Rapid extended-spectrum beta-lactamase-confirmation by using a machine learning model directly on routine automated susceptibility testing results

Rapid extended-spectrum beta-lactamase-confirmation by using a machine learning model directly on routine automated susceptibility testing results

ObjectivesPhenotypical Extended Spectrum β-Lactamase (ESBL)-production is commonly determined using the combination disk diffusion test or gradient test. This requires overnight incubation, prolonging time-to-detection and increasing duration of empirical treatment for patients with infections cause...

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Main Authors: Y. El Ghouch, M. C. Schut, K. C. E. Sigaloff, W. Altorf-Van Der Kuil, J. M. Prins, R. P. Schade, the ISIS-AR study group, J.W.T. Cohen Stuart, D.C. Melles, K. van Dijk, A. Alzubaidy, M. Scholing, S.D. Kuil, G.J. Blaauw, W. Altorf, van der Kuil, S.M. Bierman, S.C. de Greeff, S.R. Groenendijk, R. Hertroys, L. Kruithof, I.M. Nauta, D.W. Notermans, J. Polman, W.J. van den Reek, A.F. Schoffelen, F. Velthuis, C.C.H. Wielders, B.J. de Wit, R.E. Zoetigheid, W. van den Bijllaardt, E.M. Kraan, M.B. Haeseker, J.M. da Silva, E. de Jong, B. Maraha, A.J. van Griethuysen, B.B. Wintermans, M.J.C.A. van Trijp, A.E. Muller, M. Wong, A. Ott, E. Bathoorn, M. Lokate, J. Sinnige, L. Bank, N.H. Renders, J.W. Dorigo-Zetsma, L.J. Bakker, W. Ang, K. Waar, M.T. van der Beek, M.A. Leversteijn-van Hall, S.P. van Mens, E. Schaftenaar, M.H. Nabuurs-Franssen, I. Maat, P.D.J. Sturm, B.M.W. Diederen, L.G.M. Bode, D.S.Y. Ong, M. van Rijn, S. Dinant, M. den Reijer, D.W. van Dam, E.I.G.B. de Brauwer, R.G. Bentvelsen, A.L.M. Vlek, M. de Graaf, A. Troelstra, K.B. Gast, M.P.A. van Meer, J. de Vries, J.D. Machiels
Format: Article
Language:English
Published: Frontiers Media S.A. 2025-04-01
Series:Frontiers in Microbiology
Subjects:
ESBL
machine learning
antimicrobial resistance
bacteria
surveillance
Online Access:https://www.frontiersin.org/articles/10.3389/fmicb.2025.1582703/full
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Summary:ObjectivesPhenotypical Extended Spectrum β-Lactamase (ESBL)-production is commonly determined using the combination disk diffusion test or gradient test. This requires overnight incubation, prolonging time-to-detection and increasing duration of empirical treatment for patients with infections caused by gram-negative bacteria. To achieve instant confirmation without incubation, we developed a machine learning (ML)-model that predicts phenotypic ESBL-confirmation using Minimum Inhibitory Concentrations from routine automated antimicrobial susceptibility testing (AST)-results.MethodsData from the Dutch national laboratory-based surveillance system ISIS-AR collected between 2013 and 2022 from 49 laboratories were used: 178,044 isolates of E. coli (141,576), K. pneumoniae (33,088), and P. mirabilis (3,380) that exhibited resistance to cefotaxime and/or ceftazidime, and had available results of phenotypical ESBL-confirmation testing. We evaluated Logistic Regression, Random Forest and XGBoost models and calculated SHAP-values (SHapley Additive exPlanations) to identify most contributing features. We externally validated models using 5,996 isolates collected in Amsterdam University Medical Centres’ between 2013 and 2022.ResultsXGBoost achieved an AUROC (Area Under Receiver Operating Characteristics) of 0.97, a sensitivity of 0.89 and an accuracy of 0.93. The most contributing features were the antibiotics cefotaxime, cefoxitin and trimethoprim for E. coli and K. pneumoniae, and cefuroxime, imipenem and cefotaxime for P. mirabilis. External validation yielded AUROCs of 0.93 (E. coli), 0.89 (K. pneumoniae) and 0.93 (P. mirabilis).ConclusionML-models for prediction of ESBL-production using routine AST-system data achieved high performances. Implementing these models in laboratory practice could shorten time-to-detection. Once deployed, this approach could facilitate widespread screening for phenotypic ESBL-production.
ISSN:1664-302X

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