Machine learning to predict antimicrobial resistance: future applications in clinical practice?

Autor: Kherabi Y; Infectious and Tropical Disease Department, Bichat-Claude Bernard Hospital, Assistance Publique-Hôpitaux de Paris, Université Paris Cité, Paris, France; Université Paris Cité and Université Sorbonne Paris Nord, Inserm, IAME, Paris, France. Electronic address: yousra.kherabi@aphp.fr., Thy M; Medical and Infectious Diseases ICU (MI2) - Bichat-Claude Bernard Hospital, Assistance Publique-Hôpitaux de Paris, Université Paris Cité, Paris, France; EA 7323 - Pharmacology and Therapeutic Evaluation in Children and Pregnant Women, Université Paris Cité, Paris, France., Bouzid D; Université Paris Cité and Université Sorbonne Paris Nord, Inserm, IAME, Paris, France; Emergency Department, Bichat Claude Bernard Hospital, Assistance Publique-Hôpitaux de Paris, Paris, France., Antcliffe DB; Division of Anaesthetics, Pain Medicine and Intensive Care, Department of Surgery and Cancer, Imperial College London, London, UK; Department of Intensive Care Unit, Charing Cross Hospital, Imperial College Healthcare NHS Trust, London, UK., Rawson TM; National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, Imperial College London, London, UK; Centre for Antimicrobial Optimisation Imperial College London, London, UK., Peiffer-Smadja N; Infectious and Tropical Disease Department, Bichat-Claude Bernard Hospital, Assistance Publique-Hôpitaux de Paris, Université Paris Cité, Paris, France; Université Paris Cité and Université Sorbonne Paris Nord, Inserm, IAME, Paris, France; National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, Imperial College London, London, UK.
Jazyk: angličtina
Zdroj: Infectious diseases now [Infect Dis Now] 2024 Apr; Vol. 54 (3), pp. 104864. Date of Electronic Publication: 2024 Feb 12.
DOI: 10.1016/j.idnow.2024.104864
Abstrakt: Introduction: Machine learning (ML) is increasingly being used to predict antimicrobial resistance (AMR). This review aims to provide physicians with an overview of the literature on ML as a means of AMR prediction.
Methods: References for this review were identified through searches of MEDLINE/PubMed, EMBASE, Google Scholar, ACM Digital Library, and IEEE Xplore Digital Library up to December 2023.
Results: Thirty-six studies were included in this review. Thirty-two studies (32/36, 89 %) were based on hospital data and four (4/36, 11 %) on outpatient data. The vast majority of them were conducted in high-resource settings (33/36, 92 %). Twenty-four (24/36, 67 %) studies developed systems to predict drug resistance in infected patients, eight (8/36, 22 %) tested the performances of ML-assisted antibiotic prescription, two (2/36, 6 %) assessed ML performances in predicting colonization with carbapenem-resistant bacteria and, finally, two assessed national and international AMR trends. The most common inputs were demographic characteristics (25/36, 70 %), previous antibiotic susceptibility testing (19/36, 53 %) and prior antibiotic exposure (15/36, 42 %). Thirty-three (92 %) studies targeted prediction of Gram-negative bacteria (GNB) resistance as an output (92 %). The studies included showed moderate to high performances, with AUROC ranging from 0.56 to 0.93.
Conclusion: ML can potentially provide valuable assistance in AMR prediction. Although the literature on this topic is growing, future studies are needed to design, implement, and evaluate the use and impact of ML decision support systems.
Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2024 Elsevier Masson SAS. All rights reserved.)
Databáze: MEDLINE
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