Abstract
BackgroundDespite steady gains in life expectancy, individuals with cystic fibrosis (CF) lung disease still experience rapid pulmonary decline throughout their clinical course, which can ultimately end in respiratory failure. Point-of-care tools for accurate and timely information regarding the risk of rapid decline is essential for clinical decision support.ObjectiveThis study aims to translate a novel algorithm for earlier, more accurate prediction of rapid lung function decline in patients with CF into an interactive web-based application that can be integrated within electronic health record systems, via collaborative development with clinicians.MethodsLongitudinal clinical history, lung function measurements, and time-invariant characteristics were obtained for 30,879 patients with CF who were followed in the US Cystic Fibrosis Foundation Patient Registry (2003-2015). We iteratively developed the application using the R Shiny framework and by conducting a qualitative study with care provider focus groups (N=17).ResultsA clinical conceptual model and 4 themes were identified through coded feedback from application users: (1) ambiguity in rapid decline, (2) clinical utility, (3) clinical significance, and (4) specific suggested revisions. These themes were used to revise our application to the currently released version, available online for exploration. This study has advanced the application’s potential prognostic utility for monitoring individuals with CF lung disease. Further application development will incorporate additional clinical characteristics requested by the users and also a more modular layout that can be useful for care provider and family interactions.ConclusionsOur framework for creating an interactive and visual analytics platform enables generalized development of applications to synthesize, model, and translate electronic health data, thereby enhancing clinical decision support and improving care and health outcomes for chronic diseases and disorders. A prospective implementation study is necessary to evaluate this tool’s effectiveness regarding increased communication, enhanced shared decision-making, and improved clinical outcomes for patients with CF.
Highlights
BackgroundCystic fibrosis (CF) is a life-limiting, recessively inherited disease resulting from mutations in the cystic fibrosis transmembrane conductance regulator (CFTR) gene
Preliminary clinician feedback from CF chart and data conferences provided a blueprint for a bootstrap layout and structure, which was developed during the first 3 versions of Cystic Fibrosis Point of Personalized Detection (CFPOPD) [17]
Clinician participants formally reviewed versions 3 and 7.1, and a subset of participants commented on intermittent updates to CFPOPD
Summary
BackgroundCystic fibrosis (CF) is a life-limiting, recessively inherited disease resulting from mutations in the cystic fibrosis transmembrane conductance regulator (CFTR) gene. Tools to predict rapid decline are crucial for clinical decision support and timely intervention. Linear mixed-effects models with random intercepts and slopes are commonly employed but are problematic because lung function data are correlated within an individual over time in a potentially more complex and nonlinear manner [5]. We recently used a nonstationary Gaussian linear mixed-effects model [7] to predict rapid FEV1 decline using data from the US Cystic Fibrosis Foundation Patient Registry (CFFPR) [8]. Despite steady gains in life expectancy, individuals with cystic fibrosis (CF) lung disease still experience rapid pulmonary decline throughout their clinical course, which can end in respiratory failure. Point-of-care tools for accurate and timely information regarding the risk of rapid decline is essential for clinical decision support
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