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Patterns of multiple resistance to antibiotics in gram-negative bacteria demonstrated by factor analysis
L Leibovici1, A J Wysenbeek, H Konisberger
1Department of Medicine B, Beilinson Medical Centre, Petah Tiqva, Israel.
Abstract:
Principal component analysis was used to demonstrate the main associations between patterns of resistance to antibiotic drugs in 670 gram-negative bacteria consecutively isolated from blood cultures over a period of two years. Six factors were derived, which accounted for 84% of the total variance of the original matrix. Each factor represented an association between resistance to certain antibiotics as follows: factor 1: aztreonam, third generation cephalosporins and aminoglycosides; factor 2: first and second generation cephalosporins; factor 3: tetracycline and chloramphenicol; factor 4: ampicillin and ureidopenicillins; factor 5: trimethoprim/sulfamethoxazole; factor 6: fluoroquinolones. On two-way analysis of variance the difference in the factor scores was significant between bacteria for all factors except factor 5. The difference in factor scores between community and hospital acquired strains was significant only for factors 1, 2 and 6. Only the score of factor 6 showed a clear trend to increase with time during the two-year study period. Patients who were treated with antibiotics prior to bacteremia had higher scores for all factors, the difference being most marked in patients treated with fluoroquinolones. Factor analysis can be used to describe phenotypic associations between resistance to antibiotics, and the factor score used to compare groups of isolates and to demonstrate temporal and other trends.
Insights
Principal component analysis revealed distinct antibiotic resistance patterns in gram-negative bacteria. Fluoroquinolone resistance significantly increased over time, highlighting evolving antimicrobial resistance trends.
Area of Science:
- Medical Microbiology
- Infectious Diseases
- Statistical Analysis
Background:
- Antibiotic resistance in gram-negative bacteria is a growing global health concern.
- Understanding resistance patterns is crucial for effective treatment strategies.
Purpose of the Study:
- To identify and characterize associations between antibiotic resistance patterns in gram-negative bacteria.
- To investigate temporal trends and factors influencing these resistance patterns.
Main Methods:
- Principal component analysis (PCA) applied to antibiotic resistance data from 670 gram-negative bacterial isolates.
- Two-way analysis of variance (ANOVA) used to compare factor scores between different groups.
Main Results:
- Six distinct antibiotic resistance factors were identified, explaining 84% of the variance.
- Fluoroquinolone resistance (Factor 6) showed a significant increasing trend over the two-year study period.
- Patients with prior antibiotic treatment exhibited higher resistance scores, particularly for fluoroquinolones.
Conclusions:
- PCA is effective in describing phenotypic associations of antibiotic resistance.
- Factor scores can be used to compare bacterial isolate groups and identify temporal trends.
- The increasing fluoroquinolone resistance highlights the need for ongoing surveillance and stewardship.