Yes / Purpose: To demonstrate methods that enable visual field sensitivities to be compared with normative data without restriction to a fixed test pattern. Methods: Healthy participants (n = 60, age 19–50) undertook microperimetry (MAIA-2) using 237 spatially dense locations up to 13° eccentricity. Surfaces were fit to the mean, variance, and 5th percentile sensitivities. Goodness-of-fit was assessed by refitting the surfaces 1000 times to the dataset and comparing estimated and measured sensitivities at 50 randomly excluded locations. A leave-one-out method was used to compare individual data with the 5th percentile surface. We also considered cases with unknown fovea location by adding error sampled from the distribution of relative fovea–optic disc positions to the test locations and comparing shifted data to the fixed surface. Results: Root mean square (RMS) difference between estimated and measured sensitivities were less than 0.5 dB and less than 1.0 dB for the mean and 5th percentile surfaces, respectively. Root mean square differences were greater for the variance surface, median 1.4 dB, range 0.8 to 2.7 dB. Across all participants 3.9% (interquartile range, 1.8–8.9%) of sensitivities fell beneath the 5th percentile surface, close to the expected 5%. Positional error added to the test grid altered the number of locations falling beneath the 5th percentile surface by less than 1.3% in 95% of participants. Conclusions: Spatial interpolation of normative data enables comparison of sensitivity measurements from varied visual field locations. Conventional indices and probability maps familiar from standard automated perimetry can be produced. These methods may enhance the clinical use of microperimetry, especially in cases of nonfoveal fixation.

Yes / Purpose: To investigate how well visual field sensitivity predicts visual acuity at the same locations in macular disease, and to assess whether such predictions may be useful for selecting an optimum area for fixation training. Methods: Visual field sensitivity and acuity were measured at nine locations in the central 10° in 20 people with AMD and stable foveal fixation. A linear mixed model was constructed to predict acuity from sensitivity, taking into account within-subject effects and eccentricity. Cross validation was used to test the ability to predict acuity from sensitivity in a new patient. Simulations tested whether sensitivity can predict nonfoveal regions with greatest acuity in individual patients. Results: Visual field sensitivity (P < 0.0001), eccentricity (P = 0.007), and random effects of subject on eccentricity (P = 0.043) improved the model. For known subjects, 95% of acuity prediction errors (predicted − measured acuity) fell within −0.21 logMAR to +0.18 logMAR (median +0.00 logMAR). For unknown subjects, cross validation gave 95% of acuity prediction errors within −0.35 logMAR to +0.31 logMAR (median −0.01 logMAR). In simulations, the nonfoveal location with greatest predicted acuity had greatest “true” acuity on median 26% of occasions, and median difference in acuity between the location with greatest predicted acuity and the best possible location was +0.14 logMAR (range +0.04 to +0.17). Conclusions: The relationship between sensitivity and acuity in macular disease is not strongly predictive. The location with greatest sensitivity on microperimetry is unlikely to represent the location with the best visual acuity, even if eccentricity is taken into account. / College of Optometrists Postdoctoral Research Award (JD and ATA; London, UK) and National Institute for Health Research (NIHR) Postdoctoral Fellowship (ATA; London, UK). Presents independent research funded by the NIHR. / Research Development Fund Publication Prize Award winner, August 2018.