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ORIGINAL RESEARCH article
Front. Med.
Sec. Pulmonary Medicine
Volume 11 - 2024 |
doi: 10.3389/fmed.2024.1457048
This article is part of the Research Topic Immune and Vascular Regulation of Lung Injury, Repair, and Regeneration View all articles
Analysis of Factors Influencing Bronchiectasis Patients with Active Pulmonary Tuberculosis and Development of a Nomogram Prediction Model
Provisionally accepted
Yitian Yang
Lianfang Du
Weilong Ye
Weifeng Liao
Zhenzhen Zheng
Xiaoxi Lin
Feiju Chen
Jingjing Pan
Bainian Chen
Riken Chen
*
Weimin Yao
*- Second Affiliated Hospital of Guangdong Medical University, Zhanjiang, China
Background: To identify the risk factors for bronchiectasis patients with active pulmonary tuberculosis (APTB) and to develop a predictive nomogram model for estimating the risk of APTB in bronchiectasis patients. Methods: A retrospective cohort study was conducted on 16,750 bronchiectasis patients hospitalized at the Affiliated Hospital of Guangdong Medical University and the Second Affiliated Hospital of Guangdong Medical University between January 2019 and December 2023. The 390 patients with APTB were classified as the case group, while 818 patients were randomly sampled by computer at a 1:20 ratio from the 16,360 patients with other infections to serve as the control group. Relevant indicators potentially leading to APTB in bronchiectasis patients were collected. Patients were categorized into APTB and inactive pulmonary tuberculosis (IPTB) groups based on the presence of tuberculosis. The general characteristics of both groups were compared. Variables were screened using the least absolute shrinkage and selection operator (LASSO) analysis, followed by multivariate logistic regression analysis. A nomogram model was established based on the analysis results. The model's predictive performance was evaluated using calibration curves, C-index, and ROC curves, and internal validation was performed using the bootstrap method. Results: LASSO analysis identified 28 potential risk factors. Multivariate analysis showed that age, gender, TC, ALB, MCV, FIB, PDW, LYM, hemoptysis, and hypertension are independent risk factors for bronchiectasis patients with APTB (P<0.05). The nomogram demonstrated strong calibration and discrimination, with a C-index of 0.745 (95% CI: 0.715-0.775) and an AUC of 0.744 for the ROC curve. Internal validation using the bootstrap method produced a C-index of 0.738, further confirming the model's robustness. Conclusion: The nomogram model, developed using common clinical serological characteristics, holds significant clinical value for assessing the risk of APTB in bronchiectasis patients.
Keywords: Active pulmonary tuberculosis, Bronchiectasis, nomogram, Prediction model, Infection
Received: 30 Jun 2024; Accepted: 28 Oct 2024.
Copyright: © 2024 Yang, Du, Ye, Liao, Zheng, Lin, Chen, Pan, Chen, Chen and Yao. 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:
Riken Chen, Second Affiliated Hospital of Guangdong Medical University, Zhanjiang, China
Weimin Yao, Second Affiliated Hospital of Guangdong Medical University, Zhanjiang, China
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