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

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. Objective: 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: One hundred forty-seven 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; the highest recall (93.3%) was observed for the Gram+ class. The Gram+ class was correctly classified 75% of the time; the 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.