P116 Developing a risk prediction tool for spirometrically defined COPD in adults; experience from the ageing lungs in european cohorts project. (December 2018) - Jarvis, D; Minelli, C; Adamson, A; Portas, L; Garcia-Aymerich, J; Elfadaly, F; Jarvelin, MR; Probst-Hensch, N; Henderson, J; Svanes, C; Dharmage, S
Abstract : Background: The EU H2020 funded Ageing Lungs in European Cohorts study (ALEC www.alecstudy.org) is a consortia of population-based birth and adult inception cohorts with repeated measures of spirometry several years apart. Using these data, ALEC aims to identify risk factors for spirometrically defined COPD, and to develop an online risk prediction tool for use by the public/physicians. Our systematic review of available risk prediction tools (Matheson et al IJCOPD 2018;13:1927–35) showed previous attempts had used numerous variables (most commonly smoking, sex and age), but current models could not accurately rule in nor rule out future risk of COPD. Method: Using Bayesian statistical methods we developed a model to predict a subject's lifetime risk of developing COPD (FEV1/FVC<LLN). This model can use information from all cohorts even when some predictors of interest have not been collected within a cohort. Compared with existing approaches to deal with variables missing across studies, our Bayesian approach performs well and is flexible. Furthermore, it allows inclusion in the model of evidence from elsewhere on the effect of specific predictors if available and appropriate. Using R Shiny, we developed an online prototype interface that implements our prediction model allowing users to answer questions on their physical characteristics and lifestyle to generate a personalised risk of COPD. This prototype online prediction tool was reviewed by a group of researchers and clinicians. Results: R shiny provides a technically suitable web-based interface for input of personal information and linkage to model outputs. Feedback from potential users was cautiously positive with concerns expressed regarding presentation of risk, provision of information to patients directing them for further health advice, limitation of underlying data to white Caucasian populations, interpretation/management of 'low risk of COPD' in those who smoked, and absence of risk prediction of exacerbations in those with established disease. Conclusion: Using appropriate statistical methods, it is possible to develop a risk prediction model by combining data across cohorts even when they have not all collected exactly the same information. R Shiny provides a user-friendly means to create online disease risk prediction tools; however many challenges remain regarding full-scale implementation. These are common to many risk prediction tools.