Evidence map›Paper›PMID 33987400›Full record

ArticleAnnals of translational medicine2021

Comparison of traditional methods and high-throughput genetic sequencing in the detection of pathogens in pulmonary infectious diseases.

Xianqiu Chen, Kebin Cheng, Xiaoli Sun, Yuan Zhang, Zu Cao, Jianxiong Li, Jiuwu Bai, Haiwen Lu, Shuyi Gu, Li Zhang and 3 more

Abstract read
In one paragraph

Article in Annals of translational medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Modified percutaneous vertebral body-intervertebral disc biopsy combined with genetic testing for the diagnosis of early-stage pyogenic spondylodiscitis: a retrospective cohort study.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
    Article
  2. Review
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

13 authors.

Xianqiu ChenDepartment of Respiratory and Critical Care Medicine, Shanghai Pulmonary Hospital, Tongji University, School of Medicine, Shanghai, China.
Kebin ChengDepartment of Respiratory and Critical Care Medicine, Shanghai Pulmonary Hospital, Tongji University, School of Medicine, Shanghai, China.
Xiaoli SunDepartment of Respiratory and Critical Care Medicine, Shanghai Pulmonary Hospital, Tongji University, School of Medicine, Shanghai, China.
Yuan ZhangDepartment of Respiratory and Critical Care Medicine, Shanghai Pulmonary Hospital, Tongji University, School of Medicine, Shanghai, China.
Zu CaoDepartment of Respiratory and Critical Care Medicine, Shanghai Pulmonary Hospital, Tongji University, School of Medicine, Shanghai, China.
Jianxiong LiDepartment of Respiratory and Critical Care Medicine, Shanghai Pulmonary Hospital, Tongji University, School of Medicine, Shanghai, China.
Jiuwu BaiDepartment of Respiratory and Critical Care Medicine, Shanghai Pulmonary Hospital, Tongji University, School of Medicine, Shanghai, China.
Haiwen LuDepartment of Respiratory and Critical Care Medicine, Shanghai Pulmonary Hospital, Tongji University, School of Medicine, Shanghai, China.
Shuyi GuDepartment of Respiratory and Critical Care Medicine, Shanghai Pulmonary Hospital, Tongji University, School of Medicine, Shanghai, China.
Li ZhangDepartment of Respiratory and Critical Care Medicine, Shanghai Pulmonary Hospital, Tongji University, School of Medicine, Shanghai, China.
Jinfu XuDepartment of Respiratory and Critical Care Medicine, Shanghai Pulmonary Hospital, Tongji University, School of Medicine, Shanghai, China.
Ping JiangDepartment of Respiratory and Critical Care Medicine, Shanghai Pulmonary Hospital, Tongji University, School of Medicine, Shanghai, China.
Shuo LiangDepartment of Respiratory and Critical Care Medicine, Shanghai Pulmonary Hospital, Tongji University, School of Medicine, Shanghai, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe major causes of pulmonary infections are various microorganisms. This study aimed to compare the positive rates of pathogenic microorganism DNA/RNA high-throughput genetic sequencing (PMseq), which is an emerging technique, with traditional methods for pulmonary disease detection, and to investigate the differences in different sample types.

methodsBronchoalveolar lavage fluid (BALF) and venous blood samples from 104 patients were collected for detection.

resultsThe positive rates of PMseq in BALF and venous blood were both significantly higher than those of traditional methods in the same sample (P<0.001). For BALF, the detection sensitivities were 96.9% for non-febrile patients and 100% for febrile patients. For venous blood, the detection sensitivities were 50.0% for non-febrile patients and 81.3% for febrile patients. There was no statistical difference in the sensitivity of venous blood samples with or without fever (P=0.075). For patients without fever, the sensitivity of BALF was much higher than venous blood samples (P<0.001). In patients with fever, there were no significant differences between different samples.

conclusionsThis study showed that PMseq has a higher positive rate for the detection of pulmonary diseases. For patients without fever, it is recommended to use BALF instead of venous blood samples because of the higher sensitivity. However, for patients with fever, venous blood samples can be used when bronchoalveolar lavage is inconvenient.

Indexed as

bronchoalveolar lavage fluid (BALF)feverPathogenic microorganism DNA/RNA high-throughput genetic sequencing (PMseq)sensitivityvenous blood

Identifiers

PMID33987400
PMCPMC8106068

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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.