Rhonda D Szczesniak1,2,3, Dan Li4, Weiji Su5, Cole Brokamp1, John Pestian6,3, Michael Seid2,7,3, John P Clancy2,3. 1. 1 Division of Biostatistics and Epidemiology. 2. 2 Division of Pulmonary Medicine. 3. 3 Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, Ohio. 4. 4 Alzheimer's Therapeutic Research Institute, Keck School of Medicine, University of Southern California, Los Angeles, California; and. 5. 5 Department of Mathematical Sciences, University of Cincinnati, Cincinnati, Ohio. 6. 6 Division of Biomedical Informatics, and. 7. 7 James M. Anderson Center for Health Systems Excellence, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio.
Abstract
RATIONALE: Individuals with cystic fibrosis are at risk for prolonged drops in lung function, clinically termed rapid decline, during discreet periods of the disease. OBJECTIVES: To identify phenotypes of rapid pulmonary decline and determine how these phenotypes are related to patient characteristics. METHODS: A longitudinal cohort study of patients with cystic fibrosis aged 6-21 years was conducted using the Cystic Fibrosis Foundation Patient Registry. A statistical approach for clustering longitudinal profiles, sparse functional principal components analysis, was used to classify patients into distinct phenotypes by evaluating trajectories of FEV1 decline. Phenotypes were compared with respect to baseline and mortality characteristics. MEASUREMENTS AND MAIN RESULTS: Three distinct phenotypes of rapid decline were identified, corresponding to early, middle, and late timing of maximal FEV1 loss, in the overall cohort (n = 18,387). The majority of variation (first functional principal component, 94%) among patient profiles was characterized by differences in mean longitudinal FEV1 trajectories. Average degree of rapid decline was similar among phenotypes (roughly -3% predicted/yr); however, average timing differed, with early, middle, and late phenotypes experiencing rapid decline at 12.9, 16.3, and 18.5 years of age, respectively. Individuals with the late phenotype had the highest initial FEV1 but experienced the greatest loss of lung function. The early phenotype was more likely to have respiratory infections and acute exacerbations at baseline or to develop them subsequently, compared with other phenotypes. CONCLUSIONS: By identifying phenotypes and associated risk factors, timing of interventions may be more precisely targeted for subgroups at highest risk of lung function loss.
RATIONALE: Individuals with cystic fibrosis are at risk for prolonged drops in lung function, clinically termed rapid decline, during discreet periods of the disease. OBJECTIVES: To identify phenotypes of rapid pulmonary decline and determine how these phenotypes are related to patient characteristics. METHODS: A longitudinal cohort study of patients with cystic fibrosis aged 6-21 years was conducted using the Cystic Fibrosis Foundation Patient Registry. A statistical approach for clustering longitudinal profiles, sparse functional principal components analysis, was used to classify patients into distinct phenotypes by evaluating trajectories of FEV1 decline. Phenotypes were compared with respect to baseline and mortality characteristics. MEASUREMENTS AND MAIN RESULTS: Three distinct phenotypes of rapid decline were identified, corresponding to early, middle, and late timing of maximal FEV1 loss, in the overall cohort (n = 18,387). The majority of variation (first functional principal component, 94%) among patient profiles was characterized by differences in mean longitudinal FEV1 trajectories. Average degree of rapid decline was similar among phenotypes (roughly -3% predicted/yr); however, average timing differed, with early, middle, and late phenotypes experiencing rapid decline at 12.9, 16.3, and 18.5 years of age, respectively. Individuals with the late phenotype had the highest initial FEV1 but experienced the greatest loss of lung function. The early phenotype was more likely to have respiratory infections and acute exacerbations at baseline or to develop them subsequently, compared with other phenotypes. CONCLUSIONS: By identifying phenotypes and associated risk factors, timing of interventions may be more precisely targeted for subgroups at highest risk of lung function loss.
Entities:
Keywords:
cluster analysis; epidemiology; functional data analysis; nonlinear trajectories; pulmonary function
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