Microbial Etiology of Acute Febrile Illness in Children Presenting to Hospitals in Ghana
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Date
2019-07
Authors
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Journal ISSN
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Publisher
University Of Ghana
Abstract
Background
Acute febrile illness (AFI) is responsible for a significant number of childhood mortality
and morbidity and remains a common clinical presentation at most hospitals. Lack of
appropriate screening techniques present a challenge in identifying potential pathogens
associated with AFI. This study investigated the microbial etiology of AFI among febrile
children, evaluated the potential use of inflammatory mediators as biomarkers of fever, and
developed a web-based model to predict the infection status of febrile children.
Methods
858 children aged 1-15 years with acute uncomplicated fever were clinically screened
using point-of-care and advanced diagnostic methods. A panel of laboratory tests
comprising malaria microscopy, malaria Rapid Diagnostic Test (RDT), complete blood
count, blood and urine cultures and polymerase chain reaction (PCR) were employed to
screen samples for parasitic, bacterial, and viral pathogens. Concentrations of serum
cytokines and hematological parameters were respectively measured using a Luminexbased
magnetic bead assay and a fully automated hematology analyzer. The test of
sensitivity and specificity, as well as the area under the curve (AUC) of the receiver
operating characteristic (ROC) of cytokines and hematological parameters, were used as
measures of diagnostic accuracy to predict fever and in the selection of the suitable data
mining technique to model malaria and bacterial infection status of febrile children.
Results
Etiologies of fever were identified in 43.7% (374/858) of children studied. From blood
samples analyzed, 38.6% (331/858) tested positive for the Plasmodium parasite which was the most frequent pathogen detected. From 140 blood and 137 urine cultures performed, 59
organisms were identified. The most common organisms isolated were Staphylococcus
aureus (4.7%), Escherichia coli (3.2%), Group D Streptococcus (2.5%), Pseudomonas
aeruginosa (1.8%), Non-typhoidal salmonellae (1.4%), Coagulase negative staphylococci
(1.4%), Citrobacter freundii (1.1%), Enterobacter clocoae (1.1%), Salmonella Typhi
(0.9%), Streptococcus pneumonia (0.7%) and Klebsiella pneumonia (0.4%). Pathogens
detected using TaqMan-based PCR from 166 blood samples included: Dengue virus
(1.2%), Coxiella burnetti (0.6%), Rickettsia (3.0%), HIV (0.6%) and Plasmodium
falciparum (37.9%). Of the enrolled children, 3.2% had Plasmodium-bacteria coinfections:
Plasmodium-Staphylococcus aureus (0.9%), Plasmodium-dengue (0.3%), and
Plasmodium-Rickettsia (0.6%). From the cytokine analysis, tumor necrosis factor (TNF-α)
with sensitivity of 84.4% (95% CI: 75.5-91.0) and specificity of 72.2% (95% CI: 46.5-
90.3) was the best predictor of fever, having AUC for the ROC curve to be 0.7.
Lymphocyte (LYM-%) was the best hematological predictor of fever, with sensitivity of
65.2%, specificity of 67.3% and a ROC of 0.78. Naïve Bayes model, which incorporated
only clinical symptoms, proved useful for the development of the interactive tool to predict
infection status of children with AFI.
Conclusion
Malaria remains a major contributor of AFI in the study area, despite additional diagnoses
of bacterial and viral origin. Dengue virus, Rickettsia felis and Coxiella burnetti were
detected among the children but not clinically diagnosed. Febrile illnesses due to comorbid
infection are common and call for differential diagnosis of AFI to ensure judicious
use of drugs and to avoid evolution of drug resistance. In addition, routine haematological parameters including lymphocyte, and circulating cytokines such as TNF-α are useful and
independent prognostic factors for fever. A web-based clinical decision tool has been
developed to predict infection status of febrile patients.
Description
PhD. Molecular Cell Biology of Infectious Disease
Keywords
Acute Febrile illness (AFI), Ghana, Microbial Etiology