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Multicenter Normative Data for Mesopic Microperimetry
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DOI：10.1167/iovs.65.12.27.png)
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Abstract
En 中文
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.
Keywords:
microperimetry
static perimetry
visual psychophisics
mesopic light sensitiv- ity
fundus-controlled perimetry
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