TY - JOUR AB - 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. AD - 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 AU - Xu,Dechang AU - Zeng,Jiang AU - Xie,Fangfang AU - Yang,Qianting AU - Huang,Kaisong AU - Xiao,Wei AU - Zou,Houwen AU - Zhang,Huihua DA - 2023/05/01 DO - 10.3892/br.2023.1616 IS - 5 JO - Biomed Rep KW - pulmonary tuberculosis pneumonia acid‑fast bacilli staining interferon‑gamma release assays multivariate risk model PY - 2023 SN - 2049-9434 2049-9442 SP - 34 ST - Construction of a risk model and deep learning network based on patients with active pulmonary tuberculosis and pulmonary inflammation T2 - Biomedical Reports TI - Construction of a risk model and deep learning network based on patients with active pulmonary tuberculosis and pulmonary inflammation UR - https://doi.org/10.3892/br.2023.1616 VL - 18 ER -