An overview of international literature from cystic fibrosis registries. Part 5: Update 2012-2015 on lung disease

Cystic Fibrosis Point of Personalized Detection (CFPOPD): An Interactive Web Application

Background

Despite 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.

Objective

This 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.

Methods

Longitudinal 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).

Results

A 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.

Conclusions

Our 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.

Dynamic predictive probabilities to monitor rapid cystic fibrosis disease progression

Cystic fibrosis (CF) is a progressive, genetic disease characterized by frequent, prolonged drops in lung function. Accurately predicting rapid underlying lung‐function decline is essential for clinical decision support and timely intervention. Determining whether an individual is experiencing a period of rapid decline is complicated due to its heterogeneous timing and extent, and error component of the measured lung function. We construct individualized predictive probabilities for “nowcasting” rapid decline. We assume each patient's true longitudinal lung function, S(t), follows a nonlinear, nonstationary stochastic process, and accommodate between‐patient heterogeneity through random effects. Corresponding lung‐function decline at time t is defined as the rate of change, S′(t). We predict S′(t) conditional on observed covariate and measurement history by modeling a measured lung function as a noisy version of S(t). The method is applied to data on 30 879 US CF Registry patients. Results are contrasted with a currently employed decision rule using single‐center data on 212 individuals. Rapid decline is identified earlier using predictive probabilities than the center's currently employed decision rule (mean difference: 0.65 years; 95% confidence interval (CI): 0.41, 0.89). We constructed a bootstrapping algorithm to obtain CIs for predictive probabilities. We illustrate real‐time implementation with R Shiny. Predictive accuracy is investigated using empirical simulations, which suggest this approach more accurately detects peak decline, compared with a uniform threshold of rapid decline. Median area under the ROC curve estimates (Q1‐Q3) were 0.817 (0.814‐0.822) and 0.745 (0.741‐0.747), respectively, implying reasonable accuracy for both. This article demonstrates how individualized rate of change estimates can be coupled with probabilistic predictive inference and implementation for a useful medical‐monitoring approach.

Developing Multicenter Registries to Advance Quality Science

There are several benefits to clinical registries as an information repository tool, ultimately lending itself to the acquisition of new knowledge. Registries have the unique advantage of garnering much data quickly and are, therefore, especially helpful for niche populations or low-prevalence diseases. They can be used to inform on the ideal structure, process, or outcome involving an identified population. The data can be used in many ways, for example, as an observational tool to reveal associations or as a basis for framing future research studies or quality improvement projects.

Update of literature from cystic fibrosis registries 2012-2015. Part 6: Epidemiology, nutrition and complications

Patient registries provide useful information to afford more knowledge on rare diseases like Cystic Fibrosis (CF). Twenty-two studies originating from national CF registries, focusing on demographics, survival, genetics, nutritional status, and non-pulmonary complications, were published between December 2011 and March 2015. The purpose of this review article is to examine these reports, aiming attention to the clinical characteristics of CF patients included in the registries, current, and estimated future epidemiological data, the role of gender gap, the increasing survival in different countries. Some studies offer insights into pubertal growth and non-pulmonary complications, such as liver disease, nephropathy, and cancer. Pediatr Pulmonol. 2017;52:390-398. © 2016 Wiley Periodicals, Inc.