Evidence map›Paper›PMID 40956040›Full record

ArticleTranslational vision science & technology2025

A Bayesian Hierarchical Longitudinal Model for Estimation of Central Visual Field Rates of Change in Glaucoma.

Vahid Mohammadzadeh, Erica Su, Sajad Besharati, Abraham Liu, Simon K Law, Anne L Coleman, Joseph Caprioli, Robert E Weiss, Kouros Nouri-Mahdavi

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Translational vision science & technology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Vahid MohammadzadehGlaucoma Division, Stein Eye Institute, David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, CA, USA.
Erica SuGlaucoma Division, Stein Eye Institute, David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, CA, USA.
Sajad BesharatiGlaucoma Division, Stein Eye Institute, David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, CA, USA.
Abraham LiuDepartment of Biostatistics, Fielding School of Public Health, University of California Los Angeles, Los Angeles, CA, USA.
Simon K LawGlaucoma Division, Stein Eye Institute, David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, CA, USA.
Anne L ColemanGlaucoma Division, Stein Eye Institute, David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, CA, USA.
Joseph CaprioliGlaucoma Division, Stein Eye Institute, David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, CA, USA.
Robert E WeissDepartment of Biostatistics, Fielding School of Public Health, University of California Los Angeles, Los Angeles, CA, USA.
Kouros Nouri-MahdaviGlaucoma Division, Stein Eye Institute, David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, CA, USA.

Funding

Detection of Disease Progression in Advanced GlaucomaR01EY029792 · NEI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI NOURI-MAHDAVI, KOUROS · 2020 to 2024
$1.9M
NEI NIH HHS R01 EY029792
6 · The paper itself

Abstract

Purpose: Individual visual field (VF) sensitivities become unreliable at threshold sensitivities of 19 dB or less, limiting glaucoma monitoring. We evaluated longitudinal variability of central 10° VF measurements based on baseline sensitivity using a Bayesian hierarchical model. Methods: We included 124 glaucoma patients (124 eyes) with central or moderate-to-advanced VF damage, more than 2 years follow-up, and more than 4 central 10-2 VF tests. A Bayesian linear model estimated pointwise change rates, compared with simple linear regression (SLR). Simulations modeled average (-0.21 dB/year) and benchmark (-0.5 dB/year) slopes with residual standard deviations (SD) of 2, 4, 7, or 10 dB. Outcomes included pointwise residual SDs and proportions of significant slopes in cohort and simulations. Results: The average baseline 10-2 VF mean deviation, follow-up time, and median VF tests were 8.4 ± 5.4 dB, 4.6 ± 0.8 years, and 9 VF tests (range, 4-12 VF tests), respectively. The mean global slopes for Bayesian and SLR models were -0.21 and -0.36 dB/year. Residual SDs were markedly higher when baseline threshold sensitivities was 5 to 20 dB compared with 25 dB or greater. The Bayesian model identified more significant negative slopes, particularly at points with residual SD of less than 4 dB, relative to SLR. Conclusions: When baseline pointwise sensitivity is 5 to 20 dB, residual variability is very large, substantially reducing the ability to detect glaucoma progression. Translational Relevance: Visual field locations with sensitivity near or less than 20 dB demonstrate markedly greater variability over time; thus, excluding these points from visual field algorithms or analytical models could improve efficiency in detecting perimetric progression.

Indexed as

GlaucomaVisual FieldsAgedBayes TheoremDisease ProgressionFemaleHumansLongitudinal StudiesMaleMiddle AgedVisual Field Tests

Identifiers

PMID40956040
PMCPMC12448127

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.