Distribution Patterns and Antibiotic Resistance Profiles of Bacterial Pathogens Among Patients with Wound Infections in the Jiaxing Region from 2021 to 2023

Purpose: To systematically assess the distribution and antimicrobial susceptibility of pathogens in wound infections, and analyze risk factors associated with multidrug resistance (MDR).
Patients and Methods: Retrospectively analyzing Jiaxing-region medical records between January 2021 and December 2023, we identified a cohort of 461 wound infection patients. Cultures were grown on various agars, with bacteria identified via Matrix-Assisted Laser Desorption/Ionization Time-of-Flight Mass Spectrometry. The antimicrobial susceptibility of the organisms were conducted by VITEK 2 system, Kirby-Bauer disk diffusion method and Epsilometer test. Statistical Package for the Social Sciences (SPSS) version 22 was used for statistical analysis. Multivariable logistic regression models were developed to pinpoint risk factors for multidrug-resistant organism (MDRO) infections and predict occurrences.
Results: From 461 patients, 549 bacterial pathogens were isolated, predominantly consisting of Staphylococcus aureus, Pseudomonas aeruginosa, Escherichia coli, Acinetobacter baumannii, Enterobacter cloacae, and Enterococcus faecalis. Vancomycin, linezolid, and tigecycline maintained their efficacy against Staphylococcus aureus and Enterococcus species, while Pseudomonas aeruginosa demonstrated sensitivity to aminoglycosides. Conversely, Escherichia coli exhibited high amoxicillin resistance (85.4%). More than half of the isolates were resistant to levofloxacin, ceftriaxone, cotrimoxazole, and gentamicin, with Acinetobacter baumannii strains showing considerable resistance (65.8– 68.4%) to advanced cephalosporins and carbapenems. Within this group, 58 MDROs were detected, primarily originating from Burn Plastic Surgery, Emergency, and Intensive Care Unit (ICU) departments. Multivariate logistic regression identified hyperglycemia, hypoalbuminemia, surgery, extended hospitalization, and exposure to multiple antibiotic classes as independent risk factors for MDRO wound infections. Based on these findings, a predictive model for MDRO occurrence in wounds was constructed, which had a sensitivity of 0.627, specificity of 0.933, and an Area Under the Curve (AUC) of 0.838.
Conclusion: Staphylococcus aureus and Pseudomonas aeruginosa dominated in wound infections with differential antibiotic resistance. Independent risk factors included hyperglycemia, hypoalbuminemia, surgery, extended hospitalization, and polyantibiotic use. We urge prioritizing culture, susceptibility tests, and personalized antibiotic strategies to address MDRO risks and improve wound infection management specificity and efficacy.

Keywords: antibiotic resistance, wound infection, pathogenic bacteria, risk factor, prediction model