Evidence map›Paper›PMID 33585263›Full record

ArticleFrontiers in cellular and infection microbiology2020

Improving Pulmonary Infection Diagnosis with Metagenomic Next Generation Sequencing.

Yi-Yi Qian, Hong-Yu Wang, Yang Zhou, Hao-Cheng Zhang, Yi-Min Zhu, Xian Zhou, Yue Ying, Peng Cui, Hong-Long Wu, Wen-Hong Zhang and 2 more

Registry-linked trialAbstract read
In one paragraph

Article in Frontiers in cellular and infection microbiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06307405 (To Explore the Application of mNGS Detection Technology in the Diagnosis of Pulmonary Infection), which is not on this map. Cited by 94 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
94citing papers in PubMed, 1 pooled it
–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.

NCT06307405 unknown statusnot on this mapstarted 2024, after this paper: background citation

To Explore the Application of mNGS Detection Technology in the Diagnosis of Pulmonary Infection

TypeobservationalSponsorYunfeng HouRan2024 to 2025Enrolled50ConditionsPulmonary InfectionArmsnext generation sequencing
3 · Its place in the literature

Who cites it

94 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Rapid diagnosis ofFrontiers in cellular and infection microbiology · 2022
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34 more citing papers are in PubMed but not listed here.

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

12 authors.

Yi-Yi QianDepartment of Infectious Diseases, Huashan Hospital, Fudan University, Shanghai, China.
Hong-Yu WangDepartment of Infectious Diseases, Huashan Hospital, Fudan University, Shanghai, China.
Yang ZhouDepartment of Infectious Diseases, Huashan Hospital, Fudan University, Shanghai, China.
Hao-Cheng ZhangDepartment of Infectious Diseases, Huashan Hospital, Fudan University, Shanghai, China.
Yi-Min ZhuDepartment of Infectious Diseases, Huashan Hospital, Fudan University, Shanghai, China.
Xian ZhouDepartment of Infectious Diseases, Huashan Hospital, Fudan University, Shanghai, China.
Yue YingDepartment of Infectious Diseases, Huashan Hospital, Fudan University, Shanghai, China.
Peng CuiDepartment of Infectious Diseases, Huashan Hospital, Fudan University, Shanghai, China.
Hong-Long WuBGI PathoGenesis Pharmaceutical Technology Co., Ltd, BGI-Shenzhen, Shenzhen, China.
Wen-Hong ZhangDepartment of Infectious Diseases, Huashan Hospital, Fudan University, Shanghai, China.
Jia-Lin JinDepartment of Infectious Diseases, Huashan Hospital, Fudan University, Shanghai, China.
Jing-Wen AiDepartment of Infectious Diseases, Huashan Hospital, Fudan University, Shanghai, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Pulmonary infections are among the most common and important infectious diseases due to their high morbidity and mortality, especially in older and immunocompromised individuals. However, due to the limitations in sensitivity and the long turn-around time (TAT) of conventional diagnostic methods, pathogen detection and identification methods for pulmonary infection with greater diagnostic efficiency are urgently needed. In recent years, unbiased metagenomic next generation sequencing (mNGS) has been widely used to detect different types of infectious pathogens, and is especially useful for the detection of rare and newly emergent pathogens, showing better diagnostic performance than traditional methods. There has been limited research exploring the application of mNGS for the diagnosis of pulmonary infections. In this study we evaluated the diagnostic efficiency and clinical impact of mNGS on pulmonary infections. A total of 100 respiratory samples were collected from patients diagnosed with pulmonary infection in Shanghai, China. Conventional methods, including culture and standard polymerase chain reaction (PCR) panel analysis for respiratory tract viruses, and mNGS were used for the pathogen detection in respiratory samples. The difference in the diagnostic yield between conventional methods and mNGS demonstrated that mNGS had higher sensitivity than traditional culture for the detection of pathogenic bacteria and fungi (95% vs 54%; p<0.001). Although mNGS had lower sensitivity than PCR for diagnosing viral infections, it identified 14 viral species that were not detected using conventional methods, including multiple subtypes of human herpesvirus. mNGS detected viruses with a genome coverage >95% and a sequencing depth >100× and provided reliable phylogenetic and epidemiological information. mNGS offered extra benefits, including a shorter TAT. As a complementary approach to conventional methods, mNGS could help improving the identification of respiratory infection agents. We recommend the timely use of mNGS when infection of mixed or rare pathogens is suspected, especially in immunocompromised individuals and or individuals with severe conditions that require urgent treatment.

Indexed as

High-Throughput Nucleotide SequencingMetagenomicsAgedChinaHumansPhylogenySensitivity and Specificitydiagnosismetagenomicnext generation sequencingpulmonary infectionrespiration tract infection

Identifiers

PMID33585263
PMCPMC7874146

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Registered trials

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.