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

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.