Individualized prediction of lung-function decline in chronic obstructive pulmonary disease

TitleIndividualized prediction of lung-function decline in chronic obstructive pulmonary disease
Publication TypeJournal Article
Year of Publication2016
AuthorsZafari, Z, Sin, D, Postma, DS, Löfdahl, CG, Vonk, J, Bryan, S, Lam, S, Tammemagi, CM, Khakban, R, Man, SF, Tashkin, D, Wise, RA, Connett, JE, McManus, B, Ng, R, Hollander, Z, Sadatsafavi, M
JournalCanadian Medical Association Journal
Volume188
Issue14
Pagination1004-1011
Date Published10/2016
ISSNPrint: 0820-3946; Online: 1488-2329
Abstract

BACKGROUND:

The rate of lung-function decline in chronic obstructive pulmonary disease (COPD) varies substantially among individuals. We sought to develop and validate an individualized prediction model for forced expiratory volume at 1 second (FEV1) in current smokers with mild-to-moderate COPD.

METHODS:

Using data from a large long-term clinical trial (the Lung Health Study), we derived mixed-effects regression models to predict future FEV1 values over 11 years according to clinical traits. We modelled heterogeneity by allowing regression coefficients to vary across individuals. Two independent cohorts with COPD were used for validating the equations.

RESULTS:

We used data from 5594 patients (mean age 48.4 yr, 63% men, mean baseline FEV1 2.75 L) to create the individualized prediction equations. There was significant between-individual variability in the rate of FEV1 decline, with the interval for the annual rate of decline that contained 95% of individuals being -124 to -15 mL/yr for smokers and -83 to 15 mL/yr for sustained quitters. Clinical variables in the final model explained 88% of variation around follow-up FEV1. The C statistic for predicting severity grades was 0.90. Prediction equations performed robustly in the 2 external data sets.

INTERPRETATION:

A substantial part of individual variation in FEV1 decline can be explained by easily measured clinical variables. The model developed in this work can be used for prediction of future lung health in patients with mild-to-moderate COPD.

DOI10.1503/cmaj.151483