The Usefulness of Assessing Glaucoma Progression With Postprocessed Visual Field Data.

ABSTRACT:

Purpose

Data postprocessing with statistical techniques that are less sensitive to noise can be used to reduce variability in visual field (VF) series. We evaluated the detection of glaucoma progression with postprocessed VF data generated with the dynamic structure-function (DSF) model and MM-estimation robust regression (MRR).

Method

The study included 118 glaucoma eyes with at least 15 visits selected from the Rotterdam dataset. The DSF and MRR models were each applied to observed mean deviation (MD) values from the first three visits (V1-3) to predict the MD at V4. MD at V5 was predicted with data from V1-4 and so on until the MD at V9 was predicted, creating two additional datasets: DSF-predicted and MRR-predicted. Simple linear regression was performed to assess progre

SUBMITTER: Abu SL 

PROVIDER: S-EPMC9078054 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature