Estimation of lung age via a spline method and its application in chronic respiratory diseases

• Published in: NPJ primary care respiratory medicine Vol. 32; no. 1; p. 36
• Main Authors: Liang, Xiaolin, Xie, Yanqing, Gao, Yi, Zhou, Yumin, Jian, Wenhua, Jiang, Mei, Wang, Hongyu, Zheng, Jinping
• Format: Journal Article
• Language: English
• Published: Amsterdam Nature Publishing Group 29.09.2022; Nature Publishing Group UK; Nature Portfolio
• Subjects: Age; Asthma; Chronic obstructive pulmonary disease; Regression analysis; Respiratory diseases
• ISSN: 2055-1010; 2055-1010
• DOI: 10.1038/s41533-022-00293-9

Abstract Lung age is a simplified concept that makes spirometry data easier to understand, but it is not widely used due to limitations in estimation methods. The aim of this study was to develop new equations to estimate lung age and to explore the application value of lung age in chronic respiratory diseases. Retrospective spirometric data of 18- to 80-year-old healthy subjects were used to develop the lung age estimation equations. Models were respectively built by multiple linear regression, piecewise linear regression, and the natural cubic spline method. Patients with chronic obstructive pulmonary disease (COPD) and asthma were subdivided into stages I–IV according to the severity of airflow limitation under the recommendation of the Global Initiative for Chronic Obstructive Lung Disease. Propensity score matching was performed to balance age, height and sex between healthy subjects and patients. The difference between lung age and chronological age (∆ lung age) of patients with COPD and asthma was analyzed. A total of 3409 healthy subjects, 280 patients with COPD and 285 patients with asthma data were included in the analysis. The lung age estimation equation with the best goodness of fit was built by the spline method and composed of FEV 1 , FEF 50% , FEF 75% and height as explanatory variables. ∆ lung age progressively increased with the degree of airflow limitation in patients with COPD or asthma. Lung age estimation equations were developed by a spline modeling method. Lung age may be used in the assessment of chronic respiratory patients.