Predicting future hospital antimicrobial resistance prevalence using machine learning

Abstract Objectives Predicting antimicrobial resistance (AMR), a top global health threat, nationwide at a hospital level could help target interventions. Using machine learning, we exploit historical AMR and antimicrobial usage to predict future AMR. Methods Antimicrobial use and AMR prevalence in bloodstream infections in hospitals in England were obtained per hospital group (Trust) and financial year (FY, April-March) for 22 pathogen-antibiotic combinations (FY2016-2017-FY2021-2022). XGBoost model predictions were compared in a to previous value taken forwards, difference between the previous two years taken forwards and linear trend forecasting (LTF). XGBoost feature importances were to aid interpretability. Results Relatively limited year-to-year variability in AMR prevalence within Trust-pathogen-antibiotic combinations meant previous value taken forwards achieved a low mean absolute error (MAE). XGBoost models performed similarly, while difference between the previous two years taken forwards and LTF were consistently worse. XGBoost considerably outperformed all other methods in Trusts with a larger change in AMR prevalence from FY2020-2021 (last training year) to FY2021-2022 (held-out test set). Feature importance values indicated that complex relationships were exploited for predictions. Conclusion Year-to-year resistance has generally changed little within Trust-pathogen-antibiotic combinations. In those with larger changes, XGBoost models could improve predictions, enabling informed decisions, efficient resource allocation, and targeted interventions. ### Competing Interest Statement DWE declares lecture fees from Gilead, outside the submitted work. All other authors report no potential conflicts of interest. ### Funding Statement This work was supported by the National Institute for Health Research Health Protection Research Unit (NIHR HPRU) in Healthcare Associated Infections and Antimicrobial Resistance at Oxford University in partnership with the UK Health Security Agency (NIHR200915), and the NIHR Biomedical Research Centre, Oxford. D. W. E. is a Big Data Institute Robertson Fellow. A. S. W. is an NIHR Senior Investigator. D. A. C. was supported by the Pandemic Sciences Institute at the University of Oxford; the National Institute for Health Research (NIHR) Oxford Biomedical Research Centre (BRC); an NIHR Research Professorship; a Royal Academy of Engineering Research Chair; and the InnoHK Hong Kong Centre for Centre for Cerebro-cardiovascular Engineering (COCHE). The views expressed are those of the authors and not necessarily those of the NHS, the NIHR, the Department of Health or the UK Health Security Agency. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes A subset of the antibiotic resistance dataset is available through UKHSA's online data service, Fingertips(35). Information on the use of antibiotics in secondary care was obtained from IQVIA (formerly QuintilesIMS, formed from the merger of IMS Health and Quintiles). All IQVIA data used retains IQVIA Solutions UK Limited and its affiliates Copyright. All rights reserved. Use of IQVIA data for sales, marketing or any other commercial purposes is not permitted without IQVIA Solutions UK Limited's approval, expressed by IQVIA's Terms of Use.