ORIGINAL RESEARCH article
Front. Public Health
Sec. Infectious Diseases: Epidemiology and Prevention
This article is part of the Research TopicAdvancements in Diversity and Drug Resistance Mechanisms in Mycobacterial DiseasesView all 18 articles
Analysis of Influencing Factors and Construction of Prediction Model for Multidrug-Resistant Tuberculosis in Nanning Area
Provisionally accepted
- 1Guangxi (Nanning) and The Fourth People’s Hospital, Guangxi, China
- 2Institute of Data Science, City University of Macau, Macao, Shanghai Municipality, China
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Objective: This study aims to analyze the characteristics of multidrug-resistant Mycobacterium tuberculosis isolates and to identify the factors influencing multidrug resistance in the Nanning area. Methods: This study retrospectively analyzed all sputum specimens from pulmonary tuberculosis patients collected at the Fourth People's Hospital of Nanning from January 2021 to June 2022, including a total of 337 strains of Mycobacterium tuberculosis.Univariate analysis and binary Logistics regression analysis were used to identify factors influencing multidrug resistance. A predictive model was constructed with SPSS software, and the predictive value of the model was evaluated with the Receiver Operating Characteristic (ROC) curve. Results: The results of binary Logistics regression analysis indicated that treatment status and high-risk population were independent factors influencing multidrug resistance (P < 0.05). According to the Logistics regression analysis results, the model was constructed as follows: Logit(P) = -1.874 + (1.187X1) + (0.837X2). ROC analysis showed that the area under the curve (AUC) of the model was 0.936. In the validation group, the AUC was 0.853. Conclusion: This study results provide a basis for precise prevention and control of multidrug-resistant tuberculosis bacteria in Nanning, help reduce the risk of transmission, and ensure public health safety of local and surrounding populations.
Keywords: Mycobacterium, Tuberculosis, Drug Resistance, receiver operating characteristic, predictive model
Received: 30 Apr 2025; Accepted: 20 Nov 2025.
Copyright: © 2025 Huang, Zhu, Xie, Lu, Lu, Chen, Yu and Hu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Hailing Yu, hailingyuvip@163.com
Yanling Hu, ylhupost@163.com
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