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

BackgroundPatients 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. MethodsWe 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. Findings4709 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. InterpretationPredicting 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. FundingNational Institute of Health Research (NIHR) Oxford Biomedical Research Centre, NIHR Health Protection Research Unit in Healthcare-associated Infection and Antimicrobial Resistance. Research in context Evidence before this studyWe searched Pubmed and Google Scholar using the terms: [antibiotic OR antimicrobial] AND [resistance] AND [prediction OR machine learning OR AI OR artificial intelligence] for articles published up to 31 August 2024. References and citations for articles identified were also reviewed. Several studies have shown that machine learning can potentially be used to predict antimicrobial resistance (AMR) subsequently identified on phenotypic antimicrobial susceptibility testing. Most have focused either on identifying resistance in urinary tract infection, or in all samples received by a microbiology laboratory, which are often dominated by urine cultures. Only two studies were identified focusing specifically on bloodstream infection, and these only investigated a limited number of antibiotics. Overall, prediction performance was typically modest, e.g. area under the receiver operating curve (AUC) values of 0.65-0.75. Most studies focus on data available in the community or hospital but not both. Four studies retrospectively compared clinical prescribing to model predictions and showed models could potentially reduce inappropriate antibiotic use, but none focused specifically on bloodstream infection. External validation of models is uncommon, and most studies do not cover how models can be updated over time or to new locations. Added value of this studyWe developed machine learning models to predict resistance to seven antibiotics (amoxicillin, co-amoxiclav, ceftriaxone, piperacillin-tazobactam, ciprofloxacin, co-trimoxazole, and gentamicin) in bloodstream infections caused by Enterobacterales species. We focused on this clinical syndrome as it is an important cause of AMR-associated mortality. We used data from Oxfordshire, UK, between January 2017 and December 2023 for model training and evaluation (4709 infection episodes in 4243 patients). In held-out test data, predictive 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]). AMR identified in recent microbiology results was the most important predictor of resistance. Model performance was relatively consistent over time. AMR prediction was also challenging for clinicians: their implied sensitivity for detecting resistance, i.e., the proportion of patients treated with a beta-lactam with resistance receiving active treatment was 97% for amoxicillin, 29% for co-amoxiclav, 19% for ceftriaxone, and 6% for piperacillin-tazobactam. 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 information could have led to 79% of patients receiving an active beta-lactam: 45% over-treated, 34% optimally treated, and 21% under-treated. Implications of all the available evidenceDespite considering a wide range of input features, including hospital and some community data, model performance was broadly consistent with what has been described previously for similar tasks. This suggests there is a potential ceiling on the performance of machine learning in this context. However, despite modest performance, machine learning models could still increase the proportion of patients receiving active treatment by up to 9% over current clinical practice in an environment prioritising antimicrobial stewardship.