Collecting patient preference information using a clinical data research network: Demonstrating feasibility with idiopathic pulmonary fibrosis

Abstract

© 2019 Hollin et al. Purpose: Rare diseases present challenges for accessing patient populations to conduct surveys. Clinical Data Research Networks (CDRNs) offer an opportunity to overcome those challenges by providing infrastructure for accessing patients and sharing data. This study aims to demonstrate the feasibility of collecting patient preference information for a rare disease in a CDRN, using idiopathic pulmonary fibrosis as proof of concept. Patients and methods: Utilizing a cohort of idiopathic pulmonary fibrosis (IPF) patients across a CDRN, a discrete choice experiment was administered via electronic and paper methods to collect patient preference information about benefits and risks of two therapeutic options. Survey data were augmented with data from electronic health records and patient-reported outcome surveys. Results: Thirty-three patients completed the preference experiment. The amount of choice attributable to a benefit of slowing of decline in lung function was 36%. Improving efficacy in terms of lung function was 2.16 times as important as improving efficacy in terms of shortness of breath. In terms of side effects, decreasing risk of gastrointestinal problems was 2.6 times as important as decreasing risk of sun sensitivity and 2.4 times as important as decreasing risk of liver injury. In terms of benefit-risk trade-offs, improving efficacy in terms of lung function was 1.6 times as important as decreasing risk of gastrointestinal problems. Conclusion: This study used IPF as a proof of concept to demonstrate the feasibility of collecting patient preference information in a CDRN. The network was advantageous to the study of patient preferences. Future research should continue to explore pathways for the collection and use of patient preference information across networks. The power of consolidated collection efforts may lead to the ability to use preference data to inform decision-making at the regional, specialty, or individual encounter level.