New Approach to CF Questionnaires Leads To Improved Patient Outcomes
Results from a new study indicate that mapping functions can be used with Cystic Fibrosis Questionnaire datasets to assess the utility values for interventions and costs related to Cystic Fibrosis. The study was recently published in the journal Health and Quality of Life Outcomes.
Cystic fibrosis (CF) is a hereditary and life-threatening autosomal recessive disorder that affects 80,000 children and young adults worldwide. The condition can cause chronic respiratory infections, pancreatic enzyme insufficiency and associated complications.
Despite these advances in the clinical management of CF, the disease represents a burden for patients in terms of their symptoms, loss of functioning and poor health related quality of life (HRQL). The Cystic Fibrosis Questionnaire-Revised (CFQ-R) is a validated patient reported outcome (PRO) measure of HRQL specifically designed for individuals with CF. The US Food and Drug Administration (FDA) and National Institute for Health & Care Excellence (NICE) in the UK have become increasingly interested in the information that can be captured from HRQL PROs. UK national guidelines recommend the use of generic preference-based measures to capture utility, with a stated preference for the EQ-5D questionnaire, however, these data sets are not always collected in clinical trials.
To address this data gap, in the study titled “Mapping the EQ-5D index from the cystic fibrosis questionnaire-revised using multiple modeling approaches,” a collaboration between the Oxford Outcomes Ltd, University of Sheffield and Gilead Sciences designed a mapping algorithm to assess the utility of EQ-5D from data derived from the revised version of the Cystic Fibrosis Questionnaire (CFQ-R).
The research team surveyed a total of 401 patients with CF. Patients were asked to fill the CFQ-R, the EQ-5D and a questionnaire assessing CF patients background. Using robust multivariate statistical approaches such as regression models, exploring item and domain level predictors, the results showed that the items with the best performance were the CFQ-R Physical-, Role- and Emotional-functioning, Vitality, Eating Disturbances, Weight, and Digestive Symptoms domains and a selection of squared terms.
The results indicated that domain and item level models using all three modeling approaches reached an acceptable degree of predictive performance with domain models performing well in out-of-sample validation. Based on the results, the researchers concluded that these specific mapping functions can be applied to CFQ-R datasets to estimate EQ-5D utility values for economic evaluations of interventions for patients with cystic fibrosis. The en result of these findings could help to optimize and streamline patient care for CF and ultimately improve quality of life outcomes through the use of therapeutic interventions.