Evidence map›Paper›PMID 39835383›Full record

ArticleThe clinical respiratory journal2025

Significance of Combining Bronchoalveolar Lavage Fluid With Targeted Next-Generation Sequencing in the Pathogen Detection-Based Diagnosis of Pulmonary Infections.

Jiangbo Liu, Bo Yang, Yu Wu, Guihong Yang, Xiaojiu Zha, Wei Jiang

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Article in The clinical respiratory journal, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

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2citing papers in PubMed
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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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2 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Jiangbo LiuDepartment of Pulmonary and Critical Care Medicine, Tianjin First Central Hospital, Tianjin, China.ORCID https://orcid.org/0009-0001-6846-1861
Bo YangDepartment of Thoracic Surgery, Tianjin First Central Hospital, Tianjin, China.
Yu WuDepartment of Pulmonary and Critical Care Medicine, Tianjin First Central Hospital, Tianjin, China.
Guihong YangDepartment of Pulmonary Diseases, Dafang County Traditional Chinese Medicine Hospital, Bijie, Guizhou, China.
Xiaojiu ZhaDepartment of Pulmonary and Critical Care Medicine, Tianjin First Central Hospital, Tianjin, China.
Wei JiangInfectious Diseases Department, Tianjin First Central Hospital, Tianjin, China.

Funding

Tianjin Clinical Key Specialty Construction Project Funding (2024) - Respiratory Department 8252
6 · The paper itself

Abstract

objectiveIn this study, we investigated the application value of bronchoalveolar lavage fluid (BALF) combined with targeted next-generation sequencing (tNGS) in the pathogen detection-based diagnosis of patients with lung infections.

methodA retrospective analysis was conducted on patients who underwent tracheoscopy and conventional microbiological tests (CMTs) on BALF, coupled with metagenomic next-generation sequencing (mNGS) or tNGS. This investigation encompassed individuals with suspected lung infections at Tianjin First Central Hospital from March 2023 to July 2023. Diagnostic rates based on pathogens detected via tNGS were compared with CMTs within the tNGS group. Additionally, diagnostic rates obtained through tNGS were compared with mNGS between the two groups.

resultsThe data of a total of 169 patients (78 in the tNGS group and 91 in the mNGS group) were collected, and 145 patients (67 in the tNGS group and 78 in the mNGS group) were finally diagnosed with lung infections. The comprehensive positive pathogen detection-based diagnosis rate for tNGS was 86.6%, with a single-pathogen lung infection diagnosis rate of 85.7% and a mixed-pathogen pulmonary infection diagnosis rate of 88.0%. In contrast, the overall positive pathogen detection-based diagnosis rate for CMTs was 38.8%, comprising a single-pathogen pulmonary infection diagnosis rate of 28.6% and a mixed-pathogen pulmonary infection diagnosis rate of 20.0%. The difference in positive diagnosis rate was deemed statistically significant (p < 0.05). In the mNGS group, the overall pathogen detection-based diagnosis rate was 89.7%, with a single-pathogen pulmonary infection diagnosis rate of 84.9%, and a 100% diagnosis rate for mixed-pathogen pulmonary infections. There was no statistically significant difference in the positive diagnosis rate when compared with the tNGS group (p > 0.05).

conclusionIn patients with pulmonary infections, the diagnosis rate based on BALF pathogen detection using tNGS exceeded that of CMTs, showing no statistically significant difference compared to mNGS.

Indexed as

Bronchoalveolar Lavage FluidHigh-Throughput Nucleotide SequencingRespiratory Tract InfectionsAdultAgedBronchoscopyFemaleHumansMaleMiddle AgedRetrospective Studiesbronchoalveolar lavage fluidconventional microbiological testsmetagenomic next‐generation sequencingpulmonary infectiontargeted next‐generation sequencing

Identifiers

PMID39835383
PMCPMC11747687

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.