Evidence map›Paper›PMID 41314338›Full record

ArticleOphthalmology. Glaucoma

Time to Identify Glaucoma Progression Given Typical Visual Field Testing Frequencies in a US Nationwide Insured Population.

Brian C Stagg, Ben J Brintz, Joshua D Stein, Rachel Hess, Kensaku Kawamoto, Alessandro A Jammal, Felipe A Medeiros

Abstract readMulticenter Study
In one paragraph

Article in Ophthalmology. Glaucoma. 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

7 authors.

Brian C StaggDepartment of Ophthalmology and Visual Sciences, John Moran Eye Center, University of Utah, Salt Lake City, Utah; Department of Population Health Sciences, University of Utah, Salt Lake City, Utah. Electronic address: brian.stagg@hsc.utah.edu.
Ben J BrintzDivision of Epidemiology, Department of Internal Medicine, University of Utah, Salt Lake City, Utah.
Joshua D SteinDepartment of Ophthalmology and Visual Sciences, Center for Eye Policy & Innovation, Kellogg Eye Center, University of Michigan, Ann Arbor, Michigan; Institute for Healthcare Policy and Innovation, University of Michigan, Ann Arbor, Michigan; Department of Health Management and Policy, University of Michigan School of Public Health, Ann Arbor, Michigan.
Rachel HessDepartment of Internal Medicine, University of Utah, Salt Lake City, Utah.
Kensaku KawamotoDepartment of Biomedical Informatics, University of Utah, Salt Lake City, Utah.
Alessandro A JammalBascom Palmer Eye Institute, University of Miami Health System, Miami, Florida; Duke Eye Center, Duke University, Durham, North Carolina.
Felipe A MedeirosBascom Palmer Eye Institute, University of Miami Health System, Miami, Florida; Duke Eye Center, Duke University, Durham, North Carolina.

Funding

CTSA UM1 Program at University of UtahUM1TR004409 · NCATS · UTAH STATE HIGHER EDUCATION SYSTEM--UNIVERSITY OF UTAH · PI RACHEL HESS, Jennifer Juhl Majersik · 2023 to 2026
$21.9M
Personalized Forecasting of Disease Trajectory for Patients with Open Angle GlaucomaR01EY026641 · NEI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI LAVIERI-WILLIAMS, MARIEL SOFIA, STEIN, JOSHUA D · 2016 to 2020
$2.9M
Enhanced Identification of Ocular Phenotypes and Outcomes in Electronic Health Record DataR01EY032475 · NEI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI STEIN, JOSHUA D · 2022 to 2025
$2.7M
The BCI (Brain Computer Interface) Glaucoma Study: Objective Home-Based Detection of Progressive Visual Function Loss in GlaucomaR01EY029885 · NEI · UNIVERSITY OF MIAMI SCHOOL OF MEDICINE · PI MEDEIROS, FELIPE · 2019 to 2022
$2.2M
Health Evaluation and Access to Leverage Technology for Improved DR Outcomes (HEAL-DR)R01EY036341 · NEI · NORTHWESTERN UNIVERSITY AT CHICAGO · PI DUSTIN D. FRENCH, Joshua D Stein · 2024 to 2026
$2.1M
Health Disparities in Utilization, Quality, and Outcomes for Three Common Ocular Conditions (HealthDOC)R01EY034444 · NEI · NORTHWESTERN UNIVERSITY AT CHICAGO · PI DUSTIN D. FRENCH, Joshua D Stein · 2023 to 2026
$1.8M
Validation and Implementation of an Artificial Intelligence Machine-to-Machine (M2M) Model for Glaucoma ScreeningR01EY036593 · NEI · UNIVERSITY OF MIAMI SCHOOL OF MEDICINE · PI Felipe Medeiros · 2024 to 2026
$1.8M
Improving Glaucoma Care Using a Scalable Decision Support SystemK23EY032577 · NEI · UTAH STATE HIGHER EDUCATION SYSTEM--UNIVERSITY OF UTAH · PI Brian Stagg · 2022 to 2026
$1.3M
NCATS NIH HHS UM1 TR004409NEI NIH HHS K23 EY032577NEI NIH HHS R01 EY026641NEI NIH HHS R01 EY029885NEI NIH HHS R01 EY032475NEI NIH HHS R01 EY034444NEI NIH HHS R01 EY036341NEI NIH HHS R01 EY036593
6 · The paper itself

Abstract

purposeIn a study using US nationwide claims data from 2008 to 2017, the median time between visual field tests for patients with glaucoma was once every 1.59 years (interquartile range: 1.14-3.03 years). This is less frequent than the American Academy of Ophthalmology (AAO)-recommended minimum annual screening. We conducted simulation modeling using clinical data to determine how long it would take to identify trend-based glaucoma visual field progression at this testing frequency and compare it to AAO-recommended annual screening.

designSimulation modeling using retrospective clinical data. SUBJECTS: Patients with glaucoma in the Duke Glaucoma Registry (DGR).

methodsTo determine how long it would take to identify glaucoma progression at testing frequencies in this insured US nationwide population, we conducted simulation modeling using clinical visual field data from the DGR. Models were created to evaluate the time it would take to identify progression for patients with testing frequency at the 25th, 50th, and 75th percentiles in the previously published US nationwide claims data and at the AAO-recommended minimum annual screening. The simulations assumed baseline mean deviation (MD) of -5, -10, or -15 decibels (dB) and a true rate of progression of -0.5 or -1.0 dB/year. Progression was defined as having a negative MD slope estimate with a P value <0.05. We simulated 1000 sequences of visual fields for each test group.

main outcome measuresTime to detect glaucoma progression

resultsAcross the testing scenarios, the time to detect progression in 80% of eyes ranged from 15.2 to 24.3 years for patients receiving visual field testing at the 25th percentile (3.03 years between tests), 9.6 to 17.5 years for patients receiving visual field testing at the 50th percentile (1.59 years between tests), 8.0 to 14.9 years for patients receiving visual field testing at the 75th percentile (1.14 years between tests), and 7.1 to 14.1 years for patients receiving annual testing.

conclusionsThese results suggest that either visual field testing needs to be done more frequently or other methods of identification of progressive glaucoma vision loss need to be developed. Clinical decision support systems could help tailor the frequency of visual field testing to the needs of individual patients. FINANCIAL DISCLOSURE(S): Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Indexed as

GlaucomaIntraocular PressureRegistriesVisual FieldsVisual Field TestsAgedDisease ProgressionFemaleFollow-Up StudiesHumansMaleMiddle AgedRetrospective StudiesTime FactorsUnited StatesGlaucomaTesting frequencyVisual field

Identifiers

PMID41314338
PMCPMC12834117

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Registered trials

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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.