Biomarkers to Distinguish Bacterial From Viral Pediatric Clinical Pneumonia in a Malaria-Endemic Setting

Author:

Gillette Michael A123,Mani D R1,Uschnig Christopher14,Pellé Karell G4,Madrid Lola56,Acácio Sozinho6,Lanaspa Miguel56,Alonso Pedro56,Valim Clarissa47,Carr Steven A1,Schaffner Stephen F14,MacInnis Bronwyn14,Milner Danny A1348,Bassat Quique5691011,Wirth Dyann F14ORCID

Affiliation:

1. Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA

2. Division of Pulmonary and Critical Care Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA

3. Harvard Medical School, Boston, Massachusetts, USA

4. Harvard T. H. Chan School of Public Health, Department of Immunology and Infectious Diseases, Boston, Massachusetts, USA

5. ISGlobal, Hospital Clínic–Universitat de Barcelona, Barcelona, Spain

6. Centro de Investigação em Saúde de Manhiça (CISM), Maputo, Mozambique

7. Department of Global Health, Boston University School of Public Health, Boston, Massachusetts, USA

8. American Society for Clinical Pathology, Chicago, Illinois, USA

9. Catalan Institution for Research and Advanced Studies (ICREA) Barcelona, Spain

10. Pediatric Infectious Diseases Unit, Pediatrics Department, Hospital Sant Joan de Déu (University of Barcelona), Barcelona, Spain

11. Consorcio de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), Madrid, Spain

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.

Publisher

Oxford University Press (OUP)

Subject

Infectious Diseases,Microbiology (medical)

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