Evidence map›Paper›PMID 37808089›Full record

ArticleArXiv2023

Identifying factors associated with fast visual field progression in patients with ocular hypertension based on unsupervised machine learning.

Xiaoqin Huang, Asma Poursoroush, Jian Sun, Michael V Boland, Chris Johnson, Siamak Yousefi

Open access · greenAbstract readPreprint
In one paragraph

Article in ArXiv, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed, 0 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

6 authors at 4 institutions in 1 country.

Xiaoqin HuangDepartment of Ophthalmology, University of Tennessee Health Science Center, Memphis, USA.
Asma PoursoroushDepartment of Ophthalmology, University of Tennessee Health Science Center, Memphis, USA.
Jian SunIntegrated Data Sciences Section, Research Technologies Branch, National Institute of Allergy and Infectious Diseases, National Institutes of Health (NIH), Bethesda, USA.
Michael V BolandDepartment of Ophthalmology, Massachusetts Eye and Ear, Boston, USA.
Chris JohnsonDepartment of Ophthalmology and Visual Sciences, University of Iowa Hospitals and Clinics, Iowa City, IA.
Siamak YousefiDepartment of Ophthalmology, University of Tennessee Health Science Center, Memphis, USA.
University of Tennessee Health Science Center · USMassachusetts Eye and Ear Infirmary · USNational Institute of Allergy and Infectious Diseases · USUniversity of Iowa Hospitals and Clinics · US

Funding

Predicting the risk of glaucoma from structural, functional, and genetic factors using artificial intelligenceR01EY033005 · NEI · UNIVERSITY OF TENNESSEE HEALTH SCI CTR · PI YOUSEFI, SIAMAK · 2022 to 2025
$2.4M
Improved Glaucoma Monitoring Using Artificial-Intelligence Enabled DashboardR21EY031725 · NEI · UNIVERSITY OF TENNESSEE HEALTH SCI CTR · PI YOUSEFI, SIAMAK · 2020 to 2022
$545k
NEI NIH HHS R01 EY033005NEI NIH HHS R21 EY031725
6 · The paper itself

Abstract

Purpose: To identify ocular hypertension (OHT) subtypes with different trends of visual field (VF) progression based on unsupervised machine learning and to discover factors associated with fast VF progression. Design: Cross-sectional and longitudinal study. Participants: A total of 3133 eyes of 1568 ocular hypertension treatment study (OHTS) participants with at least five follow-up VF tests were included in the study. Methods: We used a latent class mixed model (LCMM) to identify OHT subtypes using standard automated perimetry (SAP) mean deviation (MD) trajectories. We characterized the subtypes based on demographic, clinical, ocular, and VF factors at the baseline. We then identified factors driving fast VF progression using generalized estimating equation (GEE) and justified findings qualitatively and quantitatively. Main Outcome Measure: Rates of SAP mean deviation (MD) change. Results: The LCMM model discovered four clusters (subtypes) of eyes with different trajectories of MD worsening. The number of eyes in clusters were 794 (25%), 1675 (54%), 531 (17%) and 133 (4%). We labeled the clusters as Improvers, Stables, Slow progressors, and Fast progressors based on their mean of MD decline, which were 0.08, -0.06, -0.21, and -0.45 dB/year, respectively. Eyes with fast VF progression had higher baseline age, intraocular pressure (IOP), pattern standard deviation (PSD) and refractive error (RE), but lower central corneal thickness (CCT). Fast progression was associated with calcium channel blockers, being male, heart disease history, diabetes history, African American race, stroke history, and migraine headaches. Conclusion: Unsupervised clustering can objectively identify OHT subtypes including those with fast VF worsening without human expert intervention. Fast VF progression was associated with higher history of stroke, heart disease, diabetes, and history of more using calcium channel blockers. Fast progressors were more from African American race and males and had higher incidence of glaucoma conversion. Subtyping can provide guidance for adjusting treatment plans to slow vision loss and improve quality of life of patients with a faster progression course.

Identifiers

PMID37808089
PMCPMC10557795
OpenAlexW4387209721

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-SA
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.