Median regression spline modeling of longitudinal FEV1 measurements in cystic fibrosis (CF) and chronic obstructive pulmonary disease (COPD) patients.

RATIONALE: Clinical phenotyping, therapeutic investigations as well as genomic, airway secretion metabolomic and metagenomic investigations can benefit from robust, nonlinear modeling of FEV1 in individual subjects. We demonstrate the utility of measuring FEV1 dynamics in representative cystic fibrosis (CF) and chronic obstructive pulmonary disease (COPD) populations. METHODS: Individual FEV1 data from CF and COPD subjects were modeled by estimating median regression splines and their predicted first and second derivatives. Classes were created from variables that capture the dynamics of these curves in both cohorts. RESULTS: Nine FEV1 dynamic variables were identified from the splines and their predicted derivatives in individuals with CF (n = 177) and COPD (n = 374). Three FEV1 dynamic classes (i.e. stable, intermediate and hypervariable) were generated and described using these variables from both cohorts. In the CF cohort, the FEV1 hypervariable class (HV) was associated with a clinically unstable, female-dominated phenotypes while stable FEV1 class (S) individuals were highly associated with the male-dominated milder clinical phenotype. In the COPD cohort, associations were found between the FEV1 dynamic classes, the COPD GOLD grades, with exacerbation frequency and symptoms. CONCLUSION: Nonlinear modeling of FEV1 with splines provides new insights and is useful in characterizing CF and COPD clinical phenotypes.