Clinical Application and Evaluation of Metagenomic Next-Generation Sequencing in Pulmonary Infection with Pleural Effusion

Application value of metagenomic next-generation sequencing in hematological patients with high-risk febrile neutropenia

Background

Metagenomic next-generation sequencing (mNGS) is a novel non-invasive and comprehensive technique for etiological diagnosis of infectious diseases. However, its practical significance has been seldom reported in the context of hematological patients with high-risk febrile neutropenia, a unique patient group characterized by neutropenia and compromised immune responses.

Methods

This retrospective study evaluated the results of plasma cfDNA sequencing in 164 hematological patients with high-risk febrile neutropenia. We assessed the diagnostic efficacy and clinical impact of mNGS, comparing it with conventional microbiological tests.

Results

mNGS identified 68 different pathogens in 111 patients, whereas conventional methods detected only 17 pathogen types in 36 patients. mNGS exhibited a significantly higher positive detection rate than conventional methods (67.7% vs. 22.0%, P < 0.001). This improvement was consistent across bacterial (30.5% vs. 9.1%), fungal (19.5% vs. 4.3%), and viral (37.2% vs. 9.1%) infections (P < 0.001 for all comparisons). The anti-infective treatment strategies were adjusted for 51.2% (84/164) of the patients based on the mNGS results.

Conclusions

mNGS of plasma cfDNA offers substantial promise for the early detection of pathogens and the timely optimization of anti-infective therapies in hematological patients with high-risk febrile neutropenia.

Clinical Efficacy and Diagnostic Value of Metagenomic Next-Generation Sequencing for Pathogen Detection in Patients with Suspected Infectious Diseases: A Retrospective Study from a Large Tertiary Hospital

Purpose

Metagenomic next-generation sequencing (mNGS) is a powerful yet unbiased method to identify pathogens in suspected infections. However, little is known about its clinical effectiveness. The present study aimed to assess the efficacy of mNGS in routine clinical practice.

Patients and Methods

In this single-center retrospective cohort study, 518 patients with suspected infectious diseases were assessed for inclusion. Among them, each patient had undergone mNGS testing; 407 patients had undergone both microbial culture and mNGS testing. The result of mNGS testing was compared to microbial culture performed concurrently. The diagnostic performance of mNGS was evaluated using the comprehensive clinical diagnosis as the reference standard.

Results

There was a significant difference in the positive detection rates of pathogens between mNGS and culture (331/407, 81.3% vs 79/407, 19.4%, P < 0.001). The sensitivity of mNGS was much higher than the culture method (79.5% vs 21.3%, P < 0.001), especially in sample types of sputum and bronchoalveolar lavage fluid (BALF). Notably, the sensitivity of blood mNGS was relatively lower than other sample types (67.4% vs 88.9-93.8%). Pathogen cfDNA load based on standardized stringently mapped read number at the species level of microorganisms (SDSMRN) was significantly lower in blood than in other sample types from the same patient (P = 0.0003). Importantly, mNGS directly led to a change of treatment regimen in 142 (27.4%) cases, including antibiotic escalation (15.3%), antibiotic de-escalation (9.1%), and early definitive diagnosis to initiate appropriate treatment (3.1%).

Conclusion

Our in-house mNGS platform significantly improved the sensitivity for the diagnosis of infectious diseases. mNGS has the potential to improve clinical outcomes by optimizing antimicrobial therapy.

Evaluation of respiratory samples in etiology diagnosis and microbiome characterization by metagenomic sequencing

Background

The application of clinical mNGS for diagnosing respiratory infections improves etiology diagnosis, however at the same time, it brings new challenges as an unbiased sequencing method informing all identified microbiomes in the specimen.

Methods

Strategy evaluation and metagenomic analysis were performed for the mNGS data generated between March 2017 and October 2019. Diagnostic strengths of four specimen types were assessed to pinpoint the more appropriate type for mNGS diagnosis of respiratory infections. Microbiome complexity was revealed between patient cohorts and infection types. A bioinformatic pipeline resembling diagnosis results was built based upon multiple bioinformatic parameters.

Results

The positive predictive values (PPVs) for mNGS diagnosing of non-mycobacterium, Nontuberculous Mycobacteria (NTM), and Aspergillus were obviously higher in bronchoalveolar lavage fluid (BALF) demonstrating the potency of BALF in mNGS diagnosis. Lung tissues and sputum were acceptable for diagnosis of the Mycobacterium tuberculosis (MTB) infections. Interestingly, significant taxonomy differences were identified in sufficient BALF specimens, and unique bacteriome and virome compositions were found in the BALF specimens of tumor patients. Our pipeline showed comparative diagnostic strength with the clinical microbiological diagnosis.

Conclusions

To achieve reliable mNGS diagnosis result, BALF specimens for suspicious common infections, and lung tissues and sputum for doubtful MTB infections are recommended to avoid the false results given by the complexed respiratory microbiomes. Our developed bioinformatic pipeline successful helps mNGS data interpretation and reduces manual corrections for etiology diagnosis.