Evidence map›Paper›PMID 36899314›Full record

ArticleBMC infectious diseases2023

A cross-sectional study: a breathomics based pulmonary tuberculosis detection method.

Liang Fu, Lei Wang, Haibo Wang, Min Yang, Qianting Yang, Yi Lin, Shanyi Guan, Yongcong Deng, Lei Liu, Qingyun Li and 4 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC infectious diseases, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.

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

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

  1. 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

14 authors.

Liang Fu *Division Two of the Pulmonary Diseases Department, The Third People's Hospital of Shenzhen, National Clinical Research Center for Infectious Disease, Southern University of Science and Technology, Shenzhen, 518112, China.
Lei Wang *Breax Laboratory, PCAB Research Center of Breath and Metabolism, Beijing, 100074, China.
Haibo Wang *Peking University Clinical Research Institute, Peking University First Hospital, Beijing, 100000, China.
Min YangDivision Two of the Pulmonary Diseases Department, The Third People's Hospital of Shenzhen, National Clinical Research Center for Infectious Disease, Southern University of Science and Technology, Shenzhen, 518112, China.
Qianting YangInstitute for Hepatology, The Third People's Hospital of Shenzhen, National Clinical Research Center for Infectious Disease, Southern University of Science and Technology, Shenzhen, 518112, China.
Yi LinDivision Two of the Pulmonary Diseases Department, The Third People's Hospital of Shenzhen, National Clinical Research Center for Infectious Disease, Southern University of Science and Technology, Shenzhen, 518112, China.
Shanyi GuanMedical Examination Department, The Third People's Hospital of Shenzhen, National Clinical Research Center for Infectious Disease, Southern University of Science and Technology, Shenzhen, 518112, China.
Yongcong DengPulmonary Diseases Out-Patient Department, The Third People's Hospital of Shenzhen, National Clinical Research Center for Infectious Disease, Southern University of Science and Technology, Shenzhen, 518112, China.
Lei LiuDivision Two of the Pulmonary Diseases Department, The Third People's Hospital of Shenzhen, National Clinical Research Center for Infectious Disease, Southern University of Science and Technology, Shenzhen, 518112, China.
Qingyun LiBreax Laboratory, PCAB Research Center of Breath and Metabolism, Beijing, 100074, China.
Mengqi HeBreax Laboratory, PCAB Research Center of Breath and Metabolism, Beijing, 100074, China.
Peize ZhangDivision Two of the Pulmonary Diseases Department, The Third People's Hospital of Shenzhen, National Clinical Research Center for Infectious Disease, Southern University of Science and Technology, Shenzhen, 518112, China. 82880246@qq.com.
Haibin ChenBreax Laboratory, PCAB Research Center of Breath and Metabolism, Beijing, 100074, China. haibinc@hotmail.com.ORCID http://orcid.org/0000-0002-5542-683X
Guofang DengDivision Two of the Pulmonary Diseases Department, The Third People's Hospital of Shenzhen, National Clinical Research Center for Infectious Disease, Southern University of Science and Technology, Shenzhen, 518112, China. jxxk1035@yeah.net.

Funding

Basic and Applied Basic Research Foundation of Guangdong Province 2019B1515120041Guangdong Provincial Key Laboratory of Prevention and Control for Severe Clinical Animal Diseases 2020B1111170014Key Technologies Research and Development Program 2019YFC0840602Key Technologies Research and Development Program 2020YFA0907200National Natural Science Foundation of China 82070016Shenzhen Natural Science Foundation JCYJ20220530163212027Shenzhen Scientific and Technological Foundation JCYJ20180228162112889Shenzhen Scientific and Technological Foundation KCXFZ202002011007083Shenzhen Third People's Hospital G2022051Summit Plan for Foshan High-level Hospital Construction FSSYKF-2020001the Shenzhen Clinical Research Center for Tuberculosis 20210617141509001The Shenzhen Fund for Guangdong Provincial High-level Clinical Key Specialties SZGSP010the Special fund of Shenzhen Central-leading-local Scientific and Technological Foundation LCYX20220620105200001
6 · The paper itself

Abstract

backgroundDiagnostics for pulmonary tuberculosis (PTB) are usually inaccurate, expensive, or complicated. The breathomics-based method may be an attractive option for fast and noninvasive PTB detection.

methodExhaled breath samples were collected from 518 PTB patients and 887 controls and tested on the real-time high-pressure photon ionization time-of-flight mass spectrometer. Machine learning algorithms were employed for breathomics analysis and PTB detection mode, whose performance was evaluated in 430 blinded clinical patients.

resultsThe breathomics-based PTB detection model achieved an accuracy of 92.6%, a sensitivity of 91.7%, a specificity of 93.0%, and an AUC of 0.975 in the blinded test set (n = 430). Age, sex, and anti-tuberculosis treatment does not significantly impact PTB detection performance. In distinguishing PTB from other pulmonary diseases (n = 182), the VOC modes also achieve good performance with an accuracy of 91.2%, a sensitivity of 91.7%, a specificity of 88.0%, and an AUC of 0.961.

conclusionsThe simple and noninvasive breathomics-based PTB detection method was demonstrated with high sensitivity and specificity, potentially valuable for clinical PTB screening and diagnosis.

Indexed as

Lung DiseasesTuberculosis, PulmonaryAlgorithmsCross-Sectional StudiesHumansMachine LearningBreathomicsMachine learningPulmonary tuberculosisVolatile organic compounds

Identifiers

PMID36899314
PMCPMC9999612

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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.