Classifying severity of cystic fibrosis lung disease using longitudinal pulmonary function data.

Rationale: The study of genetic modifiers in cystic fibrosis (CF) lung disease requires rigorous phenotyping. One type of genetic association study design compares polymorphisms in patients at extremes of phenotype, requiring accurate classification of pulmonary disease at varying ages.

Objective: To evaluate approaches to quantify severity of pulmonary disease and their ability to discriminate between patients with CF at the extremes of phenotype.

Methods: DeltaF508 homozygotes (n = 828) were initially classified as "severe" (approximate lowest quartile of FEV(1) (% pred) for age, 8-25 yr) or "mild" disease (highest quartile of FEV(1) for age, > or = 15 yr). FEV(1) measurements from the 5 yr before enrollment (total = 18,501 measurements; average 23 per subject) were analyzed with mixed models, and patient-specific estimates of FEV(1) (% pred) at ages 5, 10, 15, 20, and 25 yr and slope of FEV(1) versus age were examined for their ability to discriminate between groups using receiver operating characteristics (ROC) curve areas.

Results: Logistic regression of severity group on mixed model (empirical Bayes) estimates of intercept and slope of FEV(1) (% pred) versus age discriminated better than did classification using FEV(1) slope alone (ROC area = 0.995 vs. 0.821) and was equivalent to using estimated FEV(1) at 20 yr of age as a single discriminator. The estimated survival percentile from a joint survival/longitudinal model provided equally good classification (ROC area = 0.994).

Conclusions: In CF, estimated FEV(1) (% pred) at 20 yr of age and the estimated survival percentile are useful indices of pulmonary disease severity.