Predictive models of choroidal neovascularization and geographic atrophy incidence applied to clinical trial design.

PURPOSE: To develop comprehensive predictive models for choroidal neovascularization (CNV) and geographic atrophy (GA) incidence within 3 years that can be applied realistically to clinical practice. DESIGN: Retrospective evaluation of data from a longitudinal study to develop and validate predictive models of CNV and GA. METHODS: The predictive performance of clinical, environmental, demographic, and genetic risk factors was explored in regression models, using data from both eyes of 2011 subjects from the Age-Related Eye Disease Study (AREDS). The performance of predictive models was compared using 10-fold cross-validated receiver operating characteristic curves in the training data, followed by comparisons in an independent validation dataset (1410 AREDS subjects). Bayesian trial simulations were used to compare the usefulness of predictive models to screen patients for inclusion in prevention clinical trials. RESULTS: Logistic regression models that included clinical, demographic, and environmental factors had better predictive performance for 3-year CNV and GA incidence (area under the receiver operating characteristic curve of 0.87 and 0.89, respectively), compared with simple clinical criteria (AREDS simplified severity scale). Although genetic markers were associated significantly with 3-year CNV (CFH: Y402H; ARMS2: A69S) and GA incidence (CFH: Y402H), the inclusion of genetic factors in the models provided only marginal improvements in predictive performance. CONCLUSIONS: The logistic regression models combine good predictive performance with greater flexibility to optimize clinical trial design compared with simple clinical models (AREDS simplified severity scale). The benefit of including genetic factors to screen patients for recruitment to CNV prevention studies is marginal and is dependent on individual clinical trial economics.