ArticleBMC infectious diseases2024
Characteristics and antibiotic resistance patterns of urinary tract isolates in hospitalized and non-hospitalized patients: a cross-sectional study in Khartoum, Sudan.
Article in BMC infectious diseases, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- A Narrative Review of Antibiotic Prescribing Practices and Antimicrobial Resistance Challenges in Conflict-Affected Sudan.Antibiotics (Basel, Switzerland) · 2026Review
- Bacterial etiology of urinary tract infections and their sensitivity patterns towards commonly used antibiotics in port Sudan City, Sudan: a retrospective study.BMC infectious diseases · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundUrinary tract infections (UTIs) are widespread worldwide impacting society, economics, and healthcare systems. The rise of antibiotic resistance in both hospital and community settings has further complicated UTI management. This study aims to assess the epidemiologic distribution and microbiologic classification of UTI-causing microorganisms, as well as their resistance patterns.
methodsThis cross-sectional descriptive study was conducted at the National Public Health Laboratories in Khartoum, Sudan. Urine samples were collected from three hospitals from September to December 2021, and 65 bacterial isolates were recovered from patients with clinical symptoms of UTI. Bacterial identification and antimicrobial susceptibility testing were performed using standard bacteriological methods and Kirby-Bauer's disc diffusion method. Genotyping for Extended Spectrum Beta-Lactamase (EsβL) production was conducted using multiplex PCR.
resultsOf the 65 bacterial isolates, 27 (41.5%) were community-acquired UTI patients (C-UTIs), and 38 (58.5%) were hospital acquired UTI patients (H-UTIs). Males were more commonly affected by H-UTIs (68.4%), while females represented the majority among C-UTIs (85.2%). Gram-negative bacteria were predominant in urinary isolates (81.54%), with Escherichia coli being the most common organism (49.1%). Gram-positive organisms accounted for 18.46% of isolates, with Enterococcus spp. being the most prevalent. In-vitro susceptibility analysis revealed that meropenem was the most effective antibiotic in Gram-negative isolates (77.4%), while nitrofurantoin was the most effective in Gram-positive isolates (91.67%). EsβL production was detected in 11/23 (47.8%) of the tested isolates with Escherichia coli being the most common ESβL producer.
conclusionThis study highlights the pattern of antibiotic-resistant bacteria in UTIs and their significance. Continuous monitoring, development of local antibiograms, and implementation of surveillance programs are essential for guiding empirical antibiotic use and managing UTIs effectively, especially in resource-limited settings like Sudan.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.