Evidence map›Paper›PMID 40702446›Full record

ArticleBMC infectious diseases2025

Seasonal and hospital settings variations in antimicrobial resistance among clinical isolates from cardiac patients: insights from a 7-Year study.

Muhammad Umer Asghar, Arsalan Haseeb Zaidi, Muhammad Tariq, Noor Ul Ain

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Article in BMC infectious diseases, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

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6citing papers in PubMed
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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

Who cites it

6 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Muhammad Umer AsgharNational Probiotic Lab, National Institute for Biotechnology and Genetic Engineering-College (NIBGE-C), Faisalabad 38000, Pakistan. omer.asghar@yahoo.com.
Arsalan Haseeb ZaidiNational Probiotic Lab, National Institute for Biotechnology and Genetic Engineering-College (NIBGE-C), Faisalabad 38000, Pakistan.
Muhammad TariqNational Probiotic Lab, National Institute for Biotechnology and Genetic Engineering-College (NIBGE-C), Faisalabad 38000, Pakistan.
Noor Ul AinCentre for Clinical Microbiology, University College London (UCL), London, UK. noor.ain.21@ucl.ac.uk.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAntimicrobial resistance (AMR) is a growing global health challenge, particularly in specialized tertiary care settings. Despite its significance, data on the effects of seasonal variations, demographics, and healthcare settings on AMR in Pakistan are scarce.

aimThis study aimed to evaluate the prevalence and resistance patterns of AMR and identify the key contributing factors at a cardiac hospital in Faisalabad from 2012 to 2019 to inform targeted infection control strategies. METHODOLOGY: This retrospective cross-sectional analysis of 3,035 patient records adhered to STROBE guidelines. AMR profiles, including the multiple antibiotic resistance (MAR) index and antibiotic resistance genes (ARGs), were examined in relation to seasonality, hospital settings, and patient demographics.

resultsThis study identified Staphylococcus aureus, Klebsiella pneumoniae, and Escherichia coli as the most prevalent pathogens, with S. aureus and K. pneumoniae classified among the ESKAPE group (Enterococcus faecium, S. aureus, K. pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacter spp.). Isolation rates were higher in inpatients (IPs) than outpatients (OPs), with peak occurrences in autumn and winter among IPs and in spring among OPs. Age and sex significantly influenced pathogen isolation rates. The multiple antibiotic resistance (MAR) index was highest for S. aureus isolates between 2017 and 2019, which showed complete resistance to vancomycin and oxacillin. Key resistance genes mecA, vanA, tetM, and aph(3') were frequently co-detected in S. aureus. Polymyxin B and colistin remained the most effective antibiotics against multidrug-resistant (MDR) strains.

conclusionThese findings highlight the critical need for year-round AMR surveillance, with an enhanced focus during seasonal peaks, particularly for high-risk IPs in winter and autumn. Implementing localized antimicrobial stewardship programs (ASPs) and robust infection control measures is essential to reduce the AMR burden and curb its spread in inpatient settings, especially in resource-constrained healthcare systems, such as Pakistan.

Indexed as

Anti-Bacterial AgentsBacteriaBacterial InfectionsDrug Resistance, BacterialDrug Resistance, Multiple, BacterialSeasonsAdultAgedAged, 80 and overCross-Sectional StudiesFemaleHospitalsHumansMaleMicrobial Sensitivity TestsMiddle AgedAnti-Bacterial AgentsAnd outpatientsAntimicrobial resistanceCardiac patientsESKAPE pathogensHospital-acquired infectionsInpatients

Identifiers

PMID40702446
PMCPMC12288291

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.