Integrating Event- and Trend-Based Analyses to Improve Detection of Glaucomatous Visual Field Progression

Purpose To present and evaluate a new method of integrating event- and trend-based analyses of visual field progression in glaucoma. Design Observational cohort study. Participants The study included 711 eyes of 357 glaucoma patients or suspects followed up for an average of 5.0±2.0 years with an average of 7.7±2.3 standard automated perimetry visual fields. An additional group of 55 eyes of 55 glaucoma patients underwent repeated tests over a short period to test the specificity of the method. Methods Event-based analysis of progression was performed using the Guided Progression Analysis (GPA; Carl-Zeiss Meditec, Inc., Dublin, CA). Trend-based assessment used the visual field index (VFI). A hierarchical Bayesian model was built to incorporate results from the GPA in the prior distribution for the VFI slopes, allowing the event-based method to influence the inferences made for the trend-based assessment. Main Outcome Measures The Bayesian method was compared with the conventional ordinary least squares (OLS) regression method of trend-based assessment. Results Of the 711 eyes followed up over time, 64 (9%) had confirmed progression with GPA. Bayesian slopes of VFI change were able to detect 63 of these eyes (98%). An additional group of 49 eyes (7%) had progression by Bayesian slopes, but not by GPA. Slopes of VFI change calculated by the OLS method were able to identify only 32 of the 64 eyes (50%) with GPA progression. The agreement with GPA was significantly better for the Bayesian compared with the OLS method (κ = 0.68 vs. 0.43, respectively; P