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Construction of a risk model and deep learning network based on patients with active pulmonary tuberculosis and pulmonary inflammation

  • Authors:
    • Dechang Xu
    • Jiang Zeng
    • Fangfang Xie
    • Qianting Yang
    • Kaisong Huang
    • Wei Xiao
    • Houwen Zou
    • Huihua Zhang
  • View Affiliations / Copyright

    Affiliations: Internal Medicine Center of Ganzhou Cancer Hospital, Ganzhou, Jiangxi 341000, P.R. China, Ganzhou Fifth People's Hospital, The Ninth Affiliated Clinical College, Gannan Medical University, Ganzhou, Jiangxi 341000, P.R. China, Department of Endocrinology, Ganzhou People's Hospital, Ganzhou, Jiangxi 341000, P.R. China, Shenzhen Institute of Liver Diseases, Third People's Hospital, Shenzhen, Guangdong 518112, P.R. China, Guangdong Provincial Key Laboratory of Regional Immunity and Disease, Department of Pathogen Biology, School of Medicine, Shenzhen University, Shenzhen, Guangdong 518052, P.R. China, Guangdong Provincial Key Laboratory of Regional Immunity and Disease, Department of Pathogen Biology, School of Medicine, Shenzhen University, Shenzhen, Guangdong 518052, P.R. China
    Copyright: © Xu et al. This is an open access article distributed under the terms of Creative Commons Attribution License.
  • Article Number: 34
    |
    Published online on: March 28, 2023
       https://doi.org/10.3892/br.2023.1616
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Abstract

Abstract. Most patients with active pulmonary tuberculosis (TB) are difficult to be differentiated from pneumonia (PN), especially those with acid‑fast bacillus smear‑negative (AFB‑) and interferon‑γ release assay‑positive (IGRA+) results. Thus, the aim of the present study was to develop a risk model of low‑cost and rapid test for the diagnosis of AFB‑ IGRA+ TB from PN. A total of 41 laboratory variables of 204 AFB‑ IGRA+ TB and 156 PN participants were retrospectively analyzed. Candidate variables were identified by t‑statistic test and univariate logistic model. The logistic regression analysis was used to construct the multivariate risk model and nomogram with internal and external validation. A total of 13 statistically differential variables were compared between AFB‑ IGRA+ TB and PN by false discovery rate (FDR) and odds ratio (OR). By integrating five variables, including age, uric acid (UA), albumin (ALB), hemoglobin (Hb) and white blood cell counts (WBC), a multivariate risk model with a concordance index (C‑index) of 0.7 (95% CI: 0.61, 0.8) was constructed. The nomogram showed that UA and Hb acted as protective factors with an OR <1, while age, WBC and ALB were risk factors for TB occurrence. Internal and external validation revealed that nomogram prediction was consistent with the actual observations. Collectively, it was revealed that an integration of five biomarkers (age, UA, ALB, Hb and WBC) may be used to quickly predict TB in AFB‑ IGRA+ clinical samples from PN.
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Copy and paste a formatted citation
Spandidos Publications style
Xu D, Zeng J, Xie F, Yang Q, Huang K, Xiao W, Zou H and Zhang H: Construction of a risk model and deep learning network based on patients with active pulmonary tuberculosis and pulmonary inflammation. Biomed Rep 18: 34, 2023.
APA
Xu, D., Zeng, J., Xie, F., Yang, Q., Huang, K., Xiao, W. ... Zhang, H. (2023). Construction of a risk model and deep learning network based on patients with active pulmonary tuberculosis and pulmonary inflammation. Biomedical Reports, 18, 34. https://doi.org/10.3892/br.2023.1616
MLA
Xu, D., Zeng, J., Xie, F., Yang, Q., Huang, K., Xiao, W., Zou, H., Zhang, H."Construction of a risk model and deep learning network based on patients with active pulmonary tuberculosis and pulmonary inflammation". Biomedical Reports 18.5 (2023): 34.
Chicago
Xu, D., Zeng, J., Xie, F., Yang, Q., Huang, K., Xiao, W., Zou, H., Zhang, H."Construction of a risk model and deep learning network based on patients with active pulmonary tuberculosis and pulmonary inflammation". Biomedical Reports 18, no. 5 (2023): 34. https://doi.org/10.3892/br.2023.1616
Copy and paste a formatted citation
x
Spandidos Publications style
Xu D, Zeng J, Xie F, Yang Q, Huang K, Xiao W, Zou H and Zhang H: Construction of a risk model and deep learning network based on patients with active pulmonary tuberculosis and pulmonary inflammation. Biomed Rep 18: 34, 2023.
APA
Xu, D., Zeng, J., Xie, F., Yang, Q., Huang, K., Xiao, W. ... Zhang, H. (2023). Construction of a risk model and deep learning network based on patients with active pulmonary tuberculosis and pulmonary inflammation. Biomedical Reports, 18, 34. https://doi.org/10.3892/br.2023.1616
MLA
Xu, D., Zeng, J., Xie, F., Yang, Q., Huang, K., Xiao, W., Zou, H., Zhang, H."Construction of a risk model and deep learning network based on patients with active pulmonary tuberculosis and pulmonary inflammation". Biomedical Reports 18.5 (2023): 34.
Chicago
Xu, D., Zeng, J., Xie, F., Yang, Q., Huang, K., Xiao, W., Zou, H., Zhang, H."Construction of a risk model and deep learning network based on patients with active pulmonary tuberculosis and pulmonary inflammation". Biomedical Reports 18, no. 5 (2023): 34. https://doi.org/10.3892/br.2023.1616
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