Multicenter normative data for mesopic microperimetry

Purpose: To provide a large, multi-center normative dataset for the Macular Integrity Assessment (MAIA) microperimeter and compare the goodness-of-fit and prediction interval calibration-error for a panel of hill-of-vision models. Methods: Microperimetry examinations from five independent study groups and one previously available dataset were included. Linear mixed models (LMMs) were fitted to the data to obtain interpretable hill-of-vision models. For predicting age-adjusted normative values, an array of regression models were compared using cross-validation with site-wise splits. The mean absolute error (MAE) and miscalibration area (area between the calibration curve and the ideal diagonal) were evaluated as the performance measures. Results: 1,052 tests from 531 eyes of 432 participants were included. Based on the parameters 'participant age', 'eccentricity from the fovea', 'overlap with the central fixation target' and 'eccentricity along the four principal meridians', a Bayesian mixed model had the lowest MAE (2.13 dB; 95% confidence interval [CI] = 1.86, 2.40 dB) and miscalibration area (0.14; 95% CI = 0.07, 0.20). However, a parsimonious linear model provided a comparable MAE (2.16 dB; 95% CI = 1.89, 2.43 dB) and a similar miscalibration area (0.14; 95% CI = 0.08, 0.20). Conclusions: Normal variations in visual sensitivity on mesopic microperimetry can be effectively explained by a linear model that includes age and eccentricity. The dataset and a code vignette are provided for estimating normative values across a large range of retinal locations, applicable to customized testing patterns.