A next generation DNA sequencing study offers a streamlined protocol that integrates the genomic portrait of pathogen, microbiome, and host transcriptome for a more accurate diagnosis of lower respiratory tract infections.
Lower respiratory tract infections (LRTIs) are the leading causes of infectious disease-related deaths worldwide but continue to be challenging to diagnose because of limitations in existing microbiologic tests. In critically ill patients, noninfectious respiratory syndromes that resemble LRTIs further complicate diagnosis and interfere with targeted treatments.
Now, a new study, published in the Proceedings of the National Academy of Sciences USA, has reported on the development of a metagenomic sequencing-based approach that simultaneously examines and identifies 3 core elements of acute airway infections—the pathogen, airway microbiome, and host response—that when used in critically ill patients with acute respiratory failure was able to achieve accurate LRTI diagnosis and identify etiologic pathogens in patients who had clinically identified infections but otherwise negative testing.1
The research team performed metagenomic next generation DNA sequencing (mNGS) on tracheal aspirates from 92 adults admitted to the hospital intensive care unit with acute respiratory failure. Patients underwent testing with standard-of-care microbiologic diagnostics, and subjects with LRTI infections were identified using CDC surveillance case definitions and retrospective electronic health record review. Investigators were blinded to mNGS results.
The patients were assigned to 1 of 4 groups:
Using extracted nucleic acid samples, investigators performed both metagenomic DNA sequencing and RNA sequencing. They developed algorithms to distinguish respiratory pathogens from background commensal flora to enhance detection of LRTI etiology. To differentiate patients with LRTI from those with noninfectious critical respiratory illnesses, the researchers created metrics of LRTI probability based on pathogen, airway microbiome diversity, and host gene expression.
The researchers developed a rule-based model and a logistics regression model 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%: the pathogen metric performed with an area under the receiver-operating curve (AUC) of 0.96 (95% CI, 0.63-0.98), and the host transcriptional classifier performed with an AUC of 0.88 (95% CI, 0.75-1.00). Combining the 2 achieved a negative predictive value of 100%, suggesting that patients with a negative screening test truly do not have the disease.
“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,” the researchers conclude. They note that even with the best available clinical diagnostics, a contributory pathogen can be detected in only 38% of adults with community-acquired pneumonia due to the low sensitivity and time requirements of culture, and the limited number of microbes detectable by current assays. Given this lack of definitive microbiologic diagnosis, for example, clinicians may presume symptoms are due to a noninfectious inflammatory condition and initiate corticosteroids, which can exacerbate the hidden infection.
In addition, the researchers note that their use of mNGS allows for all potential infectious agents to be simultaneously assessed, avoiding the need to order multiple individual tests for each different pathogen of concern. Future studies in a larger validation cohort can help optimize host and microbe LRTI rule-out thresholds and further assess test performance before deployment in a clinical setting, the researchers advise.