Development and validation of a visual field cluster in retinitis pigmentosa

Takashi Omoto,Ryo Asaoka,Akio Oishi,Yuri Fujino,Hiroshi Murata,Tatsuya Inoue,Keiko Azuma,Manabu Miyata,Ryo Obata

doi:10.1038/s41598-021-89233-0

Abstract

The aim was to establish and evaluate a new clustering method for visual field (VF) test points to predict future VF in retinitis pigmentosa. A Humphrey Field Analyzer 10-2 test was clustered using total deviation values from 858 VFs. We stratified 68 test points into 24 sectors. Then, mean absolute error (MAE) of the sector-wise regression with them (S1) was evaluated using 196 eyes with 10 VF sequences and compared to pointwise linear regression (PLR), mean sensitivity of total area (MS) and also another sector-wise regression basing on VF mapping for glaucoma (29 sectors; S2). MAE with S1 were smaller than with PLR when between the first-third and first-seventh VFs were used. MAE with the method were significantly smaller than those of S2 when between the first-sixth and first-ninth VFs were used. The MAE of MS was smaller than those with S1 only when first to 3rd and first to 4th VFs were used; however, the prediction accuracy became far larger than any other methods when larger number of VFs were used. More accurate prediction was achieved using this new sector-wise regression than with PLR. In addition, the obtained cluster was more useful than that for glaucoma to predict progression.

Highlights

The aim was to establish and evaluate a new clustering method for visual field (VF) test points to predict future VF in retinitis pigmentosa
We previously reported that this approach is advantageous when predicting future VFs with the Humphrey Field Analyzer (HFA) 24-2 test compared to pointwise linear regression (PLR) in glaucoma c ases[17,18]
The 68 test points in the HFA 10-2 test were clustered into 24 sectors (S1 mapping; Fig. 1A) using the Hierarchical Ordered Partitioning and Collapsing Hybrid (HOPACH)—Partitioning Around Medoids (PAM) algorithm[20] for the training data

Summary

Introduction

The aim was to establish and evaluate a new clustering method for visual field (VF) test points to predict future VF in retinitis pigmentosa. VF tests, especially in the central area, such as the Humphrey Field Analyzer (HFA; Carl Zeiss Meditec AG, Dublin, CA, USA) 10-2 tests often are u sed[3,4,5]. This measurement is important because it is essential to assess disease stage, and the results are associated closely with vision-related quality of life as confirmed in RP6. We established a new VF cluster for RP with the HFA 10-2 test, and investigated whether this approach was useful to improve prediction accuracy when predicting future VF

Methods

Results

Conclusion