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. 2024 Apr 17:15:1383989.
doi: 10.3389/fmicb.2024.1383989. eCollection 2024.

A study of antibiotic resistance pattern of clinical bacterial pathogens isolated from patients in a tertiary care hospital

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A study of antibiotic resistance pattern of clinical bacterial pathogens isolated from patients in a tertiary care hospital

Vishal L Handa et al. Front Microbiol. .

Abstract

We investigated antibiotic resistance pattern in clinical bacterial pathogens isolated from in-patients and out-patients, and compared it with non-clinical bacterial isolates. 475 bacterial strains isolated from patients were examined for antibiotic resistance. Staphylococcus spp. (148; 31.1%) were found to be the most prevalent, followed by Klebsiella pneumoniae (135; 28.4%), Escherichia coli (74; 15.5%), Pseudomonas aeruginosa (65; 13.6%), Enterobacter spp. (28; 5.8%), and Acinetobacter spp. (25; 5.2%). Drug-resistant bacteria isolated were extended spectrum-β-lactamase K. pneumoniae (8.8%), E. coli (20%), metallo-β-lactamase P. aeruginosa (14; 2.9%), erythromycin-inducing clindamycin resistant (7.4%), and methicillin-resistant Staphylococcus species (21.6%). Pathogens belonging to the Enterobacteriaceae family were observed to undergo directional selection developing resistance against antibiotics ciprofloxacin, piperacillin-tazobactam, cefepime, and cefuroxime. Pathogens in the surgical ward exhibited higher levels of antibiotic resistance, while non-clinical P. aeruginosa and K. pneumoniae strains were more antibiotic-susceptible. Our research assisted in identifying the drugs that can be used to control infections caused by antimicrobial resistant bacteria in the population and in monitoring the prevalence of drug-resistant bacterial pathogens.

Keywords: antimicrobial resistance; erythromycin-induced clindamycin resistance; extended spectrum-β-lactamase; metallo-β-lactamase; methicillin-resistant Staphylococcus aureus.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Datasets for antibiotic susceptibility tests. (A) Distribution of the specimens gathered for this investigation, (B) the source of the pathogenic bacterial strains, (C) Age-wise distribution of bacterial isolates, (D) Sample distributions by gender, (E) Prevalence of bacterial isolates in various wards of the hospital, (F) the frequency of testing for different antibiotics (grouped by antibiotic class) against bacterial strains.
Figure 2
Figure 2
Prevalence of antibiotic resistance in clinical strains. (A) Diversity of antibiotic resistance in clinical isolates. Each bar represents the number of isolates that were resistant to antibiotics. (B) Total number of drugs for which resistance was found. It is plotted using box plots; the standard deviation is indicated by the error bar on both sides, and the middle line displays the mean value of the number of antibiotics resistant to each type of bacteria. Tukey post-hoc test was conducted for comparisons between the mean values of number of resistant antibiotics, p < 0.05 was considered statistically significant. Diference alphabets indicates statistical significant.
Figure 3
Figure 3
Comparison of the size of antibiotic susceptibility zone of Gram-negative bacteria. (A) Klebsiella pneumoniae isolated from Spodoptera frugiperda caterpillar carcasses, (B) Klebsiella pneumoniae isolated from clinical samples, (C) Pseudomonas aeruginosa isolated from Spodoptera frugiperda caterpillar carcasses, and (D) Pseudomonas aeruginosa isolated from clinical samples. Box plots were used for the comparison; each black dot represents an isolated bacterial strain, the central line shows the mean value of the zone of inhibition, and the error bar on both sides reflects the standard deviation.

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References

    1. Akiba M., Senba H., Otagiri H., Prabhasankar V. P., Taniyasu S., Yamashita N., et al. . (2015). Impact of wastewater from different sources on the prevalence of antimicrobial-resistant Escherichia coli in sewage treatment plants in South India. Ecotoxicol. Environ. Saf. 115, 203–208. doi: 10.1016/j.ecoenv.2015.02.018, PMID: - DOI - PubMed
    1. Bai A. D., Lo C. K., Komorowski A. S., Suresh M., Guo K., Garg A., et al. . (2022). Staphylococcus aureus bacteremia mortality across country income groups: a secondary analysis of a systematic review. Int. J. Infect. Dis. 122, 405–411. doi: 10.1016/j.ijid.2022.06.026, PMID: - DOI - PubMed
    1. Baquero F. (2001). Low-level antibacterial resistance: a gateway to clinical resistance. Drug Resist. Updat. 4, 93–105. doi: 10.1054/drup.2001.0196, PMID: - DOI - PubMed
    1. Baquero F., Martinez J. L., Lanza F. V., Rodríguez-Beltrán J., Galán J. C., San Millán A., et al. . (2021). Evolutionary pathways and trajectories in antibiotic resistance. Clin. Microbiol. Rev. 34:e0005019. doi: 10.1128/CMR.00050-19, PMID: - DOI - PMC - PubMed
    1. Barbosa C., Trebosc V., Kemmer C., Rosenstiel P., Beardmore R., Schulenburg H., et al. . (2017). Alternative evolutionary paths to bacterial antibiotic resistance cause distinct collateral effects. Mol. Biol. Evol. 34, 2229–2244. doi: 10.1093/molbev/msx158, PMID: - DOI - PMC - PubMed