Evidence map›Paper›PMID 40041441›Full record

ArticleOpen forum infectious diseases2025

Effect of Interpretation of Positive Metagenomic Next-Generation Sequencing Reports on the Infection Diagnosis in Patients With Hematological Disorders.

Chunhui Xu, Yuyan Shen, Shulian Chen, Teng Liu, Xin Chen, Yuetian Yu, Li Liu, Runzhi Ma, Lining Zhang, Xin Liu and 3 more

Abstract read
In one paragraph

Article in Open forum infectious diseases, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

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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

Who cites it

4 citing papers in PubMed.

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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

13 authors.

Chunhui XuState Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, China.
Yuyan ShenState Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, China.
Shulian ChenState Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, China.
Teng LiuMicrobiology laboratory, Tianjin Union Precision Medical Diagnostic Co ., Ltd, Tianjin, China.
Xin ChenState Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, China.
Yuetian YuDepartment of Critical Care Medicine, Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Li LiuState Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, China.
Runzhi MaState Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, China.
Lining ZhangState Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, China.
Xin LiuState Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, China.ORCID https://orcid.org/0000-0003-0880-2422
Lukun ZhouDepartment of Hematology, Zhongda Hospital, Southeast University, Nanjing, People's Republic of China.
Guoqing ZhuState Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, China.
Sizhou FengState Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Metagenomic next-generation sequencing (mNGS) has become a crucial diagnostic tool for infectious diseases in patients with hematological disorders. However, despite the abundant microbial information provided by positive mNGS reports, interpreting these results remains challenging due to the lack of standardized criteria. Methods: We surveyed 92 clinicians to identify common challenges in understanding mNGS reports. Microbiologists then provided additional "report interpretation cards" (RICs) for positive mNGS results alongside original reports. The aim of using RICs was to determine whether each detected microorganism was likely cause of infection. After a 3-month period, a panel of clinical experts retrospectively reviewed 281 cases, involving 728 detected microorganisms, to assess RIC accuracy. Results: In total, 82.6% of clinicians (76 of 92) experienced difficulties in interpreting mNGS reports. After receiving RICs, 97.8% of clinicians (90 of 92) reported satisfaction. The overall concordance rates between interpretation and adjudication in the 281 cases was 79.0% (222 of 281). In 203 cases in which multiple microorganisms were detected, 37.9% (77 of 203) and 37.4% (76 of 203) were interpreted and adjudicated as mixed infections. Among the 728 microorganisms, interpretation and adjudication revealed concordance rates of 93.9% (154 of 164), 95.7% (88 of 92), and 72.3% (339 of 469) for bacterial, fungal, and viral infections, respectively. In 68.7% of the cases (193 of 281), mNGS positively influenced pathogen diagnosis. Conclusions: Not all microorganisms detected by mNGS are responsible for infection, and appropriate interpretation is essential. The provision of interpretations by microbiologists aids clinicians in accurately using mNGS for infection diagnosis.

Indexed as

diagnosishematological disordersinfectioninterpretationnext generation sequencing

Identifiers

PMID40041441
PMCPMC11878555

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LicenceCC BY-NC-ND
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Registered trials

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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.