DOI: 10.5061/dryad.zpc866thj
title: A decade-long analysis of trends in antimicrobial resistance at a neurosurgical hospital in Kathmandu, Nepal

description: Background: Multidrug-resistant (MDR) bacteria cause infections with
 higher risks of morbidity, mortality, and financial burden, emphasizing
 the importance of understanding pathogen-specific resistance patterns for
 effective treatment and resistance management. Therefore, this
 retrospective cohort study examined the prevalence, causes, and trends of
 antimicrobial resistance in bacterial infections in a neurosurgical
 hospital in Nepal. Method: We analyzed the demographics, bacteriological
 profiles, and antimicrobial susceptibility results of patients who visited
 a neurosurgical hospital in Kathmandu, Nepal, between January 2014 and
 January 2024, using SPSS, version 17.00. Results: Among 4,758 patients,
 465 (9.77%) had infections caused by 571 bacteria. Of them, 435 (93.55%)
 patients had urinary tract infections, 89 (19.14%) had bloodstream
 infections, and 31 (6.67%) had respiratory tract infections. Klebsiella
 pneumoniae (n=172, 30.12%) was the predominant bacteria. Resistance rates
 for Enterobacterales and GPC against tetracyclines were 83.33% and 45.83%,
 cephalosporins were 78.02% and 40.45%, quinolones were 72.25% and 50.00%,
 aminoglycosides were 65.14% and 43.53%, carbapenems were 62.96% and
 30.00%, penicillins were 54.55% and 57.89%, and penicillin with
 beta-lactamase inhibitors (PwBLIs) were 40.54% and 42.31%, respectively.
 Non-fermenters showed 100% resistance to these antibiotics. MDR isolates
 (n=118, 20.67%) were 100.00% susceptible to piperacillin-tazobactam and
 83.33% to polymyxin B. Over the years, resistance increased for
 cephalosporins (48.15-60.53%) but decreased for carbapenems
 (50.00-33.33%), penicillins (64.29-42.31%), PwBLIs (50.00-12.50%),
 aminoglycosides (60.00-49.12%), tetracyclines (100.00-16.67%), and
 polymyxins (76.22-16.67%). Conclusion: One-tenth of hospital-visiting
 patients had bacterial infections, with three-fourths involving
 Enterobacterales and one-fifth involving MDR bacteria. In recent years,
 resistance to cephalosporins has increased, while resistance to other
 beta-lactams, aminoglycosides, and polymyxins has decreased.
description: In the patient information sheet, outcome variables [bacterial
 pathogens and viral-bacterial coinfections (simultaneous occurrences)] 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). 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 &lt;0.05 within a
 95% confidence interval (CI).
description: # A decade-long analysis of trends in antimicrobial resistance at a
 neurosurgical hospital in Kathmandu, Nepal
 [https://doi.org/10.5061/dryad.zpc866thj](https://doi.org/10.5061/dryad.zpc866thj) ## Description of the data and file structure A decade-long analysis of trends in antimicrobial resistance at a neurosurgical hospital in Kathmandu, Nepal The dataset is of hospital-visiting individuals who were with and without bacterial infections. The dataset consists of two sheets. The first sheet details clinico-demographic information, such as patient age group, gender, specimen type, and bacterial infection status. The exact age is removed and categorized as an age group to anonymize the data. The second sheet details the year, bacterial species, and antimicrobial susceptibility patterns. Given that the patient identification number and specimen number are classified as human subject data and require anonymization, these identifiers have been removed from the dataset. 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 (30th edition) or other reasons for omitting antibiotic testing. Positive culture results from Foley catheter tips were only used to compare with urine cultures, not for additional analysis, since they may harbor bacteria that do not indicate infection. Units of the study variables were standard and as follows: (a) Age group Years 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]. Additionally, antimicrobial susceptibility testing was done with Kirby Bauer Disc Diffusion method [2]. Multidrug resistance was defined as an isolate resistant to at least one agent in ≥3 antimicrobial categories [3]. MAR index was calculated as the ratio of bacterial strain resistant to a total number of antibiotics to the total number of antibiotics tested. 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). 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 &lt;0.05 within a 95% confidence interval (CI). n/a represents not available, 0 represents female, 1 represents male, CoNS represents Coagulase-negative Staphylococcus, S=susceptible, I=intermediate, R=resistance, AMX represents amoxicillin, AMP represents ampicillin, OXA represents oxacillin, F represents flucloxacillin, CB represents carbenicillin, COX represents cloxacillin, GEN represents gentamicin, AK represents amikacin, TOB represents tobramycin, CL represents colisitn, PB represents polymyxin B, NA represents nalidixic acid, CIP represents ciprofloxacin, OF represents ofloxacin, LF represents levoflxacin, MRP represents meropenem, IMP represents imipenem, CTR represents ceftriaxone, CTX represents cefotaxime, CAZ represents ceftazidime, CZN represents cefazolin, CFM represents cefixime, CPM represents Cefepime, CFUM represents cefuroxime, CPDM represents cefpodoxime, CX represents cefoxitin, DXC represents doxycycline, TE represents tetracycline, AMX represents amoxicillin-clavulanate, CEC represents cefotaxime-clavulanate, PIT represents piperacillin tazobactam, SAM represents ampicillin sulbactam, TEIC represents teicoplanin, E represents erythromycin, AZM represents azithromycin, C represents chloramphenicol, CD represents clindamycin, TM-S represents Trimethoprim-Sulfamethoxazole, COT represents cotrimoxazole, NIT represents nitrofurantoin, TGC represents tigecycline, LZ represents linezolid. Readers may access the data from the Dryad repository or with a request email to Ajaya Basnet (xlcprk@gmail.com). 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. \[3] Magiorakos AP, Srinivasan A, Carey RB, Carmeli Y, Falagas ME, Giske CG, Harbarth S, Hindler JF, Kahlmeter G, Olsson-Liljequist B, Paterson DL. Multidrug-resistant, extensively drug-resistant and pandrug-resistant bacteria: an international expert proposal for interim standard definitions for acquired resistance. Clin Microbiol Infect. 2012 Mar 1;18(3):268-81.