AIMS: To produce new lung age equations using four previously published predictive equations for forced expiratory volume in 1 second and to compare them with lung age equations published in 1985 and 2010. METHODS: Initial comparisons used phantom subjects of different ages and levels of lung function. Comparison of lung age equations by regression analysis used an independent dataset of 3,206 randomly selected community-dwelling adults aged >18 years in the North West Adelaide Health Study. RESULTS: The more recent equations estimated lung age as greater than chronological age as lung function decreased, whereas the oldest equations estimated lung age as less than chronological age until lung function was severely limited. Significant differences (p<0.001) were detected by regression analysis, with four newer lung age equations being significantly different from the 1985 equation and one being no different. CONCLUSIONS: Lung age estimates using six predictive equations spanning 50 years show differences attributable to cohort and period effects. This reinforces the need for regular updating of predictive equations for lung function. These results further confirm the need to use modern lung age equations which will provide a stronger message in smoking cessation counselling.
AIMS: To produce new lung age equations using four previously published predictive equations for forced expiratory volume in 1 second and to compare them with lung age equations published in 1985 and 2010. METHODS: Initial comparisons used phantom subjects of different ages and levels of lung function. Comparison of lung age equations by regression analysis used an independent dataset of 3,206 randomly selected community-dwelling adults aged >18 years in the North West Adelaide Health Study. RESULTS: The more recent equations estimated lung age as greater than chronological age as lung function decreased, whereas the oldest equations estimated lung age as less than chronological age until lung function was severely limited. Significant differences (p<0.001) were detected by regression analysis, with four newer lung age equations being significantly different from the 1985 equation and one being no different. CONCLUSIONS: Lung age estimates using six predictive equations spanning 50 years show differences attributable to cohort and period effects. This reinforces the need for regular updating of predictive equations for lung function. These results further confirm the need to use modern lung age equations which will provide a stronger message in smoking cessation counselling.
Authors: Donna R Parker; Sarah Eltinge; Caitlin Rafferty; Charles B Eaton; Jennifer G Clarke; Roberta E Goldman Journal: Lung Date: 2015-03-20 Impact factor: 2.584