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 -