Sampling the Visual Field Based on Individual Retinal Nerve Fiber Layer Thickness Profile.

Purpose Current perimeters use fixed grid patterns. We test whether a grid based on an individual's retinal nerve fiber layer (RNFL) thickness profile would find more visual field (VF) defects. Methods We describe the defect-based method for choosing test locations. First, the 26 VF locations with the highest positive predictive value to detect glaucoma from the 24-2 pattern are chosen. An additional 26 locations are chosen from a 2 × 2 degree grid based on RNFL thickness. An individualized map was used to relate VF locations to peripapillary RNFL thickness. To test whether the 52 locations chosen by the defect-based method find more defects than other test grids, we collected a 386-location (2 × 2 degree grid) VF measurement on 23 glaucoma participants and classed each location in the dataset as either abnormal or normal using a suprathreshold test. Using this data, defect-based sampling was compared to: a method that sampled VF locations uniformly around the optic nerve head (ONH); the 24-2 pattern; a polar pattern; and a reduced polar pattern. The outcome measure was the number of abnormal points that were selected as test locations. Results For 8 eyes, no method found more abnormal points than would be expected by chance (hypergeometric distribution, P < 0.05). Of the remaining 15 eyes, the defect-based method identified more abnormal locations on nine eyes, which was significantly better than the other three sampling schemes (24-2: 2 eyes, P < 0.001; polar: 2 eyes, P < 0.001; reduced polar: 2 eyes, P < 0.004; and uniform: 1 eye, P < 0.001). Conclusions Using structural information to choose locations to test in a VF for individual patients identifies more abnormal locations than using existing grid patterns and uniform sampling based on structure.