Multi-drug resistance in early and late onset neonatal sepsis in a tertiary hospital in Nigeria
Neonatal sepsis is a clinical syndrome characterized by systemic signs of infection, and accompanied by bacteremia in the first month of life and is responsible for 30-50% of total neonatal deaths, each year in developing countries. This study investigated multi-drug resistant organisms associated with early and late onset neonatal sepsis in the University of Ilorin Teaching Hospital (UITH). It was a descriptive cross-sectional study. One hundred and sixty-two blood samples from neonates admitted into the neonatal intensive care unit of UITH with clinical diagnosis of sepsis were obtained. One milliliter of blood was taken per neonate and cultured aerobically in brain heart infusion broth and sub cultured onto blood and MacConkey agar plates. Identification of the isolates was carried out by colonial morphology, Gram stain microscopy and several biochemical tests. Antibiotic susceptibility test was done using the modified Kirby-Bauer method, screening for methicillin resistance Staphylococcus aureus (MRSA) and extended spectrum beta lactamase (ESBL) was done by the cefoxiin- based methods and double disc synergy test respectively. Data analysis was carried out using Microsoft excel version 2007 and Epi-info version 2012. Sepsis was confirmed bacteriologically in 22.2% of the samples. The prevalence of multidrug resistant isolate was 29.0%. The prevalence of MRSA was found to be 37% while that of ESBL producing Enterobacteria was 44.4% with ESBL producing Klebsiella pneumoniae and Escherichia coli prevalence of 50% and 25% respectively. This study shows a high prevalence of Methicillin Resistant Staphylococcus Aureus and Extended Spectrum Beta Lactamase producing Klebsiella pneumoniae and Escherichia coli causing neonatal sepsis in UITH Ilorin.
PlumX Metrics provide insights into the ways people interact with individual pieces of research output (articles, conference proceedings, book chapters, and many more) in the online environment. Examples include, when research is mentioned in the news or is tweeted about. Collectively known as PlumX Metrics, these metrics are divided into five categories to help make sense of the huge amounts of data involved and to enable analysis by comparing like with like.
Copyright (c) 2019 Lateefat O. Sa’adu, Tope O. Obasa, Aishat O. Saka, Mohammed J. Saka, Charles Nwabuisi
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.