Bacteriological Profile and Antibiotic Susceptibility in Patients With Diabetic Foot Infections

Introduction Diabetic foot ulcer (DFU), is a common complication of uncontrolled diabetes, that frequently progresses to diabetic foot infection (DFI), contributing to morbidity, economic burden, and lower limb amputations. This study characterizes the bacteriological profile and antibiotic susceptibility patterns in DFIs patients. Methods A retrospective study was conducted from January 2014 and December 2018. All patients records with microbiologically confirmed DFIs. Demographic data, clinical history, ulcer classification, and microbiological findings were reviewed. Bacterial isolates were identified, and antibiotic susceptibility testing was performed. Results Of a total of 56 patients diagnosed with DFIs at a secondary care hospital in Bolivia, the medical records of 42 individuals were reviewed, of which 28 patients met the inclusion criteria for analysis. Among these, 89.2% (n=25) had polymicrobial infections. <i>Staphylococcus aureus</i> was the most frequently isolated pathogen, representing 38.4% (n=10) of cases, followed by <i>Enterococcus faecalis</i> and <i>Escherichia coli</i>. Among Gram-negative isolates, <i>Acinetobacter baumannii</i> exhibited complete resistance to both ampicillin and cephalosporins, highlighting its multidrug-resistant (MDR) profile. The most frequently administered antibiotics were metronidazole, ceftriaxone, and ciprofloxacin. Resistance to quinolones and β-lactam antibiotics was particularly pronounced across several isolates. Conclusions DFUs with associated infections (DFIs) in this study were predominantly polymicrobial and showed a high prevalence of MDR pathogens. These findings underscore the importance of early and accurate microbiological assessment to prevent infection progression and optimize antibiotic selection in patients with DFUs. Targeted antimicrobial therapy is essential to reduce the risk of treatment failure and amputation. Continued surveillance of resistance patterns is critical to inform empirical treatment strategies in similar healthcare settings.