4.7 Article

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

Journal

CLINICAL INFECTIOUS DISEASES
Volume 73, Issue 11, Pages E3939-E3948

Publisher

OXFORD UNIV PRESS INC
DOI: 10.1093/cid/ciaa1843

Keywords

malaria; pediatric; pneumonia; biomarker; diagnostic

Funding

  1. Bill and Melinda Gates Foundation [OPP50092]
  2. Government of Mozambique
  3. Spanish Agency for International Development (AECID)
  4. Spanish Ministry of Science and Innovation through the Centro de Excelencia Severo Ochoa 2019-2023 Program [CEX2018-000806-S]
  5. Generalitat de Catalunya through the Centres de Recerca de Catalunya Program

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This study identified blood protein signatures highly associated with neutrophil biology to reliably differentiate bacterial pneumonia from other causes in pediatric patients. These markers could potentially provide the basis for a rapid diagnostic tool for field-based triage for antibiotic treatment of pediatric pneumonia with appropriate technology.
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 (similar to 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.

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