Dynamic Prediction of Survival in Cystic Fibrosis: A Landmarking Analysis Using UK Patient Registry Data. Issue 1 (January 2019)

• Record Type: Journal Article
• Authors: Keogh, Ruth H.; Seaman, Shaun R.; Barrett, Jessica K.; Taylor-Robinson, David; Szczesniak, Rhonda
• Is Part Of: Epidemiology. Volume 30:Issue 1(2019)
• Publication Date: 2019-01
• Subjects: Cox regression -- Cystic fibrosis -- Dynamic prediction -- Landmarking -- Longitudinal data -- Patient registry -- Personalized prediction -- Survival; Epidemiology -- Periodicals; Epidemiology -- Environmental aspects -- Periodicals; Epidemiology -- Periodicals; 614.405
• DOI: 10.1097/EDE.0000000000000920
• Languages: English
• ISSNs: 1044-3983

Abstract

Abstract : Background: Cystic fibrosis (CF) is an inherited, chronic, progressive condition affecting around 10, 000 individuals in the United Kingdom and over 70, 000 worldwide. Survival in CF has improved considerably over recent decades, and it is important to provide up-to-date information on patient prognosis. Methods: The UK Cystic Fibrosis Registry is a secure centralized database, which collects annual data on almost all CF patients in the United Kingdom. Data from 43, 592 annual records from 2005 to 2015 on 6181 individuals were used to develop a dynamic survival prediction model that provides personalized estimates of survival probabilities given a patient's current health status using 16 predictors. We developed the model using the landmarking approach, giving predicted survival curves up to 10 years from 18 to 50 years of age. We compared several models using cross-validation. Results: The final model has good discrimination (C-indexes: 0.873, 0.843, and 0.804 for 2-, 5-, and 10-year survival prediction) and low prediction error (Brier scores: 0.036, 0.076, and 0.133). It identifies individuals at low and high risk of short- and long-term mortality based on their current status. For patients 20 years of age during 2013–2015, for example, over 80% had a greater than 95% probability of 2-year survival and 40% were predicted to survive 10 years or more. Conclusions: Dynamic personalized prediction models can guide treatment decisions and provide personalized information for patients. Our application illustrates the utility of the landmarking approach for making the best use of longitudinal and survival data and shows how models can be defined and compared in terms of predictive performance. Abstract : Supplemental Digital Content is available in the text.