Evidence map›Paper›PMID 38317206›Full record

ArticleRespiratory research2024

Clinical application of bronchoalveolar lavage fluid metagenomics next-generation sequencing in cancer patients with severe pneumonia.

Chao Wang, Xiaojuan Yin, Wenqing Ma, Li Zhao, Xuhong Wu, Nan Ma, Yuepeng Cao, Quanli Zhang, Shuliang Ma, Lin Xu and 1 more

Abstract read
In one paragraph

Article in Respiratory research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing 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

13 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

11 authors.

Chao Wang *Department of Critical Care Medicine, Jiangsu Cancer Hospital, Jiangsu Institute of Cancer Research, Nanjing Medical University Affiliated Cancer Hospital, Nanjing, Jiangsu, 210009, China.
Xiaojuan Yin *Department of Pharmacology, Nanjing Medical University, 101 Longmian Boulevard, Nanjing, Jiangsu, 210029, China.
Wenqing MaDepartment of Pharmacology, Nanjing Medical University, 101 Longmian Boulevard, Nanjing, Jiangsu, 210029, China.
Li ZhaoDepartment of Pharmacology, Nanjing Medical University, 101 Longmian Boulevard, Nanjing, Jiangsu, 210029, China.
Xuhong WuDepartment of Critical Care Medicine, Jiangsu Cancer Hospital, Jiangsu Institute of Cancer Research, Nanjing Medical University Affiliated Cancer Hospital, Nanjing, Jiangsu, 210009, China.
Nan MaDepartment of Critical Care Medicine, Jiangsu Cancer Hospital, Jiangsu Institute of Cancer Research, Nanjing Medical University Affiliated Cancer Hospital, Nanjing, Jiangsu, 210009, China.
Yuepeng CaoDepartment of Critical Care Medicine, Jiangsu Cancer Hospital, Jiangsu Institute of Cancer Research, Nanjing Medical University Affiliated Cancer Hospital, Nanjing, Jiangsu, 210009, China.
Quanli ZhangJiangsu Key Laboratory of Molecular and Translational Cancer Research, 42 Baiziting Road, Xuanwu District, Nanjing, Jiangsu, 210009, China.
Shuliang MaDepartment of Critical Care Medicine, Jiangsu Cancer Hospital, Jiangsu Institute of Cancer Research, Nanjing Medical University Affiliated Cancer Hospital, Nanjing, Jiangsu, 210009, China.
Lin XuDepartment of Thoracic Surgery, Jiangsu Cancer Hospital, Jiangsu Institute of Cancer Research, Nanjing Medical University Affiliated Cancer Hospital, Nanjing, Jiangsu, 210009, China. xulin_83@hotmail.com.
Xuerong WangDepartment of Pharmacology, Nanjing Medical University, 101 Longmian Boulevard, Nanjing, Jiangsu, 210029, China. wangxr@njmu.edu.cn.

Funding

Jiangsu Province Capability Improvement Project through Science, Technology and Education, Jiangsu Provincial Medical Key Laboratory ZDXYS202203National Natural Science Foundation of China 81972763
6 · The paper itself

Abstract

objectiveMetagenomic next-generation sequencing (mNGS), as an emerging technique for pathogen detection, has been widely used in clinic. However, reports on the application of mNGS in cancer patients with severe pneumonia remain limited. This study aims to evaluate the diagnostic performance of bronchoalveolar lavage fluid (BALF) mNGS in cancer patients complicated with severe pneumonia.

methodsA total of 62 cancer patients with severe pneumonia simultaneously received culture and mNGS of BALF were enrolled in this study. We systematically analyzed the diagnostic significance of BALF mNGS. Subsequently, optimization of anti-infective therapy based on the distribution of pathogens obtained from BALF mNGS was also assessed.

resultsFor bacteria and fungi, the positive detection rate of mNGS was significantly higher than culture method (91.94% versus 51.61%, P < 0.001), especially for poly-microbial infections (70.97% versus 12.90%, P < 0.001). Compared with the culture method, mNGS exhibited a diagnostic sensitivity of 100% and a specificity of 16.67%, with the positive predictive value (PPV) and negative predictive value (NPV) being 56.14% and 100%, respectively. The agreement rate between these two methods was 59.68%, whereas kappa consensus analysis indicated a poor concordance (kappa = 0.171). After receipt of BALF mNGS results, anti-infective treatment strategies in 39 out of 62 cases (62.90%) were optimized. Moreover, anti-tumor therapy was a high-risk factor for mixed infections (87.18% versus 65.22%, P = 0.04).

conclusionsThe present study showed that cancer patients with severe pneumonia, especially those received anti-tumor therapy, were more likely to have poly-microbial infections. BALF mNGS can provide a rapid and comprehensive pathogen distribution of pulmonary infection, making it a promising technique in clinical practice, especially for optimizing therapeutic strategies for cancer patients.

Indexed as

CoinfectionNeoplasmsPneumoniaBronchoalveolar Lavage FluidConsensusHigh-Throughput Nucleotide SequencingHumansSensitivity and SpecificityBALFCancer patientsmNGSSevere pneumonia

Identifiers

PMID38317206
PMCPMC10840150

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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.