Fecha
2025-04-12
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Prologuista
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info:eu-repo/semantics/embargoedAccess
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Springer
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Resumen
BACKGROUND: 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.
Descripción
The 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
La 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
This work has been supported by grant PID2023-150515OB-I00 from the Spanish Government, partially covered with funds from the Recovery and Resilience Facility (RRF)
La 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
This work has been supported by grant PID2023-150515OB-I00 from the Spanish Government, partially covered with funds from the Recovery and Resilience Facility (RRF)
Categorías UNESCO
Palabras clave
antibiotic resistance, diabetes-related complications, artificial intelligence, risk prediction
Citación
Carrillo-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
Centro
E.T.S. de Ingeniería Informática
Departamento
Inteligencia Artificial