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Chinese Journal of Antituberculosis ›› 2025, Vol. 47 ›› Issue (8): 1038-1043.doi: 10.19982/j.issn.1000-6621.20250064

• Original Articles • Previous Articles     Next Articles

Construction and analysis of early warning and prediction model for risk factors of initially treated severe pulmonary tuberculosis

Xue Yu1, Guo Shubin2, Lei Xuan1, Zhang Jing1, Li Wensheng1, Liu Yan1, Li Huan1, Liu Zhifeng1, Wang Wei3(), Wen Li1()   

  1. 1Department of Emergency, Beijing Chest Hospital, Capital Medical University, Beijing 101149, China
    2Department of Emergency, Beijing Chao-Yang Hospital, Capital Medical University, Beijing 100020, China
    3National Clinical Laboratory on Tuberculosis, Beijing Chest Hospital, Capital Medical University, Beijing 101149, China
  • Received:2025-02-24 Online:2025-08-10 Published:2025-08-01
  • Contact: Wen Li, Email: drli1025@163.com; Wang Wei, Email: wangwei010@aliyun.com

Abstract:

Objective: To investigate the epidemiological characteristics of initially treated severe pulmonary tuberculosis, analyze its risk factors and establish a prediction model, which provides evidence for early clinical identification of initially treated severe pulmonary tuberculosis and improvement of clinical outcomes. Methods: A total of 217 patients with initially treated pulmonary tuberculosis admitted to Beijing Chest Hospital, Capital Medical University, between January 2024 to June 2024 were divided into two groups according to the diagnostic criteria for severe pulmonary tuberculosis: the initially treated severe group (107 patients) and the initially treated non-severe group (110 patients). The information including baseline data, basic diseases, laboratory tests and bacteriological evidence was retrospectively collected. The differences in clinical data between two groups were compared. Multivariate logistic regression analysis was performed to analyze the risk factors for severe pulmonary tuberculosis before treatment initiation, establish the risk prediction model and plot a receiver operator characteristic (ROC) curve to evaluate the predictive value of this model for initially treated severe tuberculosis patients. Results: Multivariate logistic regression analysis showed that heart rate, albumin, neutrophile-lymphocyte ratio (NLR), sodium, respiratory distress were independent risk factors for the initial treated severe pulmonary tuberculosis. An increased heart rate was associated with an increased risk of severe pulmonary tuberculosis (OR=1.205, 95%CI: 1.010-1.436). An increase in the ratio of NLR significantly increased the risk of disease (OR=2.247, 95%CI: 1.133-4.455). Respiratory distress was the strongest risk factor, with an OR as high as 26.899 (95%CI: 1.713-289.780). Higher albumin levels were associated with a reduced risk of severe disease (OR=0.487, 95%CI: 0.270-0.876), as were higher blood sodium levels (OR=0.489, 95%CI: 0.257-0.928). ROC curve analysis showed that the area under the curve was 0.995 when the five risk factors were combined, and the sensitivity and specificity of predicting the initial severe pulmonary tuberculosis were 96.2% and 98.2%, respectively. Conclusion: Heart rate, respiratory distress, albumin, NLR, sodium were independent risk factors for the initial treatment severe pulmonary tuberculosis. Moreover, the combination of these five factors can effectively predict the occur of severe pulmonary tuberculosis.

Key words: Critical illness, Tuberculosis, pulmonary, Factor analysis, statistical, Models, structural

CLC Number: