Microbial Etiology of Acute Febrile Illness in Children Presenting to Hospitals in 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.