Multicenter Normative Data for Mesopic Microperimetry.

Abstract

Purpose: The purpose of this study was 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 of healthy eyes from five independent study groups and one previously available dataset were included (1137 tests from 531 eyes of 432 participants [223 women and 209 men]). Linear mixed models (LMMs) were fitted to the data to obtain interpretable hill-of-vision models. A panel of regression models to predict normative data was 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: 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 decibel [dB]; 95% confidence interval [CI] = 1.9-2.36 dB) and miscalibration area (0.13; 95% CI = 0.07-0.19). However, a parsimonious linear model provided a comparable MAE (2.17 dB; 95% CI = 1.93-2.4 dB) and a similar miscalibration area (0.14; 95% CI = 0.08-0.2).

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.