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

Xu,Dechang; Zeng,Jiang; Xie,Fangfang; Yang,Qianting; Huang,Kaisong; Xiao,Wei; Zou,Houwen; Zhang,Huihua

doi:10.3892/br.2023.1616

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

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
Published online on: March 28, 2023 https://doi.org/10.3892/br.2023.1616
Article Number: 34
Copyright: © Xu et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

Metrics: Total Views: 0 (Spandidos Publications: | PMC Statistics: )

Metrics: Total PDF Downloads: 0 (Spandidos Publications: | PMC Statistics: )

Cited By (CrossRef): 0 citations Loading Articles...

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.

View Figures

View References

1	WHO: Global Tuberculosis Report 2020. World Health Organization WHO, Geneva, 2020.
2	Kohli M, Schiller I, Dendukuri N, Yao M, Dheda K, Denkinger CM, Schumacher SG and Steingart KR: Xpert MTB/RIF Ultra and Xpert MTB/RIF assays for extrapulmonary tuberculosis and rifampicin resistance in adults. Cochrane Database Syst Rev. 1(CD012768)2021.PubMed/NCBI View Article : Google Scholar
3	Trébucq A, Enarson DA, Chiang CY, Van Deun A, Harries AD, Boillot F, Detjen A, Fujiwara PI, Graham SM, Monedero I, et al: Xpert® MTB/RIF for national tuberculosis programmes in low-income countries: When, where and how? Int J Tuberc Lung Dis. 15:1567–1572. 2011.PubMed/NCBI View Article : Google Scholar
4	Lim WS: From latent to active TB: Are IGRAs of any use? Thorax. 71:585–586. 2016.PubMed/NCBI View Article : Google Scholar
5	Auguste P, Tsertsvadze A, Pink J, Court R, McCarthy N, Sutcliffe P and Clarke A: Comparing interferon-gamma release assays with tuberculin skin test for identifying latent tuberculosis infection that progresses to active tuberculosis: Systematic review and meta-analysis. BMC Infect Dis. 17(200)2017.PubMed/NCBI View Article : Google Scholar
6	Dheda K, Makambwa E and Esmail A: The great masquerader: Tuberculosis presenting as community-acquired pneumonia. Semin Respir Crit Care Med. 41:592–604. 2020.PubMed/NCBI View Article : Google Scholar
7	Vessière A, Font H, Gabillard D, Adonis-Koffi L, Borand L, Chabala C, Khosa C, Mavale S, Moh R, Mulenga V, et al: Impact of systematic early tuberculosis detection using Xpert MTB/RIF Ultra in children with severe pneumonia in high tuberculosis burden countries (TB-Speed pneumonia): A stepped wedge cluster randomized trial. BMC Pediatr. 21(136)2021.PubMed/NCBI View Article : Google Scholar
8	Grossman RF, Hsueh PR, Gillespie SH and Blasi F: Community-acquired pneumonia and tuberculosis: Differential diagnosis and the use of fluoroquinolones. Int J Infect Dis. 18:14–21. 2014.PubMed/NCBI View Article : Google Scholar
9	Williams DJ, Creech CB, Walter EB, Martin JM, Gerber JS, Newland JG, Howard L, Hofto ME, Staat MA, Oler RE, et al: Short-vs Standard-course outpatient antibiotic therapy for community-acquired pneumonia in children: The SCOUT-CAP randomized clinical trial. JAMA Pediatr. 176:253–261. 2022.PubMed/NCBI View Article : Google Scholar
10	Hopstaken RM, Muris JW, Knottnerus JA, Kester AD, Rinkens PE and Dinant GJ: Contributions of symptoms, signs, erythrocyte sedimentation rate, and C-reactive protein to a diagnosis of pneumonia in acute lower respiratory tract infection. Br J Gen Pract. 53:358–364. 2003.PubMed/NCBI
11	Peters JS, McIvor A, Papadopoulos AO, Masangana T, Gordhan BG, Waja Z, Otwombe K, Letutu M, Kamariza M, Sterling TR, et al: Differentially culturable tubercle bacteria as a measure of tuberculosis treatment response. Front Cell Infect Microbiol. 12(1064148)2023.PubMed/NCBI View Article : Google Scholar
12	Blauenfeldt T, Villar-Hernández R, García-García E, Latorre I, Holm LL, Muriel-Moreno B, De Souza-Galvão ML, Millet JP, Sabriá F, Sánchez-Montalva A, et al: Diagnostic accuracy of interferon gamma-induced protein 10 mRNA release assay for tuberculosis. J Clin Microbiol. 58:e00848–20. 2020.PubMed/NCBI View Article : Google Scholar
13	Qiu X, Wang H, Tang Y, Su X, Ge L, Qu Y and Mu D: Is interleukin-2 an optimal marker for diagnosing tuberculosis infection? A systematic review and meta-analysis. Ann Med. 52:376–385. 2020.PubMed/NCBI View Article : Google Scholar
14	Ahmad R, Xie L, Pyle M, Suarez MF, Broger T, Steinberg D, Ame SM, Lucero MG, Szucs MJ, MacMullan M, et al: A rapid triage test for active pulmonary tuberculosis in adult patients with persistent cough. Sci Transl Med. 11(eaaz9925)2019.PubMed/NCBI View Article : Google Scholar
