Yuan, Kevin;
Luk, Augustine;
Walker, Ann;
Zhu, Tingting;
Eyre, David W;
(2025)
Machine learning and clinician predictions of antibiotic resistance in Enterobacterales bloodstream infections.
Journal of Infection
, 90
(2)
, Article 106388. 10.1016/j.jinf.2024.106388.
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Abstract
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 antimicrobial resistance to seven antibiotics in patients with Enterobacterales bloodstream infection. Models were trained using hospital and community data from Oxfordshire, UK, for patients with positive blood cultures between 01-January-2017 and 31-December-2021. Model performance was evaluated by comparing predictions to final microbiology results in test datasets from 01-January-2022 to 31-December-2023 and to 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 identifications (available ∼24 h later) 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. CONCLUSIONS: 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.
| Type: | Article |
|---|---|
| Title: | Machine learning and clinician predictions of antibiotic resistance in Enterobacterales bloodstream infections |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1016/j.jinf.2024.106388 |
| Publisher version: | https://doi.org/10.1016/j.jinf.2024.106388 |
| Language: | English |
| Additional information: | © 2024 The Authors. Published by Elsevier Ltd on behalf of The British Infection Association. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
| Keywords: | Antibiotics, Bloodstream infection, Antimicrobial resistance, Machine learning, Antibiotic stewardship |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Inst of Clinical Trials and Methodology UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Inst of Clinical Trials and Methodology > MRC Clinical Trials Unit at UCL |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10202687 |
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