In-vitro efficacy of quinolones and carbapenems against non-fermenting gram-negative bacteria isolated from clinical specimens received in the Nepalese tertiary care hospital
Data files
May 06, 2025 version files 36.22 KB
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Dataset.xlsx
23.53 KB
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README.md
4.72 KB
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readme.txt
7.97 KB
May 06, 2025 version files 36.19 KB
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Dataset.xlsx
23.53 KB
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README.md
4.70 KB
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readme.txt
7.97 KB
Abstract
Non-fermenting Gram-negative bacteria (NFGNB) pose a significant public health threat due to multidrug resistance (MDR) mediated by biofilms, efflux pumps, and carbapenemases. This cross-sectional study evaluated the minimum inhibitory concentrations (MICs) of quinolones and carbapenems against the Acinetobacter calcoaceticus–baumannii (ACB) complex and Pseudomonas aeruginosa isolated from clinical specimens at the Nepal Armed Police Force Hospital, Balambu, Kathmandu, Nepal. These NFGNB were isolated and identified using standard microbiological methods. Quinolone- and carbapenem-resistant isolates, screened by disc diffusion, were tested for MICs of quinolones and carbapenems using broth microdilution. Statistical analyses were performed in SPSS 17.0. Ninety-two NFGNB were isolated, predominantly from urine (40.2%, 37/92). The MDR proportion was high in P. aeruginosa [85.7% (18/21) versus 80.28% (57/71)]. The extensively drug-resistant (XDR) proportion was high in the ACB complex [14.1% (10/71) versus 4.8% (1/21)]. The MIC50 in P. aeruginosa against norfloxacin and ofloxacin was ≥8 and ≥0.5 µg/mL, respectively. The MIC50 against ciprofloxacin in P. aeruginosa was ≥8 µg/mL, while it was ≥32 µg/mL in the ACB complex. MIC50 against levofloxacin was ≥0.5 µg/mL in P. aeruginosa, unlike the ACB complex (≥16 µg/mL). The MIC50 for meropenem was lower in the ACB complex (≥4 vs ≥8 µg/mL) than in P. aeruginosa, while both showed similar MIC90 (≥64 µg/mL). ACB complex showed higher MIC50 against imipenem (≥4 µg/mL) than P. aeruginosa (≥0.125 µg/mL). Over three-fourths of the NFGNB infections were caused by the ACB complex, exhibiting high XDR rates and elevated MICs to quinolones and carbapenems, underscoring the need for strengthened antimicrobial stewardship.
Dataset DOI: 10.5061/dryad.ghx3ffc1f
Description of the data and file structure
Comparative In-Vitro Efficacy of Fluoroquinolones and Carbapenems among Biofilm-Forming and Non-Forming Non-Fermenters Isolated from Clinical Specimens
The dataset is of hospital-visiting individuals with infection due to non-fermenter bacteria, i.e., Acinetobacter calcoaceticus-baumanii complex and Pseudomonas aeruginosa.
The dataset comprises of single sheet. The sheet details for demographic information, such as age group and gender of the infected patients; clinical information, including clinical samples; microbiological findings comprising bacterial genera, antimicrobial resistance patterns, biofilm formers or non-formers, inhibitory concentrations of fluoroquinolones (norfloxacin, ciprofloxacin, ofloxacin, and levofloxacin) and carbapenems (imipenem and meropenem). Considering that the Patient identification number and Specimen number are human subject data and must be anonymized, these identifiers were removed from the dataset. Exact or direct age is removed and is categorized as an age group to anonymize the data.
Not available (n/a) is noted for antimicrobial susceptibility testing results where certain antibiotics were not tested against the bacterium, whether due to the guidelines of the Clinical and Laboratory Standards Institute (31st edition) or other reasons for omitting antibiotic testing.
Data were anonymized with the code. Patients with non-fermenting infections were coded with an alphanumeric number with the initial No. (N1, N23, etc.).
Units of the study variables were standard and as follows:
(a) Age group: Years
(b) Inhibitory concentrations: μg/mL (microgram per milliliter)
Significant bacterial growth on culture plates was processed for identification using standard microbiological protocols, including cultural characteristics, Gram staining, cell morphology, and biochemical characteristics [1]. Disc diffusion or antimicrobial susceptibility testing was done with the Kirby-Bauer Disc Diffusion method [2]. The inhibitory concentrations were determined by the gold standard broth microdilution technique. Biofilm formers were detected by the gold standard microtiter plate method.
In the patient information sheet, outcome variables (bacterial pathogens) and predictor variables [patient demographics, time frame, specimen type, type of bacterial isolate(s), and antimicrobial susceptibility patterns] were collected from the hospital records. The data were anonymized to ensure patient confidentiality. Data was entered and managed using Microsoft Excel, version 13.0, and analyzed using Statistical Package for Social Sciences (SPSS), version 17.0. Descriptive data were analyzed in terms of frequency and percentage. Quantitative data were reported as mean, median, and interquartile range (IQR). Except for values of inhibitory concentrations, the data were qualitative and were calculated as frequency (percentage) in SPSS version 17.0. Quantitative variables were calculated as median (interquartile range), also referred to as MIC50/MIC90 or MBIC50/MBIC90. Qualitative variables were analyzed using the Chi-square test, while quantitative variables were analyzed using the independent student t-test, with statistical significance determined at a p-value of <0.05 within a 95% confidence interval (CI).
n/a represents not available, 0 represents female, 1 represents male, AST represents antimicrobial susceptibility testing, MIC represents minimum inhibitory concentration, OD = optical density.
Readers may access the data from the Dryad repository or with a request email to the corresponding author, Aakash Parajuli (paraj.aakash52@gmail.com), of the article.
References
[1] Procop GW, Church DL, Hall GS, Janda WM. Koneman's color atlas and textbook of diagnostic microbiology. Jones & Bartlett Publishers; 2020 Jun 29.
[2] Clinical and Laboratory Standards Institute. CLSI document M100S. Performance Standards for Antimicrobial Susceptibility Testing. 26th ed. Wayne, PA. 2016.
Human subjects data
We received consent from the study participants to publish the de-identified data in the public domain.
Considering that Patient identification number (e.g., P1, P18, P128, etc.) and Specimen number (S5, S17, S6, etc.) were human subject data and must be anonymized, these identifiers were removed from the dataset. Exact or direct age is removed and is categorized as an age group in order to anonymize the data.