RT Journal Article
SR Electronic
T1 Integrating Host Response and Unbiased Microbe Detection for Lower Respiratory Tract Infection Diagnosis in Critically Ill Adults
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 341149
DO 10.1101/341149
A1 Charles Langelier
A1 Katrina L Kalantar
A1 Farzad Moazed
A1 Michael R. Wilson
A1 Emily Crawford
A1 Thomas Deiss
A1 Annika Belzer
A1 Samaneh Bolourchi
A1 Saharai Caldera
A1 Monica Fung
A1 Alejandra Jauregui
A1 Katherine Malcolm
A1 Amy Lyden
A1 Lillian Khan
A1 Kathryn Vessel
A1 Jenai Quan
A1 Matt Zinter
A1 Charles Y. Chiu
A1 Eric D. Chow
A1 Jenny Wilson
A1 Steve Miller
A1 Michael A. Matthay
A1 Katherine S. Pollard
A1 Stephanie Christenson
A1 Carolyn S. Calfee
A1 Joseph L. DeRisi
YR 2018
UL http://biorxiv.org/content/early/2018/06/11/341149.abstract
AB Lower respiratory tract infections (LRTI) lead to more deaths each year than any other infectious disease category(1). Despite this, etiologic LRTI pathogens are infrequently identified due to limitations of existing microbiologic tests(2). 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.SIGNIFICANCE STATEMENT Lower respiratory tract infections (LRTI) are the leading cause of infectious disease-related death worldwide yet remain challenging to diagnose because of limitations in existing microbiologic tests. In critically ill patients, non-infectious respiratory syndromes that resemble LRTI further complicate diagnosis and confound targeted treatment. To address this, we developed a novel metagenomic sequencing-based approach that simultaneously interrogates three core elements of acute airway infections: the pathogen, lung microbiome and host response. We studied this approach in a prospective cohort of critically ill patients with acute respiratory failure and found that combining pathogen, microbiome and host gene expression metrics achieved accurate LRTI diagnosis and identified etiologic pathogens in patients with clinically identified infections but otherwise negative testing.Funding NHLBI K12HL119997 (Langelier C), NHLBI K23HL123778 (Christensen S), NIAID P01AI091575 and the Chan Zuckerberg Biohub (DeRisi JL), NHLBI K23 HL136844 (Moazed F), NHLBI R01HL110969, K24HL133390, R35HL140026 (Calfee C), Gladstone Institutes (Pollard KS).