797-0.857]). In patients treated with a beta-lactam, clinician prescribing led to 70% receiving an active beta-lactam: 44% were over-treated (broader spectrum treatment than needed), 26% optimally treated (narrowest spectrum active agent), and 30% under-treated (inactive beta-lactam). Model predictions without species data could have led to 79% of patients receiving an active beta-lactam: 45% over-treated, 34% optimally treated, and 21% under-treated. Interpretation. Predicting AMR in bloodstream infections is challenging for both clinicians and models. Despite modest performance, machine learning models could still increase the proportion of patients receiving active empirical treatment by up to 9% over current clinical practice in an environment prioritising antimicrobial stewardship. Funding. National Institute of Health Research (NIHR) Oxford Biomedical Research Centre, NIHR Health Protection Research Unit in Healthcare-associated Infection and Antimicrobial Resistance.