Data from: Biofilm-associated multidrug-resistant and methicillin-resistant staphylococcus aureus infections in children
Abstract
Introduction: The ability of Staphylococcus aureus to form biofilms—architectural complexes that cause chronic and recalcitrant infections—along with its notorious variant, methicillin-resistant S. aureus (MRSA), leads to multidrug-resistant (MDR) infections that are challenging to treat with antibiotics. This cross-sectional study investigated the prevalence of S. aureus infections in children (<17 years) and characterized the antibiograms of MDR, MRSA, and biofilm-forming strains, along with their coexistence.
Methods: S. aureus strains were isolated and identified from clinical samples and tested for antibiograms following standard microbiology guidelines. MDR strains were non-susceptible to at least one agent in three antimicrobial categories, whereas MRSA strains were cefoxitin-resistant. The gold-standard microtiter plate method was used to detect biofilms. Statistical analyses were performed using SPSS version 17.0.
Results: S. aureus was detected in 9.02% of 543 samples, primarily from pus (79.59%, 39/49). Children aged 1 to <3 years most commonly contracted infections (30.61%, 15/49), and males (67.35%, 33/49) had twice as many infections as females (32.65%, 16/49). As high as 84.69% (83/98) of strains were penicillin-resistant, while 18.37% (27/147) were aminoglycoside-resistant. MDR accounted for 79.59% (39/49) of all S. aureus infections, while MRSA and biofilm-formers accounted for 67.35% (33/49) and 24.49% (12/49), respectively. Fluoroquinolone resistance in non-(MDR-MRSA-biofilm-formers), MDR-MRSA, MDR-biofilm-formers, and MRSA-biofilm-formers was 31.25%, 46.77%, 58.33%, and 60%, respectively, while aminoglycoside resistance was 0%, 32.26%, 50.0%, and 45.0%, and penicillin resistance was 87.50%, 85.48%, 100.0%, and 100.0%.
Conclusion: S. aureus, mostly MDR and MRSA, caused four-fifths of infections in children. Compared to MDR-MRSA and non-(MDR-MRSA-biofilm-formers), MDR-biofilm-formers and MRSA-biofilm-formers triggered higher levels of antimicrobial resistance.
README: Biofilm-Associated Multidrug-Resistant and Methicillin-Resistant Staphylococcus aureus Infections in Children
https://doi.org/10.5061/dryad.hx3ffbgmm
Description of the data and file structure
The dataset is of paediatric patients with Staphylococcus auresus infection. Dataset comprises single sheet. The sheet details for demographic information, such as age and gender of the patients; clinical information, including clinical samples; microbiological findings comprising antimicrobial resistance patterns of S. aureus, multi-drug-resistant or non-multi-drug-resistant, biofilm formers or non-formers. Exact age is removed and is categorized as age group in order to anonymize the data.
S. auresus strains from patients with infections were coded with alphanumeric with initial Isolate. (Isolate 1, Isolate23, etc.).
Units of the study variables were standard and as follows:
(a) Age group Years
(b) Optical densities (ODs) nanometer (nm)
Disc diffusion or antimicrobial susceptibility testing was done with Kirby Bauer Disc Diffusion method.
Except for values of ODs and multiple antibiotic resistance index, the data were qualitative and were calculated as frequency (percentage) in SPSS version 17.0. Quantitative variables were calculated as median (interquartile range). While quantitative variables were analyzed using t-test for statistical correlations, qualitative variables were tested using chi-square test at 95% confidence interval. Multiple antibiotic resistance index is calculated as divison of total antibiotic resistance (n) / total antibiotics tested (N)
OD represents Optical density, R represents resistant, S represents susceptibile, MARI = multiple antibiotic resistance index, MDR represents multi-drug-resistant.
Readers may access the data from the Dryad repository or with a request email to the corresponding author, Ajaya Basnet (abasnet.microberesearch@gmail.com), of the article.
Methods
The study population was categorized into five groups based on their ages, <1 month: Neonate, 1 month to <1 year: Infant, 1 year to <3 years: Toddler, 3 years to <5 years: Pre-school, 5 years to 17 years: School. Demographic information and laboratory findings were collected using a patient information sheet and recorded using Microsoft Excel version 10.0. Communication with involved healthcare providers clarified any unclear or missing records.
The data was analyzed using descriptive statistics in SPSS software version 17.0, providing frequencies and percentages as key indicators. Quantitative variables were analyzed by independent student t-test, while qualitative variables by chi-square test. The threshold for determining statistical significance was established as p<0.05.