ArticleBMC infectious diseases2025
Antibiotic susceptibility patterns of clinical isolates of salmonella species producing extended spectrum beta lactamases as predictor of multidrug resistance in a tertiary hospital, Southeastern Nigeria.
Article in BMC infectious diseases, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
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
backgroundThe global rise of multidrug-resistant (MDR) and extended-spectrum β-lactamase–producing (ESBL) Salmonella undermines treatment efficacy and threatens public health, particularly in low-resource settings. In Nigeria, data on resistance mechanisms in clinical isolates remain sparse. This study evaluates whether resistance genes and antibiogram profiles can reliably predict MDR and ESBL phenotypes to enhance early detection and surveillance.
methodsThis cross-sectional, laboratory-based study was conducted from January-December 2024 and analyzed 265 clinical samples (241 faecal, 24 blood) from patients with suspected enteric fever at a Nigerian tertiary hospital. Sixty-five Salmonella isolates were identified via convenience sampling using standard microbiological methods and tested for antibiotic susceptibility using the Kirby–Bauer disk diffusion method, per CLSI guidelines. ESBL production was screened by Double Disc Synergy Test, and PCR assays were performed to detect blaTEM, blaSHV, tetA, qnrA/B, and sul1 genes. MDR was defined as resistance to ≥ 3 antibiotic classes. Statistical analyses included chi-square tests, logistic regression (α = 0.05), and machine learning models: Classification and Regression Trees (CART), and Random Forest. SHAP (Shapley Additive Explanations) was used for interpretability.
resultsSalmonella was isolated in 65 of 265 samples (26.9%), all from fecal specimens. Resistance was highest to amoxicillin/clavulanic acid (98.5%), tetracycline (96.9%), and sulfamethoxazole/trimethoprim (90.8%) while imipenem and polymyxin B remained effective with 96.9% and 95.4% susceptibility rate respectively. ESBL production was confirmed in 18 isolates (27.7%), while 28 (43.1%) met MDR criteria. The MDR rate was 89.2%, with a mean multiple antibiotic resistance index (MARI) of 0.52. BlaTEM (77.8%), tetA (72.2%), and sul1 (61.1%) were the most prevalent resistance genes. ESBL status was strongly associated with MDR (aOR:4.6; 95%CI:1.5–14.3; p < 0.01). CTX resistance, blaTEM, tetA, and sul1 demonstrated the strongest predictive power for MDR, with respective AUCs of 0.91, 0.88, 0.82, and 0.78. These markers consistently ranked highest across multiple predictive modelling approaches, with SHAP analysis confirming their dominant contribution to MDR classification.
conclusionResistance genes and antibiogram markers—particularly blaTEM and CTX resistance—predict MDR and ESBL status reliably. Leveraging these markers through machine learning—combined with SHAP-based interpretability—enables early, accurate detection and supports targeted antimicrobial interventions in resource-limited settings. CLINICAL TRIAL: Not applicable.
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.