Integrating host response and unbiased microbe detection for lower respiratory tract infection diagnosis in critically ill adults.
Charles LangelierKatrina L KalantarFarzad MoazedMichael R WilsonEmily D CrawfordThomas DeissAnnika BelzerSamaneh BolourchiSaharai CalderaMonica FungAlejandra JaureguiKatherine MalcolmAmy LydenLillian KhanKathryn VesselJenai QuanMatt ZinterCharles Y ChiuEric D ChowJenny WilsonSteve MillerMichael A MatthayKatherine S PollardStephanie ChristensonCarolyn S CalfeeJoseph L DeRisiPublished in: Proceedings of the National Academy of Sciences of the United States of America (2018)
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.
Keyphrases
- respiratory tract
- respiratory failure
- gene expression
- end stage renal disease
- liver failure
- newly diagnosed
- ejection fraction
- chronic kidney disease
- prognostic factors
- peritoneal dialysis
- extracorporeal membrane oxygenation
- gram negative
- oxidative stress
- copy number
- randomized controlled trial
- mechanical ventilation
- dna methylation
- aortic dissection
- drug induced
- single cell
- rna seq
- candida albicans
- infectious diseases
- transcription factor
- antimicrobial resistance
- microbial community
- artificial intelligence
- cell free