Assessing the performance of two lung age equations on the Australian population: using data from the cross-sectional...

Lung age, a simple concept for patients to grasp, is frequently used as an aid in smoking cessation programs. Lung age equations should be continuously updated and should be made relevant for target populations. We observed how new lung age equations developed for Australian populations performed when utilizing the Burden of Obstructive Lung Disease (BOLD)-Australia dataset compared to more commonly used equations.

Data from a cross-sectional population study of noninstitutionalized Australians aged ≥40 years with analysis restricted to Caucasians <75 years. Lung age calculated using equations developed by Newbury et al. and Morris and Temple was compared with chronological age by smoking status and within smoking status.

There were 2,793 participants with a mean age of 57 (±10 SD) years. More than half (52%) ever smoked, and 10.4% were current smokers. Prevalence of chronic obstructive pulmonary disease stage I or higher was 13.4% (95% confidence interval = 12.2, 14.7). For both genders, newer Newbury equations estimated lung ages significantly higher than actual age across all smoking groups (p < .05). Morris and Temple equations resulted in lung age estimates significantly lower than chronological age for nonsmokers (p < .05) but no difference among current smokers. Both equations showed exposure to smoking had lung ages higher than never-smokers (p < .001). Lung age also increased with increased pack-years.

This supports the use of updated equations suited to the population of interest. The Australian Newbury equations performed well in the BOLD-Australia dataset, providing more meaningful lung age profile compared to chronological age among smokers. Using equations not developed or ideally suited for our population is likely to produce misleading results.