Volume 23 Issue 2
Feb.  2025
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Article Navigation >  Chinese Journal of General Practice  > 2025  >  23(2): 211-214
HE Fang, WANG Shu, XIA Wenjuan, HUANG Pengfei, WANG Hua. Construction and validation of a Nomogram model for assessing the risk of multidrug-resistant pulmonary tuberculosis infection in young people[J]. Chinese Journal of General Practice, 2025, 23(2): 211-214. doi: 10.16766/j.cnki.issn.1674-4152.003869
Citation: HE Fang, WANG Shu, XIA Wenjuan, HUANG Pengfei, WANG Hua. Construction and validation of a Nomogram model for assessing the risk of multidrug-resistant pulmonary tuberculosis infection in young people[J]. Chinese Journal of General Practice, 2025, 23(2): 211-214. doi: 10.16766/j.cnki.issn.1674-4152.003869
shu

Construction and validation of a Nomogram model for assessing the risk of multidrug-resistant pulmonary tuberculosis infection in young people

doi: 10.16766/j.cnki.issn.1674-4152.003869
  • 1.

    Anhui Provincial Chest Hospital/ Department of Geriatric Respiratory and Critical Illness, the Chest Clinical Medical College of Anhui Medical University, Hefei, Anhui 230022, China

Funds:

 AHWJ2022b040

  • Received Date: 2024-03-12
    Available Online: 2025-03-27
    Abstract
  •   Objective  This study aims to explore the risk factors for multidrug-resistant infections in young tuberculosis patients and construct a risk Nomogram model for assessing the incidence risk of multidrug-resistant tuberculosis.  Methods  From January 2019 to December 2021, 203 young patients with pulmonary tuberculosis were admitted to Anhui Chest Hospital, and their clinical data were collected. Using a random number table, the patients were divided into a validation group of 67 patients and a modeling group of 136 patients. Among them, there were 75 drug-resistant cases and 61 non-drug-resistant cases. Lasso regression and logistic regression were employed to analyze the risk factors for drug resistance in young pulmonary tuberculosis patients, following which a Nomogram prediction model was constructed and validated.  Results  Logistic regression analysis showed pulmonary cavitary lesions, innutrition, elevated glycosylated hemoglobin, positive sputum smear microscopy, and low income as independent risk factors for multidrug-resistant infection in young pulmonary tuberculosis patients. Using these risk factors, a Nomogram predictive model was constructed, with an area under the curve of 0. 904 for the modeling group and 0. 743 for the validation group, indicating good diagnostic accuracy. Hosmer-Lemeshow test results indicated no significant difference between the observed and model-predicted risks in both the modeling and validation datasets (P = 0. 438, 0. 733), suggesting good model fit and consistency with actual infection risks.  Conclusion  The Nomogram model, established using Lasso logistic regression analysis, exhibits high predictive capability for multidrug-resistant infection in young pulmonary tuberculosis patients. This enables prompt implementation of early intervention measures, ultimately improving prognosis.

     

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