Construction and Validation of A Nomogram to Identify Mucus Obstruction In Patients With Chronic Obstructive Pulmonary Disease

Summary

This study aimed to identify independent clinical predictors of computed tomography (CT)–detected small airway mucus plugs in patients with chronic obstructive pulmonary disease (COPD) and to construct and validate a nomogram for individualized risk prediction.

Abstract

Small airway mucus impaction in chest computed tomography (CT) is a clinically significant finding in chronic obstructive pulmonary disease (COPD), associated with accelerated pulmonary function decline, increased frequency of acute exacerbations, and higher susceptibility to respiratory infections. However, a validated predictive tool for identifying patients at risk of CT-detected mucus plugs is currently lacking. This study aimed to develop and validate a nomogram to predict small airway mucus obstruction in patients with COPD. We retrospectively enrolled 212 COPD patients from Shenzhen Second People’s Hospital (January 2021 to June 2022), of whom 47 had CT-confirmed mucus plugs (mucus plug group, MP) and 165 did not (non-mucus plug group, NMP). Univariate and receiver operating characteristic (ROC) analyses were used to identify candidate predictors. Multivariate logistic regression was conducted to construct the final predictive model, which was then transformed into a nomogram. Internal validation was performed using bootstrap sampling (1000 iterations). Bronchiectasis, chronic rhinosinusitis (CRS), body mass index (BMI), forced expiratory flow at 25–75% of predicted (FEF25–75%pred), residual volume-to-total lung capacity ratio (RV/TLC), and serum 25-hydroxyvitamin D [25(OH)D] were identified as independent risk factors for CT mucus plugs. The nomogram demonstrated excellent predictive value with an AUC of 0.9611. Calibration curves and decision curve analyses demonstrated good clinical utility. Bootstrap internal validation further supported the model’s predictive stability. This nomogram provides a practical, individualized tool to facilitate early identification and personalized management of COPD patients at risk of small-airway mucus obstruction.

Introduction

Chronic obstructive pulmonary disease (COPD) is characterized by persistent and largely irreversible airflow limitation. The World Health Organization states that it is projected to become the third leading cause of death globally by 2030¹. The disease primarily initiates in the small airways (airways with an internal diameter of less than 2 mm), which represent a fundamental site of COPD pathology. Structural and inflammatory changes in these regions often precede the emergence of clinical symptoms by several years, yet contribute substantially to the airflow obstruction. Pathological hallmarks of small airway disease in COPD include infiltrat....

Protocol

The present study was approved by the Ethics Committee of Shenzhen Second People’s Hospital (Protocol No. 20193357024). Informed consent was obtained from all participants or their legal representatives prior to enrollment.

Study population and methodology

This study was designed as a single-center, retrospective cohort study. Medical records of patients with a primary diagnosis of COPD at the Department of Respiratory Medicine, Shenzhen Second People’s Hospital, from January 2021 to June 2022 were reviewed. All adult patients (≥18 years) with a primary diagnosis of COPD were in....

Results

Baseline characteristics

The study comprised a cohort of 212 patients with COPD, divided into two groups: 47 with mucus plugs (MP) and 165 without mucus plugs (NMP). The occurrence of mucus plugs in this COPD population was found to be 28.33%. Statistical analysis, detailed in Table 1, identified significant differences between the MP and NMP groups in several key metrics. These included body mass index (BMI), the frequency of acute exacerbations (AE), preval.......

Discussion

In this study, the prevalence of CT-detected mucus plug formation among hospitalized COPD patients was 22.16%, consistent with estimates reported in prior literature²⁷. Mucus plugs in COPD are clinically significant due to their association with accelerated pulmonary function decline, increased acute exacerbation frequency, and higher mortality risk²⁸. Despite this, a validated predictive tool for identifying at-risk patients was previously lacking. This analysis identified.......

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
HRCT Scans	Shenzhen Second People's Hospital		Used for diagnosing small airway mucus obstruction in COPD patients
SPSS 25.0 Software	1BM		Statistical software used for data analysis, including t-tests and logistic regression.
R Software (Packages: mms, mstate, etc.)	R Foundation for Statistical Computing		Used for statistical analysis and model validation, including calculation of the C-index.
Electronic Medical Record System	Shenzhen Second People's Hospital		Data source for clinical and laboratory variables, including patient history and diagnostic parameters.
Logistic Regression Equation	Custom (Applied via SPSS and R)		Used to screen for independent risk factors related to small airway mucus obstruction in COPD patients.