A comparison of clinic and home spirometry as longtudinal outcomes in cystic fibrosis.

BACKGROUND: The COVID-19 pandemic has accelerated the transition to telehealth, including the use of home spirometry in cystic fibrosis. Evaluating the accuracy and precision of longitudinal home spirometry is a requisite for telehealth-based research. This secondary analysis of a CF study (eICE) evaluates whether there are cross-sectional or longitudinal differences between home and clinic spirometry. METHODS: Participants age ≥14 years with ppFEV1>25 were recruited from 2011-2015, issued a home spirometer, and asked to complete spirometry efforts twice per week for one year. Clinic spirometry was collected at baseline and every three months. Cross-sectional differences between clinic spirometry and the closest home spirometry measurement were analyzed. Longitudinally, we apply 5 methods to analyze the precision of home spirometry, and differences between clinic vs. home data. RESULTS: Home spirometry is estimated to be 2.0 (95% CI: 0.3, 3.5) percentage points lower than clinic spirometry cross-sectionally. Longitudinally, the estimates of 12-month change in home spirometry varied by analysis method from -2.6 to -1.0 ppFEV1/ year, with precision markedly different. However, home spirometry change estimates were qualitatively similar to the clinic results: -3.0 ppFEV1/year (95% CI: -4.1, -1.9). CONCLUSIONS: To leverage the potential cost, feasibility and convenience of home spirometry, the differences with clinic spirometry must be acknowledged. Significantly lower ppFEV1 in home devices shows that direct comparison to clinic spirometers may induce a spurious change from baseline, and additional variability in home devices impacts statistical power. The effect of coaching, setting, and equipment must be understood to use and improve home spirometry in CF.

Clinical Effectiveness of Elexacaftor/Tezacaftor/Ivacaftor in People with Cystic Fibrosis: A Clinical Trial.

Rationale: The cystic fibrosis (CF) modulator drug, elexacaftor/tezacaftor/ivacaftor (ETI), proved highly effective in controlled clinical trials for individuals with at least one F508del allele, which occurs in at least 85% of people with CF. Objectives: PROMISE is a postapproval study to understand the broad effects of ETI through 30 months' clinical use in a more diverse U.S. patient population with planned analyses after 6 months. Methods: Prospective, observational study in 487 people with CF age 12 years or older with at least one F508del allele starting ETI for the first time. Assessments occurred before and 1, 3, and 6 months into ETI therapy. Outcomes included change in percent predicted FEV1 (ppFEV1), sweat chloride concentration, body mass index (BMI), and self-reported respiratory symptoms. Measurements and Main Results: Average age was 25.1 years, and 44.1% entered the study using tezacaftor/ivacaftor or lumacaftor/ivacaftor, whereas 6.7% were using ivacaftor, consistent with F508del homozygosity and G551D allele, respectively. At 6 months into ETI therapy, ppFEV1 improved 9.76 percentage points (95% confidence interval [CI], 8.76 to 10.76) from baseline, cystic fibrosis questionnaire-revised respiratory domain score improved 20.4 points (95% CI, 18.3 to 22.5), and sweat chloride decreased -41.7 mmol/L (95% CI, -43.8 to -39.6). BMI also significantly increased. Changes were larger in those naive to modulators but substantial in all groups, including those treated with ivacaftor at baseline. Conclusions: ETI by clinical prescription provided large improvements in lung function, respiratory symptoms, and BMI in a diverse population naive to modulator drug therapy, using existing two-drug combinations, or using ivacaftor alone. Each group also experienced significant reductions in sweat chloride concentration, which correlated with improved ppFEV1 in the overall study population. Clinical trial registered with www.clinicaltrials.gov (NCT NCT04038047).

Tuning parameter selection for a penalized estimator of species richness.

Our goal is to estimate the true number of classes in a population, called the species richness. We consider the case where multiple frequency count tables have been collected from a homogeneous population, and investigate a penalized maximum likelihood estimator under a negative binomial model. Because high probabilities of unobserved classes increase the variance of species richness estimates, our method penalizes the probability of a class being unobserved. Tuning the penalization parameter is challenging because the true species richness is never known, and so we propose and validate four novel methods for tuning the penalization parameter. We illustrate and contrast the performance of the proposed methods by estimating the strain-level microbial diversity of Lake Champlain over 3 consecutive years, and global human host-associated species-level microbial richness.