Functional generalized estimating equation model to detect glaucomatous visual field progression

Abstract:

AIM: To build a functional generalized estimating equation (GEE) model to detect glaucomatous visual field progression and compare the performance of the proposed method with that of commonly employed algorithms. METHODS: Totally 716 eyes of 716 patients with primary open angle glaucoma (POAG) with at least 5 reliable 24-2 test results and 2y of follow-up were selected. The functional GEE model was used to detect perimetric progression in the training dataset (501 eyes). In the testing dataset (215 eyes), progression was evaluated the functional GEE model, mean deviation (MD) and visual field index (VFI) rates of change, Advanced Glaucoma Intervention Study (AGIS) and Collaborative Initial Glaucoma Treatment Study (CIGTS) scores, and pointwise linear regression (PLR). RESULTS: The proposed method showed the highest proportion of eyes detected as progression (54.4%), followed by the VFI rate (34.4%), PLR (23.3%), and MD rate (21.4%). The CIGTS and AGIS scores had a lower proportion of eyes detected as progression (7.9% and 5.1%, respectively). The time to detection of progression was significantly shorter for the proposed method than that of other algorithms (adjusted P≤0.019). The VFI rate displayed moderate pairwise agreement with the proposed method (k=0.47). CONCLUSION: The functional GEE model shows the highest proportion of eyes detected as perimetric progression and the shortest time to detect perimetric progression in patients with POAG.