Analysis of factors influencing bronchiectasis patients with active pulmonary tuberculosis and development of a nomogram prediction model.

IF 3.1 3区医学 Q1 MEDICINE, GENERAL & INTERNAL

Frontiers in Medicine Pub Date : 2024-11-08 eCollection Date: 2024-01-01 DOI:10.3389/fmed.2024.1457048

Yitian Yang, Lianfang Du, Weilong Ye, Weifeng Liao, Zhenzhen Zheng, Xiaoxi Lin, Feiju Chen, Jingjing Pan, Bainian Chen, Riken Chen, Weimin Yao

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引用次数: 0

Abstract

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.

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活动性肺结核支气管扩张症患者的影响因素分析及提名图预测模型的开发。

背景：确定支气管扩张症患者患活动性肺结核（APTB）的风险因素，并开发用于估计支气管扩张症患者APTB风险的预测提名图模型：目的：确定支气管扩张症患者活动性肺结核（APTB）的风险因素，并建立一个预测提名图模型来估计支气管扩张症患者APTB的风险：方法：对2019年1月至2023年12月期间在广东医科大学附属医院和广东医科大学附属第二医院住院治疗的16750名支气管扩张症患者进行回顾性队列研究。其中，390名APTB患者被列为病例组，818名患者则由计算机按1:20的比例从16360名其他感染患者中随机抽样，作为对照组。收集支气管扩张患者可能导致 APTB 的相关指标。根据肺结核的存在情况，将患者分为非活动性肺结核（APTB）组和非活动性肺结核（IPTB）组。比较两组患者的一般特征。使用最小绝对缩小和选择算子（LASSO）分析筛选变量，然后进行多变量逻辑回归分析。根据分析结果建立了一个提名图模型。使用校准曲线、C-指数和ROC曲线对模型的预测性能进行了评估，并使用引导法进行了内部验证：结果：LASSO 分析确定了 28 个潜在风险因素。多变量分析表明，年龄、性别、TC、ALB、MCV、FIB、PDW、LYM、咯血和高血压是支气管扩张伴 APTB 患者的独立危险因素（p 结论：LASSO 分析确定了 28 个潜在的危险因素：利用常见临床血清学特征建立的提名图模型对于评估支气管扩张症患者罹患 APTB 的风险具有重要的临床价值。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Frontiers in Medicine Medicine-General Medicine

CiteScore

5.10

自引率

5.10%

发文量

3710

审稿时长

12 weeks

期刊介绍： Frontiers in Medicine publishes rigorously peer-reviewed research linking basic research to clinical practice and patient care, as well as translating scientific advances into new therapies and diagnostic tools. Led by an outstanding Editorial Board of international experts, this multidisciplinary open-access journal is at the forefront of disseminating and communicating scientific knowledge and impactful discoveries to researchers, academics, clinicians and the public worldwide. In addition to papers that provide a link between basic research and clinical practice, a particular emphasis is given to studies that are directly relevant to patient care. In this spirit, the journal publishes the latest research results and medical knowledge that facilitate the translation of scientific advances into new therapies or diagnostic tools. The full listing of the Specialty Sections represented by Frontiers in Medicine is as listed below. As well as the established medical disciplines, Frontiers in Medicine is launching new sections that together will facilitate - the use of patient-reported outcomes under real world conditions - the exploitation of big data and the use of novel information and communication tools in the assessment of new medicines - the scientific bases for guidelines and decisions from regulatory authorities - access to medicinal products and medical devices worldwide - addressing the grand health challenges around the world