Antimicrobial Susceptibility Profiles of Escherichia coli Isolates from Clinical Cases of Ducks in Hungary Between 2022 and 2023

Abstract

Background: Antimicrobial resistance (AMR) poses a growing threat to veterinary medicine and food safety. This study examines Escherichia coli antibiotic resistance patterns in ducks, focusing on multidrug-resistant (MDR) strains. Understanding resistance patterns and predicting MDR occurrence are critical for effective intervention strategies. Methods: E. coli isolates were collected from duck samples across multiple regions. Descriptive statistics and resistance frequency analyses were conducted. A decision tree classifier and a neural network were trained to predict MDR status. Cross-resistance relationships were visualized using graph-based models, and Monte Carlo simulations estimated MDR prevalence variations. Results: Monte Carlo simulations estimated an average MDR prevalence of 79.6% (95% CI: 73.1-86.1%). Key predictors in MDR classification models were enrofloxacin, neomycin, amoxicillin, and florfenicol. Strong cross-resistance associations were detected between neomycin and spectinomycin, as well as amoxicillin and doxycycline. Conclusions: The high prevalence of MDR strains underscores the urgent need to revise antibiotic usage guidelines in veterinary settings. The effectiveness of predictive models suggests that machine learning tools can aid in the early detection of MDR, contributing to the optimization of treatment strategies and the mitigation of resistance spread. The alarming MDR prevalence in E. coli isolates from ducks reinforces the importance of targeted surveillance and antimicrobial stewardship. Predictive models, including decision trees and neural networks, provide valuable insights into resistance trends, while Monte Carlo simulations further validate these findings, emphasizing the need for proactive antimicrobial management.

Keywords: Escherichia coli; MDR; MIC; antimicrobial resistance; ducks; minimum inhibitory concentration; waterfowl.

Figure 1

Organ distribution of Escherichia coli isolates (n = 108) and their relative proportions.

Figure 2

Phenotypic antimicrobial susceptibility profile of Escherichia coli isolates (n = 108) from ducks, tested against clinically relevant antimicrobials in veterinary and public health contexts.

Figure 3

Correlation analysis of antimicrobial resistance among Escherichia coli isolates, visualized as a heatmap for each antimicrobial.

Figure 4

Principal components analysis (PCA) based on resistance patterns identified three major clusters. Isolates in Cluster 1 are marked in purple, Cluster 2 in green, and Cluster 3 in yellow.

Figure 5

Network analysis of resistance patterns using graph-based models. Imipenem-resistant isolates formed a distinct group. ACA—amoxicillin clavulanic acid; CRX—ceftriaxone; DOX—doxycycline; COL—colistin; PSA—potentiated sulphonamide; SPE—spectinomycin; NEO—neomycin; FLO—florfenicol; AMX—amoxicillin; ENR—enrofloxacin; IMI—imipenem.

Figure 6

Decision tree model for predicting MDR strain occurrence. Potentiated sulfonamide resistance was selected as the starting point due to its strong association with other antimicrobials.

Figure 7

Monte Carlo simulation-based stochastic modeling to predict MDR strain prevalence.

Figure 8

Comparison of Escherichia coli resistance rates in duck isolates with human resistance data provided by the National Public Health and Pharmaceutical Center.