Leo L. Duan

A semiparametric approach to estimate rapid lung function decline in cystic fibrosis

PURPOSE Detecting the onset of rapid lung function decline is important to reduce mortality rates in cystic fibrosis (CF) and other lung diseases. The most common approach is conventional linear mixed modeling-estimating a population-level slope of lung function decline and using random effects to address serial correlation-but this ignores nonlinear features of disease progression and distinct sources of variability. The purpose of this article was to estimate patient-specific timing and degree of rapid decline while appropriately characterizing natural progression and variation in CF. METHODS We propose longitudinal semiparametric mixed modeling and contrast it with the conventional approach, which restricts lung function (measured as forced expiratory volume in 1 second as a percentage of predicted, FEV1%) to linear decline. Each approach is applied to clinical encounter data from the United States CF Foundation Patient Registry. RESULTS Timing and degree of rapid FEV1% decline vary across patients and as a function of key covariates. Patients experience maximal FEV1% loss by early adulthood more severe than indicated by conventional slope analysis. CONCLUSIONS Semiparametric mixed modeling provides a means to estimate patient-specific changes in CF disease progression and may be used to inform prognostic decisions in chronic care settings and clinical studies.

Concentration of fractional excretion of nitric oxide (FENO): A potential airway biomarker of restored CFTR function

BACKGROUND Lower airway biomarkers of restored cystic fibrosis transmembrane conductance regulator (CFTR) function are limited. We hypothesized that fractional excretion of nitric oxide (FENO), typically low in CF patients, would demonstrate reproducibility during CFTR-independent therapies, and increase during CFTR-specific intervention (ivacaftor) in patients with CFTR gating mutations. METHODS Repeated FENO and spirometry measurements in children with CF (Cohort 1; n=29) were performed during hospital admission for acute pulmonary exacerbations and routine outpatient care. FENO measurements before and after one month of ivacaftor treatment (150 mg every 12h) were completed in CF patients with CFTR gating mutations (Cohort 2; n=5). RESULTS Cohort 1: Mean forced expiratory volume in 1s (FEV1 % predicted) at enrollment was 72.3% (range 25%-102%). Mean FENO measurements varied minimally over the two inpatient and two outpatient measurements (9.8-10.9 ppb). There were no clear changes related to treatment of pulmonary exacerbations, gender, genotype or microbiology, and weak correlation with inhaled corticosteroid use (P<0.05). Between the two inpatient measurements, FEV1 % predicted increased by 7.3% (P<0.03) and FENO did not change. In Cohort 2, mean FENO increased from 6.6 ppb (SD=2.19) to 11.8 ppb (SD=4.97) during ivacaftor treatment. Mean sweat chloride dropped by 58 mM and mean FEV1 % predicted increased by 10.2%. CONCLUSIONS Repeated FENO measurements were stable in CF patients, whereas FENO increased in all patients with CFTR gating mutations treated with ivacaftor. Acute changes in FENO may serve as a biomarker of restored CFTR function in the CF lower airway during CFTR modulator treatment.

Bayesian Ensemble Trees (BET) for Clustering and Prediction in Heterogeneous Data

We propose a novel “tree-averaging” model that uses the ensemble of classification and regression trees (CART). Each constituent tree is estimated with a subset of similar data. We treat this grouping of subsets as Bayesian ensemble trees (BET) and model them as a Dirichlet process. We show that BET determines the optimal number of trees by adapting to the data heterogeneity. Compared with the other ensemble methods, BET requires much fewer trees and shows equivalent prediction accuracy using weighted averaging. Moreover, each tree in BET provides variable selection criterion and interpretation for each subset. We developed an efficient estimating procedure with improved estimation strategies in both CART and mixture models. We demonstrate these advantages of BET with simulations and illustrate the approach with a real-world data example involving regression of lung function measurements obtained from patients with cystic fibrosis. Supplementary materials for this article are available online.