Analysis of influencing factors and a predictive model of small airway dysfunction in adults

Small airway dysfunction (SAD) is a widespread but less typical clinical manifestation of respiratory dysfunction. In lung diseases, SAD can have a higher-than-expected impact on lung function. The aim of this study was to explore risk factors for SAD and to establish a predictive model.

We included 1233 patients in the pulmonary function room of TangDu Hospital from June 2021 to December 2021. We divided the subjects into a small airway disorder group and a non-small airway disorder group, and all participants completed a questionnaire. We performed univariate and multivariate analyses to identify the risk factors for SAD. Multivariate logistic regression was performed to construct the nomogram. The performance of the nomogram was assessed and validated by the Area under roc curve (AUC), calibration curves, and Decision curve analysis (DCA).

One. The risk factors for small airway disorder were advanced age (OR = 7.772,95% CI 2.284-26.443), female sex (OR = 1.545,95% CI 1.103-2.164), family history of respiratory disease (OR = 1.508,95% CI 1.069-2.126), history of occupational dust exposure (OR = 1.723,95% CI 1.177-2.521), history of smoking (OR = 1.732,95% CI 1.231-2.436), history of pet exposure (OR = 1.499,95% CI 1.065-2.110), exposure to O₃ (OR = 1.008,95% CI 1.003-1.013), chronic bronchitis (OR = 1.947,95% CI 1.376-2.753), emphysema (OR = 2.190,95% CI 1.355-3.539) and asthma (OR = 7.287,95% CI 3.546-14.973). 2. The AUCs of the nomogram were 0.691 in the training set and 0.716 in the validation set. Both nomograms demonstrated favourable clinical consistency. 3.There was a dose‒response relationship between cigarette smoking and SAD; however, quitting smoking did not reduce the risk of SAD.

Small airway disorders are associated with age, sex, family history of respiratory disease, occupational dust exposure, smoking history, history of pet exposure, exposure to O₃, chronic bronchitis, emphysema, and asthma. The nomogram based on the above results can effectively used in the preliminary risk prediction.