{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,10]],"date-time":"2026-08-10T13:32:03Z","timestamp":1786368723847,"version":"build-2736575974"},"reference-count":22,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,4,30]],"date-time":"2025-04-30T00:00:00Z","timestamp":1745971200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Microbiol."],"abstract":"<jats:sec>\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>Phenotypical Extended Spectrum \u03b2-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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Data 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\u2019 between 2013 and 2022.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      XGBoost 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\n                      <jats:italic>E. coli<\/jats:italic>\n                      and\n                      <jats:italic>K. pneumoniae<\/jats:italic>\n                      , and cefuroxime, imipenem and cefotaxime for\n                      <jats:italic>P. mirabilis<\/jats:italic>\n                      . External validation yielded AUROCs of 0.93 (E. coli), 0.89 (K. pneumoniae) and 0.93 (P. mirabilis).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>ML-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.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fmicb.2025.1582703","type":"journal-article","created":{"date-parts":[[2025,5,1]],"date-time":"2025-05-01T03:44:20Z","timestamp":1746071060000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["Rapid extended-spectrum beta-lactamase-confirmation by using a machine learning model directly on routine automated susceptibility testing results"],"prefix":"10.3389","volume":"16","author":[{"given":"Y. El","family":"Ghouch","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"M. C.","family":"Schut","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"K. C. E.","family":"Sigaloff","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"W.","family":"Altorf-Van Der Kuil","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"J. M.","family":"Prins","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"R. P.","family":"Schade","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"name":"the ISIS-AR study group","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2025,4,30]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"122634","DOI":"10.1016\/j.saa.2023.122634","article-title":"Detection of extended-spectrum \u03b2-lactamase-producing bacteria isolated directly from urine by infrared spectroscopy and machine learning.","volume":"295","author":"Abu-Aqil","year":"2023","journal-title":"Spectrochim. Acta A Mol. Biomol. 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