Evidence map›Paper›PMID 36389162›Full record

ArticleFrontiers in cellular and infection microbiology2022

Evaluation of the metagenomic next-generation sequencing performance in pathogenic detection in patients with spinal infection.

Yi Zhang, Jinmei Chen, Xiaoli Yi, Zhiheng Chen, Ting Yao, Zhenghao Tang, Guoqing Zang, Xuejie Cao, Xiaofeng Lian, Xiaohua Chen

Open access · goldAbstract read
In one paragraph

Article in Frontiers in cellular and infection microbiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed, 2 pooled it
3.5field-weighted citation impact, top 7% of its field
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

17 citing papers in PubMed, 2 syntheses or guidelines pooled it, 21 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Article
  4. Review
  5. ​C-arm-guided percutaneous biopsy combined with mNGS: a dual-modality strategy for op​timizing diagnosis and targeted management of spinal infections.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2026
    Review
  6. Article
  7. Article
  8. Observational
  9. A diagnostic model for differentiating tuberculous spondylodiscitis from pyogenic spondylodiscitis based on pathogen-confirmed patients.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2024
    Article
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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

10 authors at 2 institutions in 1 country.

Yi ZhangDepartment of Infectious Diseases, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China.
Jinmei ChenDepartment of Infectious Diseases, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China.
Xiaoli YiGenoxor Medical Science and Technology Inc., Shanghai, China.
Zhiheng ChenDepartment of Orthopedics, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China.
Ting YaoDepartment of Infectious Diseases, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China.
Zhenghao TangDepartment of Infectious Diseases, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China.
Guoqing ZangDepartment of Infectious Diseases, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China.
Xuejie CaoGenoxor Medical Science and Technology Inc., Shanghai, China.
Xiaofeng LianDepartment of Orthopedics, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China.
Xiaohua ChenDepartment of Infectious Diseases, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China.
Shanghai Jiao Tong University · CNShanghai Sixth People's Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Spinal infection is a rarely occurred pathology, whose diagnosis remains a major challenge due to the low sensitivity of culturing techniques. Metagenomic next-generation sequencing (mNGS) is a novel approach to identify the pathogenic organisms in infectious diseases. In this study, mNGS technology was adopted for pathogenic detection in spinal infection from the tissue and pus samples. Additionally, the diagnostic performance of mNGS for spinal infection was evaluated, by comparing it with that of the conventional microbial culture, with the histopathological results as the gold standard. Overall, 56 samples from 38 patients were enrolled for mNGS testing, and 69 samples were included for microbial culture. 30 patients (78.95%) were identified to be positive by the mNGS method, which was higher than that of microbial culture (17, 44.74%). The sensitivity and specificity of mNGS with pus samples were 84.2% and 100.0%, respectively, which outperformed those of microbial culture (42.1% and 100.0%). The pathogen identification results were applied to medication guidance, and all 38 patients experienced favorable outcomes at three months, followed-up post-treatment, without any adverse effects. These findings proved that mNGS was superior to microbial culture in pathogenic identification of the spinal infection, thereby showing great promise in guiding drug administration and improving clinical outcomes.

Indexed as

MetagenomeMetagenomicsHigh-Throughput Nucleotide SequencingHumansSensitivity and SpecificitySuppurationdiagnosismetagenomic next-generation sequencing (mNGS)microbial culturepathogenic detectionspinal infection

Identifiers

PMID36389162
PMCPMC9646980
OpenAlexW4307634026

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.