Evidence map›Paper›PMID 38902783›Full record

ArticleRespiratory research2024

A single-center, retrospective study of hospitalized patients with lower respiratory tract infections: clinical assessment of metagenomic next-generation sequencing and identification of risk factors in patients.

Qinghua Gao, Lingyi Li, Ting Su, Jie Liu, Liping Chen, Yongning Yi, Yun Huan, Jian He, Chao Song

Abstract read
In one paragraph

Article in Respiratory research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

20 citing papers in PubMed.

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  12. Precise pathogen detection and clinical characterization of bronchiectasis.Frontiers in cellular and infection microbiology · 2025
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  14. An Atypical Pneumonia Case of Quinolone-RefractoryInfection and drug resistance · 2025
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  15. Article
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  18. Cavitary pulmonary tuberculosis withFrontiers in medicine · 2025
    Article
  19. Observational
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Qinghua Gao *Department of Pulmonary and Critical Care Medicine, Anning First People's Hospital Affiliated to Kunming University of Science and Technology, Kunming, 650302, China.
Lingyi Li *Department of Medical, Hangzhou Matridx Biotechnology, Hangzhou, 311100, China.
Ting SuDepartment of Pulmonary and Critical Care Medicine, Anning First People's Hospital Affiliated to Kunming University of Science and Technology, Kunming, 650302, China.
Jie LiuDepartment of Pulmonary and Critical Care Medicine, Anning First People's Hospital Affiliated to Kunming University of Science and Technology, Kunming, 650302, China.
Liping ChenDepartment of Pulmonary and Critical Care Medicine, Anning First People's Hospital Affiliated to Kunming University of Science and Technology, Kunming, 650302, China.
Yongning YiDepartment of Pulmonary and Critical Care Medicine, Anning First People's Hospital Affiliated to Kunming University of Science and Technology, Kunming, 650302, China.
Yun HuanDepartment of Pulmonary and Critical Care Medicine, Anning First People's Hospital Affiliated to Kunming University of Science and Technology, Kunming, 650302, China.
Jian HeDepartment of Pulmonary and Critical Care Medicine, Anning First People's Hospital Affiliated to Kunming University of Science and Technology, Kunming, 650302, China. ignatias@163.com.
Chao SongDepartment of Medical Imaging, Anning First People's Hospital Affiliated to Kunming University of Science and Technology, Kunming, 650302, China. chaoge6870@163.com.

Funding

Chuxiong Medical College scientific research fund project 2023YYXM01Kunming Health Management Commission 2020-SW(technology)-11Scientific Research Fund project of Education Department of Yunnan Province 2021J0354
6 · The paper itself

Abstract

introductionLower respiratory tract infections(LRTIs) in adults are complicated by diverse pathogens that challenge traditional detection methods, which are often slow and insensitive. Metagenomic next-generation sequencing (mNGS) offers a comprehensive, high-throughput, and unbiased approach to pathogen identification. This retrospective study evaluates the diagnostic efficacy of mNGS compared to conventional microbiological testing (CMT) in LRTIs, aiming to enhance detection accuracy and enable early clinical prediction.

methodsIn our retrospective single-center analysis, 451 patients with suspected LRTIs underwent mNGS testing from July 2020 to July 2023. We assessed the pathogen spectrum and compared the diagnostic efficacy of mNGS to CMT, with clinical comprehensive diagnosis serving as the reference standard. The study analyzed mNGS performance in lung tissue biopsies and bronchoalveolar lavage fluid (BALF) from cases suspected of lung infection. Patients were stratified into two groups based on clinical outcomes (improvement or mortality), and we compared clinical data and conventional laboratory indices between groups. A predictive model and nomogram for the prognosis of LRTIs were constructed using univariate followed by multivariate logistic regression, with model predictive accuracy evaluated by the area under the ROC curve (AUC).

results(1) Comparative Analysis of mNGS versus CMT: In a comprehensive analysis of 510 specimens, where 59 cases were concurrently collected from lung tissue biopsies and BALF, the study highlights the diagnostic superiority of mNGS over CMT. Specifically, mNGS demonstrated significantly higher sensitivity and specificity in BALF samples (82.86% vs. 44.42% and 52.00% vs. 21.05%, respectively, p < 0.001) alongside greater positive and negative predictive values (96.71% vs. 79.55% and 15.12% vs. 5.19%, respectively, p < 0.01). Additionally, when comparing simultaneous testing of lung tissue biopsies and BALF, mNGS showed enhanced sensitivity in BALF (84.21% vs. 57.41%), whereas lung tissues offered higher specificity (80.00% vs. 50.00%). (2) Analysis of Infectious Species in Patients from This Study: The study also notes a concerning incidence of lung abscesses and identifies Epstein-Barr virus (EBV), Fusobacterium nucleatum, Mycoplasma pneumoniae, Chlamydia psittaci, and Haemophilus influenzae as the most common pathogens, with Klebsiella pneumoniae emerging as the predominant bacterial culprit. Among herpes viruses, EBV and herpes virus 7 (HHV-7) were most frequently detected, with HHV-7 more prevalent in immunocompromised individuals. (3) Risk Factors for Adverse Prognosis and a Mortality Risk Prediction Model in Patients with LRTIs: We identified key risk factors for poor prognosis in lower respiratory tract infection patients, with significant findings including delayed time to mNGS testing, low lymphocyte percentage, presence of chronic lung disease, multiple comorbidities, false-negative CMT results, and positive herpesvirus affecting patient outcomes. We also developed a nomogram model with good consistency and high accuracy (AUC of 0.825) for predicting mortality risk in these patients, offering a valuable clinical tool for assessing prognosis.

conclusionThe study underscores mNGS as a superior tool for lower respiratory tract infection diagnosis, exhibiting higher sensitivity and specificity than traditional methods.

Indexed as

High-Throughput Nucleotide SequencingMetagenomicsRespiratory Tract InfectionsAdultAgedBronchoalveolar Lavage FluidFemaleHospitalizationHumansMaleMiddle AgedPredictive Value of TestsRetrospective StudiesRisk FactorsDiagnostic efficacyLower respiratory tract infections (LRTIs)Metagenomic next-generation sequencing (mNGS)NomogramPredictive model

Identifiers

PMID38902783
PMCPMC11191188

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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.