Integrating Host Response and Unbiased Microbe Detection for Lower Respiratory Tract Infection Diagnosis in Critically Ill Adults
Katrina L Kalantar,
Michael R. Wilson,
Charles Y Chiu,
Eric D. Chow,
Michael A. Matthay,
Katherine S. Pollard,
Carolyn S Calfee,
Posted 11 Jun 2018
bioRxiv DOI: 10.1101/341149 (published DOI: 10.1073/pnas.1809700115)
Posted 11 Jun 2018
Lower respiratory tract infections (LRTI) lead to more deaths each year than any other infectious disease category1. Despite this, etiologic LRTI pathogens are infrequently identified due to limitations of existing microbiologic tests2. In critically ill patients, non-infectious inflammatory syndromes resembling LRTI 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 lung microbiome and the host transcriptome. To differentiate pathogens from respiratory commensals, we developed rules-based and logistic regression models (RBM, LRM) in a derivation cohort of 20 patients with LRTI or non-infectious 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 non-infectious acute respiratory illnesses. When tested in the validation cohort, the pathogen metric performed with an 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 0.91 (95% CI = 0.80 - 1.00). Combining all three achieved an AUC of 0.99 (95% CI = 0.97 - 1.00) and 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 novel tool for LRTI diagnosis.
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