ArticleJAMA ophthalmology2020
Evaluation of a Primary Open-Angle Glaucoma Prediction Model Using Long-term Intraocular Pressure Variability Data: A Secondary Analysis of 2 Randomized Clinical Trials.
Article in JAMA ophthalmology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 10 papers.
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.
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.
Who cites it
10 citing papers in PubMed, 19 citations in OpenAlex.
- Risk Calculation in the Medication Arm of the Ocular Hypertension Treatment Study.Ophthalmology. GlaucomaTrial
- Interpretable longitudinal glaucoma visual field estimation deep learning system from fundus images and clinical narratives.NPJ digital medicine · 2025Article
- The relationship between intraocular pressure and glaucoma: An evolving concept.Progress in retinal and eye research · 2024Review
- Prediction of visual field progression with serial optic disc photographs using deep learning.The British journal of ophthalmology · 2024Article
- Deep learning models to predict primary open-angle glaucoma.Stat (International Statistical Institute) · 2024Article
- GRAPE: A multi-modal dataset of longitudinal follow-up visual field and fundus images for glaucoma management.Scientific data · 2023Article
- Comparing statistical methods in assessing the prognostic effect of biomarker variability on time-to-event clinical outcomes.BMC medical research methodology · 2022Article
- A deep-learning system predicts glaucoma incidence and progression using retinal photographs.The Journal of clinical investigation · 2022Article
- Incorrect Spelling in Author Byline.JAMA ophthalmology · 2020Article
- The impact of intraocular pressure fluctuations on the progression of glaucoma and associated factors.Advances in ophthalmology practice and researchReview
Corrections and comments
- Erratum issued
Authors and funding
9 authors at 3 institutions in 2 countries.
Funding
Abstract
Importance: The contribution of long-term intraocular pressure (IOP) variability to the development of primary open-angle glaucoma is still controversial. Objective: To assess whether long-term IOP variability data improve a prediction model for the development of primary open-angle glaucoma (POAG) in individuals with untreated ocular hypertension. Design, Setting, and Participants: This post hoc secondary analysis of 2 randomized clinical trials included data from 709 of 819 participants in the observation group of the Ocular Hypertension Treatment Study (OHTS) followed up from February 28, 1994, to June 1, 2002, and 397 of 500 participants in the placebo group of the European Glaucoma Prevention Study (EGPS) followed up from January 1, 1997, to September 30, 2003. Data analyses were completed between January 1, 2019, and March 15, 2020. Exposures: The original prediction model for the development of POAG included the following baseline factors: age, IOP, central corneal thickness, vertical cup-disc ratio, and pattern SD. This analysis tested whether substitution of baseline IOP with mean follow-up IOP, SD of IOP, maximum IOP, range of IOP, or coefficient of variation IOP would improve predictive accuracy. Main Outcomes and Measures: The C statistic was used to compare the predictive accuracy of multivariable landmark Cox proportional hazards regression models for the development of POAG. Results: Data from the OHTS consisted of 97 POAG end points from 709 of 819 participants (416 [58.7%] women; 177 [25.0%] African American and 490 [69.1%] white; mean [SD] age, 55.7 [9.59] years; median [range] follow-up, 6.9 [0.96-8.15] years). Data from the EGPS consisted of 44 POAG end points from 397 of 500 participants in the placebo group (201 [50.1%] women; 397 [100%] white; mean [SD] age, 57.8 [9.76] years; median [range] follow-up, 4.9 [1.45-5.76] years). The C statistic for the original prediction model was 0.741. When a measure of follow-up IOP was substituted for baseline IOP in this prediction model, the C statistics were as follows: mean follow-up IOP, 0.784; maximum IOP, 0.781; SD of IOP, 0.745; range of IOP, 0.741; and coefficient of variation IOP, 0.729. The C statistics in the EGPS were similarly ordered. No measure of IOP variability, when added to the prediction model that included mean follow-up IOP, age, central corneal thickness, vertical cup-disc ratio, and pattern SD, increased the C statistic by more than 0.007 in either cohort. Conclusions and Relevance: Evidence from the OHTS and the EGPS suggests that long-term variability does not add substantial explanatory power to the prediction model as to which individuals with untreated ocular hypertension will develop POAG.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.