Evidence map›Paper›PMID 41376967›Full record

ArticleJournal of thoracic disease2025

Performance of metagenomic next-generation sequencing, Xpert MTB/RIF and acid-fast staining for diagnosing tuberculous pleurisy and empyema.

Hong Liu, Saiguang Ji, Fuchen Xing, Chenyan Wang, Wei Sun, Hongan Shao, Chunmei Hu

Abstract read
In one paragraph

Article in Journal of thoracic disease, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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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

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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

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0 citing papers in PubMed.

No citing paper in PubMed yet.

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

7 authors.

Hong LiuDepartment of Thoracic Surgery, the Second Hospital of Nanjing, Nanjing University of Chinese Medicine, Nanjing, China.
Saiguang JiDepartment of Thoracic Surgery, the Second Hospital of Nanjing, Nanjing University of Chinese Medicine, Nanjing, China.
Fuchen XingDepartment of Thoracic Surgery, the Second Hospital of Nanjing, Nanjing University of Chinese Medicine, Nanjing, China.
Chenyan WangDepartment of Thoracic Surgery, the Second Hospital of Nanjing, Nanjing University of Chinese Medicine, Nanjing, China.
Wei SunDepartment of Thoracic Surgery, the Second Hospital of Nanjing, Nanjing University of Chinese Medicine, Nanjing, China.
Hongan ShaoDepartment of Thoracic Surgery, the Second Hospital of Nanjing, Nanjing University of Chinese Medicine, Nanjing, China.
Chunmei HuDepartment of Tuberculosis, the Second Hospital of Nanjing, Nanjing University of Chinese Medicine, Nanjing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: The detection of Methods: This study enrolled 154 patients suspected of having tuberculosis. Fresh tissues, pleural fluid or pus were collected from these patients and performed mNGS, Xpert, and acid-fast staining (AFS) tests. Their detection performance was statistically analyzed and compared. Results: Overall, the positivity rate of mNGS was 48.05% (74/154), Xpert was 44.44% (44/99), and AFS was 57.34% (82/143). The sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of mNGS were 55.22%, 100%, 100% and 25%, respectively. The sensitivity, specificity, PPV and NPV of Xpert were 48.35%, 100%, 100%, 18.97%, respectively. The sensitivity, specificity, PPV and NPV of acid-fast stain were 58.59%, 53.33%, 91.46%, 13.11%, respectively. A total of 93 samples underwent all three types of testing, and 45.16% (42/93) were completely consistent in the results of the three tests. The analysis results of these samples showed that the sensitivity, specificity, PPV and NPV of mNGS were 65.88%, 100%, 100%, 21.62%, respectively. The sensitivity, specificity, PPV and NPV of Xpert were 49.41%, 100%, 100%, 15.69%, respectively. The sensitivity, specificity, PPV and NPV of acid-fast stain were 57.65%, 62.50%, 94.23%, 12.20%, respectively. The sensitivity of mNGS was significantly higher than that of Xpert (P=0.01). Conclusions: Our research results indicate that mNGS and AFS have higher sensitivity compared to Xpert, while mNGS and Xpert have higher specificity.

Indexed as

acid-fast staining (AFS)metagenomic next-generation sequencing (mNGS)TuberculosisXpert MTB/RIF

Identifiers

PMID41376967
PMCPMC12688588

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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.