RT Journal Article
SR Electronic
T1 Individualized prediction of lung-function decline in chronic obstructive pulmonary disease
JF Canadian Medical Association Journal
JO CMAJ
FD Canadian Medical Association
SP 1004
OP 1011
DO 10.1503/cmaj.151483
VO 188
IS 14
A1 Zafar Zafari
A1 Don D. Sin
A1 Dirkje S. Postma
A1 Claes-Göran Löfdahl
A1 Judith Vonk
A1 Stirling Bryan
A1 Stephen Lam
A1 C. Martin Tammemagi
A1 Rahman Khakban
A1 S.F. Paul Man
A1 Donald Tashkin
A1 Robert A. Wise
A1 John E. Connett
A1 Bruce McManus
A1 Raymond Ng
A1 Zsuszanna Hollander
A1 Mohsen Sadatsafavi
YR 2016
UL http://www.cmaj.ca/content/188/14/1004.abstract
AB 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.Trial registration: Lung Health Study — ClinicalTrials.gov, no. NCT00000568; Pan-Canadian Early Detection of Lung Cancer Study — ClinicalTrials.gov, no. NCT00751660