Integrating host response and unbiased microbe detection for lower respiratory tract infection diagnosis in critically ill adults.

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ID: 48851
2018
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Ranked #62 of 292 articles by views in Proceedings of the National Academy of Sciences of the United States of America

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Abstract
Lower respiratory tract infections (LRTIs) lead to more deaths each year than any other infectious disease category. Despite this, etiologic LRTI pathogens are infrequently identified due to limitations of existing microbiologic tests. In critically ill patients, noninfectious inflammatory syndromes resembling LRTIs further complicate diagnosis. To address the need for improved LRTI diagnostics, we performed metagenomic next-generation sequencing (mNGS) on tracheal aspirates from 92 adults with acute respiratory failure and simultaneously assessed pathogens, the airway microbiome, and the host transcriptome. To differentiate pathogens from respiratory commensals, we developed a rules-based model (RBM) and logistic regression model (LRM) in a derivation cohort of 20 patients with LRTIs or noninfectious acute respiratory illnesses. When tested in an independent validation cohort of 24 patients, both models achieved accuracies of 95.5%. We next developed pathogen, microbiome diversity, and host gene expression metrics to identify LRTI-positive patients and differentiate them from critically ill controls with noninfectious acute respiratory illnesses. When tested in the validation cohort, the pathogen metric performed with an area under the receiver-operating curve (AUC) of 0.96 (95% CI, 0.86-1.00), the diversity metric with an AUC of 0.80 (95% CI, 0.63-0.98), and the host transcriptional classifier with an AUC of 0.88 (95% CI, 0.75-1.00). Combining these achieved a negative predictive value of 100%. This study suggests that a single streamlined protocol offering an integrated genomic portrait of pathogen, microbiome, and host transcriptome may hold promise as a tool for LRTI diagnosis.
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langelier2018integratingproceedings Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Langelier, Charles;Kalantar, Katrina L;Moazed, Farzad;Wilson, Michael R;Crawford, Emily D;Deiss, Thomas;Belzer, Annika;Bolourchi, Samaneh;Caldera, Saharai;Fung, Monica;Jauregui, Alejandra;Malcolm, Katherine;Lyden, Amy;Khan, Lillian;Vessel, Kathryn;Quan, Jenai;Zinter, Matt;Chiu, Charles Y;Chow, Eric D;Wilson, Jenny;Miller, Steve;Matthay, Michael A;Pollard, Katherine S;Christenson, Stephanie;Calfee, Carolyn S;DeRisi, Joseph L;
Journal Proceedings of the National Academy of Sciences of the United States of America
Year 2018
DOI
10.1073/pnas.1809700115
URL
Keywords
next-generation sequencing transcriptome pneumonia mechanical ventilation lower respiratory tract infection

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