Evidence map›Paper›PMID 40717117›Full record

ArticleScientific reports2025

Comparative diagnostic performance of metagenomic and two targeted sequencing methods in lower respiratory infection.

Lang Yi, Li Tan, Qichen Long, Xing Lyu, Huihui Zeng, Yating Peng, Dejian Gu, Hao Liu, Hu Ge, Yan Yu and 2 more

Abstract readComparative Study
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
7citing 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.

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

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

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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

12 authors.

Lang YiDepartment of Clinical Laboratory, Xiangya Hospital, Central South University, Changsha, China.
Li TanCenter for Clinical Molecular Diagnostics, The Second Xiangya Hospital of Central South University, Changsha, China.
Qichen LongCenter for Clinical Molecular Diagnostics, The Second Xiangya Hospital of Central South University, Changsha, China.
Xing LyuCenter for Clinical Molecular Diagnostics, The Second Xiangya Hospital of Central South University, Changsha, China.
Huihui ZengDepartment of Respiratory and Critical Care Medicine, The Second Xiangya Hospital of Central South University, Changsha, China.
Yating PengDepartment of Respiratory and Critical Care Medicine, The Second Xiangya Hospital of Central South University, Changsha, China.
Dejian GuDepartment of Medicine, GenePlus-Beijing, Beijing, China.
Hao LiuDepartment of Medicine, GenePlus-Beijing, Beijing, China.
Hu GeChangsha KingMed Diagnostics Group Co., Ltd., Changsha, China.
Yan YuChangsha KingMed Diagnostics Group Co., Ltd., Changsha, China.
Ziyang LiCenter for Clinical Molecular Diagnostics, The Second Xiangya Hospital of Central South University, Changsha, China. zylicsu@csu.edu.cn.
Min HuCenter for Clinical Molecular Diagnostics, The Second Xiangya Hospital of Central South University, Changsha, China. huminjyk@csu.edu.cn.

Funding

Hunan Natural Science Foundation No. 2025JJ60514National Natural Science Foundation of China No. 82102499Scientific Research Project for Hunan Health Commission No.202211003513
6 · The paper itself

Abstract

Lower respiratory tract infections are characterized by high morbidity and mortality, the latter associated with the low sensitivity and long turnaround time (TAT) of traditional diagnostic methods. Advances in next-generation sequencing (NGS) offers a promising solution, but in the face of so many different NGS products, how to use them appropriately remains a great challenge for clinicians. This study included 205 patients with suspected lower respiratory tract infections from the department of respiratory and critical care medicine, and collected their lower respiratory tract samples for metagenomic NGS (mNGS) and two different targeted NGS (tNGS), amplification-based tNGS and capture-based tNGS. We analyzed their microorganisms reported, and evaluated their detection performance based on the comprehensive clinical diagnosis. Compared to the two tNGS, mNGS showed significant higher cost ($840) and longer TAT (20 h). Conversely, it identified the highest number of species, totaling 80, compared to 71 species identified by capture-based tNGS and 65 species by amplification-based tNGS. When benchmarked against the comprehensive clinical diagnosis, the capture-based tNGS demonstrated significantly higher diagnostic performance than the other two NGS, with an accuracy of 93.17% and a sensitivity of 99.43%. However, it showed lower specificity compared to the amplification-based tNGS in identifying DNA virus (74.78% vs. 98.25%). The amplification-based tNGS exhibited a poor sensitivity for both gram-positive (40.23%) and gram-negative bacteria (71.74%). Moreover, tNGS was able to identify genotypes, antimicrobial resistance genes and virulence factors. In conclusion, mNGS is suited for the detection of rare pathogens; the capture-based tNGS is preferable for routine diagnostic testing; the amplification-based tNGS can be an alternative in situations requiring rapid results and constrained by limited resources.

Indexed as

High-Throughput Nucleotide SequencingMetagenomicsRespiratory Tract InfectionsAdultAgedFemaleHumansMaleMiddle AgedSensitivity and SpecificityAmplification-based targeted next-generation sequencingCapture-based targeted next-generation sequencingClinical comparative evaluationDiagnosisLower respiratory tract infectionMetagenomic next-generation sequencingPathogen

Identifiers

PMID40717117
PMCPMC12301447

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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.