Biomarkers to Distinguish Bacterial From Viral Pediatric Clinical Pneumonia in a Malaria-Endemic Setting. (3rd February 2021)

• Record Type: Journal Article
• Authors: Gillette, Michael A; Mani, D R; Uschnig, Christopher; Pellé, Karell G; Madrid, Lola; Acácio, Sozinho; Lanaspa, Miguel; Alonso, Pedro; Valim, Clarissa; Carr, Steven A; Schaffner, Stephen F; MacInnis, Bronwyn; Milner, Danny A; Bassat, Quique; Wirth, Dyann F
• Is Part Of: Clinical infectious diseases. Volume 73:Number 11(2021)
• Page Start: e3939
• Page End: e3948
• Publication Date: 2021-02-03
• Subjects: malaria -- pediatric -- pneumonia -- biomarker -- diagnostic; Communicable diseases -- Periodicals; 616.905
• DOI: 10.1093/cid/ciaa1843
• Languages: English
• ISSNs: 1058-4838

Abstract

Abstract: Background: Differential etiologies of pediatric acute febrile respiratory illness pose challenges for all populations globally, but especially in malaria-endemic settings because the pathogens responsible overlap in clinical presentation and frequently occur together. Rapid identification of bacterial pneumonia with high-quality diagnostic tools would enable appropriate, point-of-care antibiotic treatment. Current diagnostics are insufficient, and the discovery and development of new tools is needed. We report a unique biomarker signature identified in blood samples to accomplish this. Methods: Blood samples from 195 pediatric Mozambican patients with clinical pneumonia were analyzed with an aptamer-based, high-dynamic-range, quantitative assay (~1200 proteins). We identified new biomarkers using a training set of samples from patients with established bacterial, viral, or malarial pneumonia. Proteins with significantly variable abundance across etiologies (false discovery rate <0.01) formed the basis for predictive diagnostic models derived from machine learning techniques (Random Forest, Elastic Net). Validation on a dedicated test set of samples was performed. Results: Significantly different abundances between bacterial and viral infections (219 proteins) and bacterial infections and mixed (viral and malaria) infections (151 proteins) were found. Predictive models achieved >90% sensitivity and >80% specificity, regardless of number of pathogen classes. Bacterial pneumonia was strongly associated with neutrophil markers—in particular, degranulation including HP, LCN2, LTF, MPO, MMP8, PGLYRP1, RETN, SERPINA1, S100A9, and SLPI. Conclusions: Blood protein signatures highly associated with neutrophil biology reliably differentiated bacterial pneumonia from other causes. With appropriate technology, these markers could provide the basis for a rapid diagnostic for field-based triage for antibiotic treatment of pediatric pneumonia. Abstract : Identification of new biomarkers to distinguish between viral and bacterial pneumonia is needed. These markers could provide the basis for a rapid diagnostic for field-based triage for antibiotic treatment of pediatric pneumonia.