15	Yoon C, Semitala FC, Atuhumuza E, Katende J, Mwebe S, Asege L, Armstrong DT, Andama AO, Dowdy DW, Davis JL, et al: Point-of-care C-reactive protein-based tuberculosis screening for people living with HIV: A diagnostic accuracy study. Lancet Infect Dis. 17:1285–1292. 2017.PubMed/NCBI View Article : Google Scholar
16	Smyth GK: Linear models and empirical bayes methods for assessing differential expression in microarray experiments. Stat Appl Genet Mol Biol. 3(Article3)2004.PubMed/NCBI View Article : Google Scholar
17	Smyth GK: limma: Linear Models for Microarray Data. In: Bioinformatics and Computational Biology Solutions Using R and Bioconductor. Gentleman R., Carey V.J., Huber W., Irizarry R.A., Dudoit S (eds). Springer, New York, NY, pp 397-420 (, 2005).
18	Lim WK and Lim AW: A Comparison Of Usual t-Test Statistic and Modified t-Test Statistics on Skewed Distribution Functions. J modern applied statistical methods: JMASM. 15:67–89. 2016.
19	Capanu M and Seshan VE: False discovery rates for rare variants from sequenced data. Genet Epidemiol. 39:65–76. 2015.PubMed/NCBI View Article : Google Scholar
20	Tang LQ, Li CF, Li J, Chen WH, Chen QY, Yuan LX, Lai XP, He Y, Xu YX, Hu DP, et al: Establishment and validation of prognostic nomograms for endemic nasopharyngeal carcinoma. J Natl Cancer Inst. 108(djv291)2015.PubMed/NCBI View Article : Google Scholar
21	Jang JY, Park T, Lee S, Kim Y, Lee SY, Kim SW, Kim SC, Song KB, Yamamoto M, Hatori T, et al: Proposed nomogram predicting the individual risk of malignancy in the patients with branch duct type intraductal papillary mucinous neoplasms of the pancreas. Ann Surg. 266:1062–1068. 2017.PubMed/NCBI View Article : Google Scholar
22	Friedman J, Hastie T and Tibshirani R: Regularization paths for generalized linear models via coordinate descent. J Stat Softw. 33:1–22. 2010.PubMed/NCBI
23	Jalali A, Alvarez-Iglesias A, Roshan D and Newell J: Visualising statistical models using dynamic nomograms. PLoS One. 14(e0225253)2019.PubMed/NCBI View Article : Google Scholar
24	Balachandran VP, Gonen M, Smith JJ and DeMatteo RP: Nomograms in oncology: More than meets the eye. Lancet Oncol. 16:e173–e180. 2015.PubMed/NCBI View Article : Google Scholar
25	Gil-Santana L, Cruz LAB, Arriaga MB, Miranda PFC, Fukutani KF, Silveira-Mattos PS, Silva EC, Oliveira MG, Mesquita EDD, Rauwerdink A, et al: Tuberculosis-associated anemia is linked to a distinct inflammatory profile that persists after initiation of antitubercular therapy. Sci Rep. 9(1381)2019.PubMed/NCBI View Article : Google Scholar
26	Аbdullaev RY, Komissarova OG, Chumakova ES and Odinet VS: Level of uric acid in blood serum of new pulmonary tuberculosis patients with multiple drug resistance. Tuberculosis and Lung Diseases. 95:31–36. 2017.
27	Bisaso KR, Owen JS, Ojara FW, Namuwenge PM, Mugisha A, Mbuagbaw L, Luboobi LS and Mukonzo JK: Characterizing plasma albumin concentration changes in TB/HIV patients on anti retroviral and anti-tuberculosis therapy. In Silico Pharmacol. 2(3)2014.PubMed/NCBI View Article : Google Scholar
28	Rohini K, Surekha Bhat M, Srikumar PS and Mahesh Kumar A: Assessment of hematological parameters in pulmonary tuberculosis patients. Indian J Clin Biochem. 31:332–335. 2016.PubMed/NCBI View Article : Google Scholar
29	Carole C, Kokhreidze E, Tukvadze N, Banu S, Uddin MKM, Biswas S, Russomando G, Acosta CCD, Arenas R, Ranaivomanana PP, et al: Association of baseline white blood cell counts with tuberculosis treatment outcome: A prospective multicentered cohort study. Int J Infect Dis. 100:199–206. 2020.PubMed/NCBI View Article : Google Scholar
30	Yu YH, Liao CC, Hsu WH, Chen HJ, Liao WC, Muo CH, Sung FC and Chen CY: Increased lung cancer risk among patients with pulmonary tuberculosis: A population cohort study. J Thorac Oncol. 6:32–37. 2011.PubMed/NCBI View Article : Google Scholar
31	Wu CY, Hu HY, Pu CY, Huang N, Shen HC, Li CP and Chou YJ: Pulmonary tuberculosis increases the risk of lung cancer: A population-based cohort study. Cancer. 117:618–624. 2011.PubMed/NCBI View Article : Google Scholar
32	Brenner DR, McLaughlin JR and Hung RJ: Previous lung diseases and lung cancer risk: A systematic review and meta-analysis. PLoS One. 6(e17479)2011.PubMed/NCBI View Article : Google Scholar