Evidence map›Paper›PMID 36237437›Full record

ArticleFrontiers in cellular and infection microbiology2022

Application of metagenomic next-generation sequencing in the diagnosis of pulmonary invasive fungal disease.

Chengtan Wang, Zhiqing You, Juanjuan Fu, Shuai Chen, Di Bai, Hui Zhao, Pingping Song, Xiuqin Jia, Xiaoju Yuan, Wenbin Xu and 2 more

Abstract readMulticenter Study
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 39 papers.

0numbers the graph read from it
0cells of the map it votes in
39citing 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

39 citing papers in PubMed.

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  18. Exploring drug resistance genes inFrontiers in microbiology · 2025
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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.

Chengtan WangDepartment of Clinical Laboratory, Liaocheng People's Hospital, Liaocheng, China.
Zhiqing YouDepartment of Clinical Laboratory, Liaocheng People's Hospital, Liaocheng, China.
Juanjuan FuDepartment of Clinical Laboratory, Liaocheng People's Hospital, Liaocheng, China.
Shuai ChenDepartment of Clinical Laboratory, Liaocheng Third People's Hospital, Liaocheng, China.
Di BaiDepartment of Clinical Laboratory, Liaocheng Third People's Hospital, Liaocheng, China.
Hui ZhaoDepartment of Clinical Laboratory, Liaocheng People's Hospital, Liaocheng, China.
Pingping SongDepartment of Clinical Laboratory, Liaocheng People's Hospital, Liaocheng, China.
Xiuqin JiaThe Key Laboratory of Molecular Pharmacology, Liaocheng People's Hospital, Liaocheng, China.
Xiaoju YuanDepartment of Gastroenterology, Liaocheng People's Hospital, Liaocheng, China.
Wenbin XuDepartment of Clinical Laboratory, Liaocheng People's Hospital, Liaocheng, China.
Qigang ZhaoDepartment of Clinical Laboratory, Liaocheng People's Hospital, Liaocheng, China.
Feng PangDepartment of Clinical Laboratory, Liaocheng People's Hospital, Liaocheng, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Metagenomic next-generation sequencing (mNGS) is increasingly being used to detect pathogens directly from clinical specimens. However, the optimal application of mNGS and subsequent result interpretation can be challenging. In addition, studies reporting the use of mNGS for the diagnosis of invasive fungal infections (IFIs) are rare. Objective: We critically evaluated the performance of mNGS in the diagnosis of pulmonary IFIs, by conducting a multicenter retrospective analysis. The methodological strengths of mNGS were recognized, and diagnostic cutoffs were determined. Methods: A total of 310 patients with suspected pulmonary IFIs were included in this study. Conventional microbiological tests (CMTs) and mNGS were performed in parallel on the same set of samples. Receiver operating characteristic (ROC) curves were used to evaluate the performance of the logarithm of reads per kilobase per million mapped reads [lg(RPKM)], and read counts were used to predict true-positive pathogens. Result: The majority of the selected patients (86.5%) were immunocompromised. Twenty species of fungi were detected by mNGS, which was more than was achieved with standard culture methods. Peripheral blood lymphocyte and monocyte counts, as well as serum albumin levels, were significantly negatively correlated with fungal infection. In contrast, C-reactive protein and procalcitonin levels showed a significant positive correlation with fungal infection. ROC curves showed that mNGS [and especially lg(RPKM)] was superior to CMTs in its diagnostic performance. The area under the ROC curve value obtained for lg(RPKM) in the bronchoalveolar lavage fluid of patients with suspected pulmonary IFIs, used to predict true-positive pathogens, was 0.967, and the cutoff value calculated from the Youden index was -5.44. Conclusions: In this study, we have evaluated the performance of mNGS-specific indicators that can identify pathogens in patients with IFIs more accurately and rapidly than CMTs, which will have important clinical implications.

Indexed as

Invasive Fungal InfectionsLung Diseases, FungalMycosesPneumoniaC-Reactive ProteinHigh-Throughput Nucleotide SequencingHumansMetagenomicsProcalcitoninRetrospective StudiesSensitivity and SpecificitySerum AlbuminC-Reactive ProteinProcalcitoninSerum Albuminclinical mNGSimmunosuppressed patientsinvasive fungal infectionspathogen detectionpneumonia

Identifiers

PMID36237437
PMCPMC9551268

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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.