Exploring multidrug resistance patterns in community-acquired E. coli urinary tract infections with machine learning
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Accepted version
Author(s)
Type
Journal Article
Abstract
Background
While associations of antibiotic resistance traits are not random in multidrug-resistant (MDR) bacteria, clinically relevant resistance patterns remain underexplored. This study used association-set mining to explore resistance associations within E. coli isolates from community-acquired urinary tract infection (UTI) isolates collected from 2018 to 2022 by France’s national surveillance system.
Methods
Association-set mining was applied separately to extended-spectrum beta-lactamase-producing E. coli (ESBL-EC) and non-ESBL-EC. MDR patterns with expected support (reflecting pattern frequency) and conditional lift (reflecting association strength) higher than expected by chance (p-value≤0.05) were used to construct resistance associations networks, and analysed according to time, age and gender.
Findings
The number of isolates increased from 360 287 in 2018 to 629 017 in 2022. More MDR patterns were selected in ESBL-EC than non-ESBL-EC (2022: 1770 vs 93 patterns), with higher respective network densities (2022: 0.301 vs 0.100). Fluoroquinolone, third-generation cephalosporin and penicillin resistances were strongly associated in ESBL-EC. Median networks densities increased from 2018 to 2022 in both ESBL-EC (0.238 to 0.301, p-value=0.06, Pearson test) and non-ESBL-EC (0.074 to 0.100, p-value=0.04). Across all years, median densities were higher in men than in women (ESBL-EC 2022: 0.305 vs 0.271; non-ESBL-EC: 0.128 vs 0.094), and higher in individuals over 65 than under 65 (ESBL-EC: 0.289 vs 0.275; non-ESBL-EC: 0.103 vs 0.088).
Interpretation
These findings highlight temporal, age-specific and gender-specific variations in resistance patterns, underscoring the potential of machine-learning to understand them and inform antibiotic strategies.
Funding
This work received funding from the National Research Agency project COMBINE ANR–22-PAMR-0003.
While associations of antibiotic resistance traits are not random in multidrug-resistant (MDR) bacteria, clinically relevant resistance patterns remain underexplored. This study used association-set mining to explore resistance associations within E. coli isolates from community-acquired urinary tract infection (UTI) isolates collected from 2018 to 2022 by France’s national surveillance system.
Methods
Association-set mining was applied separately to extended-spectrum beta-lactamase-producing E. coli (ESBL-EC) and non-ESBL-EC. MDR patterns with expected support (reflecting pattern frequency) and conditional lift (reflecting association strength) higher than expected by chance (p-value≤0.05) were used to construct resistance associations networks, and analysed according to time, age and gender.
Findings
The number of isolates increased from 360 287 in 2018 to 629 017 in 2022. More MDR patterns were selected in ESBL-EC than non-ESBL-EC (2022: 1770 vs 93 patterns), with higher respective network densities (2022: 0.301 vs 0.100). Fluoroquinolone, third-generation cephalosporin and penicillin resistances were strongly associated in ESBL-EC. Median networks densities increased from 2018 to 2022 in both ESBL-EC (0.238 to 0.301, p-value=0.06, Pearson test) and non-ESBL-EC (0.074 to 0.100, p-value=0.04). Across all years, median densities were higher in men than in women (ESBL-EC 2022: 0.305 vs 0.271; non-ESBL-EC: 0.128 vs 0.094), and higher in individuals over 65 than under 65 (ESBL-EC: 0.289 vs 0.275; non-ESBL-EC: 0.103 vs 0.088).
Interpretation
These findings highlight temporal, age-specific and gender-specific variations in resistance patterns, underscoring the potential of machine-learning to understand them and inform antibiotic strategies.
Funding
This work received funding from the National Research Agency project COMBINE ANR–22-PAMR-0003.
Date Acceptance
2025-10-01
Citation
Antimicrobial Agents and Chemotherapy
ISSN
0066-4804
Publisher
American Society for Microbiology
Journal / Book Title
Antimicrobial Agents and Chemotherapy
Copyright Statement
Copyright This paper is embargoed until publication. Once published the author’s accepted manuscript will be made available under a CC-BY License in accordance with Imperial’s Research Publications Open Access policy (www.imperial.ac.uk/oa-policy).
License URL
Publication Status
Accepted