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

Abstract

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 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 had repeated tests over a short-period of time to test the specificity of the method. Methods Event-based analysis of progression was performed using the Guided Progression Analysis (GPA). 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 to the conventional ordinary least squares (OLS) regression method of trend-based assessment. Results From the 711 eyes followed 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 (50%) of the 64 eyes with GPA progression. The agreement with GPA was significantly better for the Bayesian compared to the OLS method (kappa of 0.68 vs. 0.43, respectively; P<0.001). Eyes progressing only by the Bayesian method had faster rates of change than those progressing only by the OLS method. When applied to the 55 eyes in the stable glaucoma group, both the Bayesian and the OLS methods had a specificity of 96%. Conclusion A Bayesian hierarchical modeling approach for integrating event- and trend-based assessments of visual field progression performed better than either method used alone. Estimates of rates of change obtained from the Bayesian model had increased precision and may be superior to the conventional OLS method for providing information on the risk of development of functional impairment in the disease.

Comparison of Glaucoma Progression Detection by Optical Coherence Tomography and Visual Field

“…The ideal method to assess specificity is to acquire many measurements over a short period of time. 8,55 Unfortunately, this type of data was not available in the AIG Study. Another limitation is that there were very few patients with end stage glaucoma in the study.…”

Combining optical coherence tomography with visual field data to rapidly detect disease progression in glaucoma: a diagnostic accuracy study

“…The prediction accuracy of ANSWERS was also better than that of PoPLR, particularly in shorter series; 75% of VF series were better predicted by ANSWERS than by PoPLR and the average prediction error of ANSWERS was 15% lower than that of PoPLR. 82 Medeiros et al 83 proposed a Bayesian framework to integrate event-based (GPA) and trend-based (SLR of VFI) analyses. The results from the event-based analysis were incorporated into the prior for the trend-based analysis.…”

How to detect progression in glaucoma

“…For example, information on RNFLT change can be used to make better estimates of visual field change . Bayesian regression models have been used to integrate the results of HFA GPA (Medeiros et al, 2012a), ONH photography (Medeiros et al, 2012d), RNFL , or rim area (Medeiros et al, 2012e;Russell et al, 2012b), with visual field measurements achieving better estimates of rates of change. Another approach used to integrate results of different examinations is the development of combined indexes.…”