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Identification of antibiotic resistance profiles in diabetic foot infections: A machine learning proof‑of‑concept analysis

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dc.contributor.authorCarrillo Larco, Rodrigo M.
dc.contributor.authorMori Orrillo, Edmundo de Elvira
dc.contributor.authorCastillo-Cara, Manuel
dc.contributor.authorGarcía, Raúl
dc.contributor.authorYovera-Aldana, Marlon
dc.contributor.authorBernabe-Ortiz, Antonio
dc.date.accessioned2025-10-15T14:58:19Z
dc.date.available2025-10-15T14:58:19Z
dc.date.issued2025-04-12
dc.descriptionThe registered version of this article, first published in International Journal of Diabetes in Developing Countries, is available online at the publisher's website: Springer, https://doi.org/10.1007/s13410-025-01490-1
dc.descriptionLa versión registrada de este artículo, publicado por primera vez en International Journal of Diabetes in Developing Countries, está disponible en línea en el sitio web del editor: Springer, https://doi.org/10.1007/s13410-025-01490-1
dc.descriptionThis work has been supported by grant PID2023-150515OB-I00 from the Spanish Government, partially covered with funds from the Recovery and Resilience Facility (RRF)
dc.description.abstractBACKGROUND: Diabetic foot infections (DFIs) are a prevalent diabetes-related complication. Managing DFIs requires timely antibiotic treatment but identifying the best antibiotic often depends on microbiological cultures, which can take days and may be unavailable or prohibitively expensive in resource-limited settings. We aimed to develop a classification model that uses readily available clinical and laboratory data to differentiate between DFIs that are Gram+ resistant, Gram- resistant, or none. METHODS: We used retrospective data from patients treated for DFIs at a hospital in Lima, Peru. Gram+ multidrug-resistant bacteria (MDRB) included MDR species of Staphylococcus aureus, other Staphylococcus and Enterococcus, whereas Gram- MDRB included MDR species of Enterobacteriaceae, Pseudomonas, and Acinetobacter. Twenty clinical (e.g., Wagner classification) and laboratory (e.g., HbA1c) variables were used as predictors in a XGBoost model which was internally validated. RESULTS: 147 patients, predominantly male (75.1%), with a mean age of 59.7 years. Of these, 19.7% had no MDRB, 34.0% had Gram+ MDRB, and 46.3% had Gram- MDRB. The model achieved an overall F1 score of 83.9%. The highest precision (91.8%) was observed for the Gram- class; highest recall (93.3%) was observed for the Gram+ class. The Gram+ class was correctly classified 75% of the time; Gram- class had a correct classification rate of 90%. CONCLUSIONS: Our work suggests it is possible to distinguish between DFIs that are non-MDR, Gram+ MDR, or Gram- MDR using readily available information. Although further validation is required, this model offers promising evidence for a digital bedside tool to guide empirical antibiotic treatment for DFIs.en
dc.description.versionversión final
dc.identifier.citationCarrillo-Larco, R.M., de Elvira Mori Orrillo, E., Castillo-Cara, M. et al. Identification of antibiotic resistance profiles in diabetic foot infections: A machine learning proof-of-concept analysis. Int J Diabetes Dev Ctries (2025). https://doi.org/10.1007/s13410-025-01490-1
dc.identifier.doihttps://doi.org/10.1007/s13410-025-01490-1
dc.identifier.issn1998-3832
dc.identifier.urihttps://hdl.handle.net/20.500.14468/30425
dc.journal.titleInternational Journal of Diabetes in Developing Countries
dc.language.isoen
dc.publisherSpringer
dc.relation.centerE.T.S. de Ingeniería Informática
dc.relation.departmentInteligencia Artificial
dc.rightsinfo:eu-repo/semantics/embargoedAccess
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/deed.es
dc.subject1203.04 Inteligencia artificial
dc.subject.keywordsantibiotic resistanceen
dc.subject.keywordsdiabetes-related complicationsen
dc.subject.keywordsartificial intelligenceen
dc.subject.keywordsrisk predictionen
dc.titleIdentification of antibiotic resistance profiles in diabetic foot infections: A machine learning proof‑of‑concept analysisen
dc.typeartículoes
dc.typejournal articleen
dspace.entity.typePublication
relation.isAuthorOfPublicationc0e39bd2-c0d8-4743-953d-488baf6b977e
relation.isAuthorOfPublication.latestForDiscoveryc0e39bd2-c0d8-4743-953d-488baf6b977e
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