Evidence map›Paper›PMID 32866692›Full record

ArticleOphthalmology. Glaucoma

Local Macular Thickness Relationships between 2 OCT Devices.

Golnoush Mahmoudinezhad, Vahid Mohammadzadeh, Navid Amini, Veronica Toriz, Mohammad Pourhomayoun, Sepideh Heydarzadeh, Apoorva Mylavarapu, Esteban Morales, Joseph Caprioli, Kouros Nouri-Mahdavi

Abstract read
PubMed Full text at PMC Publisher
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 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 citing papers in PubMed.

  1. Article
  2. Article
  3. Risk factors for microcystic macular oedema in glaucoma.The British journal of ophthalmology · 2023
    Observational
  4. Observational
  5. Article
  6. Macular Optical Coherence Tomography Imaging in Glaucoma.Journal of ophthalmic & vision research
    Article
  7. Article
  8. 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.

Golnoush MahmoudinezhadGlaucoma Division, Stein Eye Institute, David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, California.
Vahid MohammadzadehGlaucoma Division, Stein Eye Institute, David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, California.
Navid AminiDepartment of Computer Science, California State University, Los Angeles, California.
Veronica TorizDepartment of Computer Science, California State University, Los Angeles, California.
Mohammad PourhomayounDepartment of Computer Science, California State University, Los Angeles, California.
Sepideh HeydarzadehGlaucoma Division, Stein Eye Institute, David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, California.
Apoorva MylavarapuGlaucoma Division, Stein Eye Institute, David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, California.
Esteban MoralesGlaucoma Division, Stein Eye Institute, David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, California.
Joseph CaprioliGlaucoma Division, Stein Eye Institute, David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, California.
Kouros Nouri-MahdaviGlaucoma Division, Stein Eye Institute, David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, California. Electronic address: nouri-mahdavi@jsei.ucla.edu.

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

purposeTo compare local ganglion cell-inner plexiform layer (GCIPL) thickness measurements between 2 OCT devices and to explore factors that may influence the difference in measurements.

designCross-sectional study.

participantsSixty-nine glaucoma eyes (63 patients) with evidence of central damage or mean deviation (MD) of -6.0 dB or worse on a 24-2 visual field (VF).

methodsCirrus and Spectralis OCT macular volume scans were exported, data from the central 20° of both OCT devices were centered and aligned, and 50 × 50 arrays of 0.4° × 0.4° superpixels were created. We estimated nonparametric (Spearman's) correlations and used Bland-Altman plots to compare GCIPL thickness measurements between the two OCTs at the superpixel level. Factors that may have influenced the differences between thickness measurements between the two devices were explored with linear mixed models.

main outcome measuresPooled and individual-eye Spearman's correlation and agreement between thickness measurements from the two devices.

resultsThe median 24-2 VF MD was -6.8 dB (interquartile range [IQR], -4.9 to -12.3 dB). The overall pooled Spearman's correlation between the two devices for all superpixels and eyes was 0.97 (P < 0.001). The median within-eye correlation coefficient was 0.72 (IQR, 0.59-0.79). Bland-Altman plots demonstrated a systematic bias in most individual eyes, with Spectralis GCIPL measurements becoming larger than Cirrus measurements with increasing superpixel thickness. The average superpixel thickness and distance to the fovea influenced the thickness difference between the two devices in multivariate models (P < 0.001).

conclusionsLocal macular thickness measurements from the Spectralis and Cirrus devices are highly correlated, but not interchangeable. Differences in thickness measurements between the two devices are influenced by the location of superpixels and their thickness.

Indexed as

Nerve FibersTomography, Optical CoherenceCross-Sectional StudiesHumansIntraocular PressureRetinal Ganglion CellsGanglion cell–inner plexiform layer thicknessMaculaOCTSuperpixels

Identifiers

PMID32866692
PMCPMC7914265

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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.