Multicenter Normative Data for Mesopic Microperimetry.

IF 5 2区 医学 Q1 OPHTHALMOLOGY
Maximilian Pfau, Jasleen K Jolly, Jason Charng, Leon von der Emde, Philipp L Müller, Georg Ansari, Kristina Pfau, Fred K Chen, Zhichao Wu
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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.

中焦显微透视测量的多中心规范数据。
目的:本研究旨在为黄斑完整性评估(MAIA)微压计提供一个大型、多中心的标准数据集,并比较一组视丘模型的拟合优度和预测间隔校准误差:方法:纳入了来自五个独立研究小组的健康眼睛的微透视检查和一个以前可用的数据集(来自 432 名参与者[223 名女性和 209 名男性]的 531 只眼睛的 1137 次测试)。对数据进行了线性混合模型(LMM)拟合,以获得可解释的视丘模型。使用交叉验证和按部位分割的方法对预测常模数据的回归模型进行了比较。平均绝对误差(MAE)和误判面积(校准曲线与理想对角线之间的面积)作为性能指标进行评估:根据参数 "参与者年龄"、"距离眼窝的偏心率"、"与中心固定目标的重叠 "和 "沿四条主经线的偏心率",贝叶斯混合模型的平均绝对误差(MAE)(2.13 分贝[dB];95% 置信区间[CI] = 1.9-2.36 dB)和误判面积(0.13;95% 置信区间 = 0.07-0.19)最低。然而,解析线性模型提供了可比的 MAE(2.17 dB;95% CI = 1.93-2.4 dB)和相似的误判面积(0.14;95% CI = 0.08-0.2):包括年龄和偏心率在内的线性模型可以有效解释中视显微测距法视觉灵敏度的正常变化。该数据集和代码小节可用于估计视网膜大范围位置的正常值,适用于定制的测试模式。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
6.90
自引率
4.50%
发文量
339
审稿时长
1 months
期刊介绍: Investigative Ophthalmology & Visual Science (IOVS), published as ready online, is a peer-reviewed academic journal of the Association for Research in Vision and Ophthalmology (ARVO). IOVS features original research, mostly pertaining to clinical and laboratory ophthalmology and vision research in general.
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