Evidence map›Paper›PMID 31118184›Full record

ArticleThe British journal of ophthalmology2020

Compensation of retinal nerve fibre layer thickness as assessed using optical coherence tomography based on anatomical confounders.

Jacqueline Chua, Florian Schwarzhans, Duc Quang Nguyen, Yih Chung Tham, Josh Tjunrong Sia, Claire Lim, Shivani Mathijia, Carol Cheung, Aung Tin, Georg Fischer and 3 more

Open access · bronzeAbstract read
In one paragraph

Article in The British journal of ophthalmology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed
4.6field-weighted citation impact, top 5% 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

20 citing papers in PubMed, 36 citations in OpenAlex.

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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

13 authors at 6 institutions in 5 countries.

Jacqueline ChuaSingapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore.ORCID 0000-0002-6474-5293
Florian SchwarzhansSection for Medical Information Management and Imaging, Center for Medical Statistics, Informatics and Intelligent Systems, Medical University of Vienna, Vienna, Austria.
Duc Quang NguyenOcular Epidemiology Research Group, Singapore Eye Research Institute, Singapore, Singapore.
Yih Chung ThamSingapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore.
Josh Tjunrong SiaSingapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore.
Claire LimSingapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore.
Shivani MathijiaOcular Epidemiology Research Group, Singapore Eye Research Institute, Singapore, Singapore.
Carol CheungOphthalmology and Visual Sciences, The Chinese University of Hong Kong, Ma Liu Shui, Hong Kong.ORCID 0000-0002-9672-1819
Aung TinSingapore National Eye Centre, Singapore, Singapore.
Georg FischerSection for Medical Information Management and Imaging, Center for Medical Statistics, Informatics and Intelligent Systems, Medical University of Vienna, Vienna, Austria.
Ching-Yu ChengOcular Epidemiology Research Group, Singapore Eye Research Institute, Singapore, Singapore.ORCID 0000-0002-4916-6074
Clemens VassOphthalmology, Medical University of Vienna, Vienna, Austria.
Leopold SchmettererDepartment of Clinical Pharmacology, Medical University of Vienna, Vienna, Austria leopold.schmetterer@seri.com.sg.ORCID 0000-0002-7189-1707
Singapore National Eye Center · SGSingapore Eye Research Institute · SGStatistics Austria · ATChinese University of Hong Kong · HKMedical University of Vienna · ATOphthalmology Associates (United States) · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

BACKGROUND/

aimsTo compensate the retinal nerve fibre layer (RNFL) thickness assessed by spectral-domain optical coherence tomography (SD-OCT) for anatomical confounders.

methodsThe Singapore Epidemiology of Eye Diseases is a population-based study, where 2698 eyes (1076 Chinese, 704 Malays and 918 Indians) with high-quality SD-OCT images from individuals without eye diseases were identified. Optic disc and macular cube scans were registered to determine the distance between fovea and optic disc centres (fovea distance) and their respective angle (fovea angle). Retinal vessels were segmented in the projection images and used to calculate the circumpapillary retinal vessel density profile. Compensated RNFL thickness was generated based on optic disc (ratio, orientation and area), fovea (distance and angle), retinal vessel density, refractive error and age. Linear regression models were used to investigate the effects of clinical factors on RNFL thickness.

resultsRetinal vessel density reduced significantly with increasing age (1487±214 µm in 40-49, 1458±208 µm in 50-59, 1429±223 µm in 60-69 and 1415±233 µm in ≥70). Compensation reduced the variability of RNFL thickness, where the effect was greatest for Chinese (10.9%; p<0.001), followed by Malays (6.6%; p=0.075) and then Indians (4.3%; p=0.192). Compensation reduced the age-related RNFL decline by 55% in all participants (β=-3.32 µm vs β=-1.50 µm/10 years; p<0.001). Nearly 62% of the individuals who were initially classified as having abnormally thin RNFL (outside the 99% normal limits) were later reclassified as having normal RNFL.

conclusionsRNFL thickness compensated for anatomical parameters reduced the variability of measurements and may improve glaucoma detection, which needs to be confirmed in future studies.

Indexed as

AdultAgedAged, 80 and overAge FactorsAgingEthnicityFemaleHumansMaleMiddle AgedNerve FibersRegression AnalysisRetinal Ganglion CellsRetinal VesselsTomography, Optical Coherenceimaging

Identifiers

PMID31118184
PMCPMC7025730
OpenAlexW2945662552

What OpenQuestion holds

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LicenceCC BY-NC
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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.