Predictive modeling of rapid glaucoma progression based on systemic data from electronic medical records.

Richul Oh, Hyunjoong Kim, Tae-Woo Kim, Eun Ji Lee

Abstract read

In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed

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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

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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

4 authors.

Richul OhDepartment of Ophthalmology, Seoul National University College of Medicine, Seoul National University Bundang Hospital, 82, Gumi-ro, 173 Beon-gil, Bundang-gu, Seongnam, Gyeonggi-do, 13620, Republic of Korea.

Hyunjoong KimDepartment of Applied Statistics, Yonsei University, Seoul, Republic of Korea.

Tae-Woo KimDepartment of Ophthalmology, Seoul National University College of Medicine, Seoul National University Bundang Hospital, 82, Gumi-ro, 173 Beon-gil, Bundang-gu, Seongnam, Gyeonggi-do, 13620, Republic of Korea.

Eun Ji LeeDepartment of Ophthalmology, Seoul National University College of Medicine, Seoul National University Bundang Hospital, 82, Gumi-ro, 173 Beon-gil, Bundang-gu, Seongnam, Gyeonggi-do, 13620, Republic of Korea. opticdisc@snubh.org.

Funding

Ministry of Education, Science and Technology 2016R1D1A1B02011696Ministry of Health and Welfare HC19C0276Ministry of Health and Welfare HI19C0481Seoul National University Bundang Hospital 14-2022-0024

6 · The paper itself

Abstract

This study investigated the baseline systemic features that predict rapid thinning of the retinal nerve fiber layer (RNFL) in patients with primary open-angle glaucoma (POAG). A database drawn from electronic medical records (EMRs) was searched for patients diagnosed with POAG between 2009 and 2016 who had been followed up for > 5 years with the annual evaluation of global RNFL thickness using spectral-domain optical coherence tomography. The rate of change in global RNFL thickness for each eye was determined by linear regression analysis over time. Systemic data obtained within 6 months from the time of glaucoma diagnosis were extracted from the EMRs and incorporated into a model to predict the rate of progressive RNFL thinning. The predictive model was trained and tested using a random forest (RF) method and interpreted using Shapley additive explanation plots (SHAP). The features able to explain the rate of progressive RNFL thinning were identified and interpreted. Data from 1256 eyes of 696 patients and 1107 eyes of 607 patients were included in the training and test sets, respectively. The R

Indexed as

Electronic Health RecordsGlaucomaGlaucoma, Open-AngleAgedDisease ProgressionFemaleHumansIntraocular PressureMaleMiddle AgedNerve FibersTomography, Optical CoherenceElectronic medical recordGlaucoma progressionMachine learningSystemic risk factor

Identifiers

PMID40240525

DOI10.1038/s41598-025-97344-1

PMCPMC12003650

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LicenceCC BY-NC-ND

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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.