Evidence map›Paper›PMID 35904793›Full record

ArticleTranslational vision science & technology2022

Central Macular Topographic and Volumetric Measures: New Biomarkers for Detection of Glaucoma.

Vahid Mohammadzadeh, Melodyanne Cheng, Sepideh Heydar Zadeh, Kiumars Edalati, Dariush Yalzadeh, Joseph Caprioli, Sunil Yadav, Ella M Kadas, Alexander U Brandt, Kouros Nouri-Mahdavi

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Translational vision science & technology, 2022. 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
0.4field-weighted citation impact, top 42% of its field
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, 2 citations in OpenAlex.

  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

10 authors at 4 institutions in 2 countries.

Vahid MohammadzadehGlaucoma Division, Stein Eye Institute, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA.
Melodyanne ChengGlaucoma Division, Stein Eye Institute, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA.
Sepideh Heydar ZadehGlaucoma Division, Stein Eye Institute, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA.
Kiumars EdalatiGlaucoma Division, Stein Eye Institute, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA.
Dariush YalzadehGlaucoma Division, Stein Eye Institute, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA.
Joseph CaprioliGlaucoma Division, Stein Eye Institute, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA.
Sunil YadavExperimental and Clinical Research Center, Max Delbruck Center for Molecular Medicine, Berlin, Germany.
Ella M KadasExperimental and Clinical Research Center, Max Delbruck Center for Molecular Medicine, Berlin, Germany.
Alexander U BrandtDepartment of Neurology, University of California Irvine, CA, USA.
Kouros Nouri-MahdaviGlaucoma Division, Stein Eye Institute, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA.
University of California, Los Angeles · USFreie Universität Berlin · DEMax Delbrück Center · DEUniversity of California, Irvine · US

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: To test the hypothesis that newly developed shape measures using optical coherence tomography (OCT) macular volume scans can discriminate patients with perimetric glaucoma from healthy subjects. Methods: OCT structural measures defining macular topography and volume were recently developed based on cubic Bézier curves. We exported macular volume scans from 135 eyes with glaucoma (133 patients) and 155 healthy eyes (85 subjects) and estimated global and quadrant-based measures. The best subset of measures to predict glaucoma was explored with a gradient boost model (GBM) with subsequent logistic regression. Accuracy and area under receiver operating curves (AUC) were the primary metrics. In addition, we separately investigated model performance in 66 eyes with mild glaucoma (mean deviation ≥ -6 dB). Results: Average (±SD) 24-2 mean deviation was -8.2 (±6.1) dB in eyes with glaucoma. The main predictive measures for glaucoma were temporal inferior rim height, nasal inferior pit volume, and temporal inferior pit depth. Lower values for these measures predicted higher risk of glaucoma. Sensitivity, specificity, and AUC for discriminating between healthy and glaucoma eyes were 81.5% (95% CI = 76.6-91.9%), 89.7% (95% CI = 78.7-94.2%), and 0.915 (95% CI = 0.882-0.948), respectively. Corresponding metrics for mild glaucoma were 84.8% (95% CI = 72.1%-95.5%), 85.8% (95% CI = 87.1%-97.4%), and 0.913 (95% CI = 0.867-0.958), respectively. Conclusions: Novel macular shape biomarkers detect early glaucoma with clinically relevant performance. Such biomarkers do not depend on intraretinal segmentation accuracy and may be helpful in eyes with suboptimal macular segmentation. Translational Relevance: Macular shape biomarkers provide valuable information for detection of early glaucoma and may provide additional information beyond thickness measurements.

Indexed as

GlaucomaNerve FibersBiomarkersHumansRetinal Ganglion CellsROC CurveTomography, Optical CoherenceBiomarkers

Identifiers

PMID35904793
PMCPMC9344219
OpenAlexW4288708021

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.