Trial reportOphthalmology2018
Personalized Prediction of Glaucoma Progression Under Different Target Intraocular Pressure Levels Using Filtered Forecasting Methods.
Trial report in Ophthalmology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
What it found
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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
27 citing papers in PubMed.
- Viscotrabeculotomy versus trabeculectomy in the surgical treatment of open angle glaucoma: a single center, randomised controlled trial.Japanese journal of ophthalmology · 2021Trial
- Unsupervised Clustering for POAG Phenotyping.Investigative ophthalmology & visual science · 2026Article
- Management and outcomes of primary open-angle glaucoma.Journal of family medicine and primary care · 2026Review
- Review
- Preoperative predictive thresholds for successful canaloplasty in glaucoma patients.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2025Article
- AI-Based Ocular Age Estimation from Combined OCT and OCTA Metrics: Decade-Stratified Normative Modelling in Healthy Eyes - a Pilot Study.Clinical ophthalmology (Auckland, N.Z.) · 2025Article
- Democratizing Glaucoma Care: A Framework for AI-Driven Progression Prediction Across Diverse Healthcare Settings.Journal of ophthalmology · 2025Article
- Comparison of clinical outcomes between gonioscopy-assisted transluminal trabeculotomy and trabeculectomy in advanced-stage pseudoexfoliation glaucoma.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2024Article
- Artificial intelligence in the anterior segment of eye diseases.International journal of ophthalmology · 2024Review
- A machine learning framework for the quantification of experimental uveitis in murine OCT.Biomedical optics express · 2023Article
- Developing and Validating Models to Predict Progression to Proliferative Diabetic Retinopathy.Ophthalmology science · 2023Article
- Visual Field Prediction: Evaluating the Clinical Relevance of Deep Learning Models.Ophthalmology science · 2023Article
- Precision Medicine in Glaucoma: Artificial Intelligence, Biomarkers, Genetics and Redox State.International journal of molecular sciences · 2023Review
- Artificial intelligence in ophthalmology - Machines think!Indian journal of ophthalmology · 2022Article
- Artificial intelligence applied to ophthalmology and optometry: A citation network analysis.Journal of optometry · 2022Article
- Assessing Glaucoma Progression Using Machine Learning Trained on Longitudinal Visual Field and Clinical Data.Ophthalmology · 2021Article
- The Impact of Artificial Intelligence and Deep Learning in Eye Diseases: A Review.Frontiers in medicine · 2021Review
- The Relationship Between Intraocular Pressure and Rates of Central Versus Peripheral Visual Field Progression.Journal of glaucoma · 2020Observational
- Rare protein-altering variants in ANGPTL7 lower intraocular pressure and protect against glaucoma.PLoS genetics · 2020Article
- The impact of artificial intelligence in the diagnosis and management of glaucoma.Eye (London, England) · 2020Review
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
purposeTo generate personalized forecasts of how patients with open-angle glaucoma (OAG) experience disease progression at different intraocular pressure (IOP) levels to aid clinicians with setting personalized target IOPs.
designSecondary analyses using longitudinal data from 2 randomized controlled trials.
participantsParticipants with moderate or advanced OAG from the Collaborative Initial Glaucoma Treatment Study (CIGTS) or the Advanced Glaucoma Intervention Study (AGIS).
methodsBy using perimetric and tonometric data from trial participants, we developed and validated Kalman Filter (KF) models for fast-, slow-, and nonprogressing patients with OAG. The KF can generate personalized and dynamically updated forecasts of OAG progression under different target IOP levels. For each participant, we determined how mean deviation (MD) would change if the patient maintains his/her IOP at 1 of 7 levels (6, 9, 12, 15, 18, 21, or 24 mmHg) over the next 5 years. We also model and predict changes to MD over the same time horizon if IOP is increased or decreased by 3, 6, and 9 mmHg from the level attained in the trials.
main outcome measuresPersonalized estimates of the change in MD under different target IOP levels.
resultsA total of 571 participants (mean age, 64.2 years; standard deviation, 10.9) were followed for a mean of 6.5 years (standard deviation, 2.8). Our models predicted that, on average, fast progressors would lose 2.1, 6.7, and 11.2 decibels (dB) MD under target IOPs of 6, 15, and 24 mmHg, respectively, over 5 years. In contrast, on average, slow progressors would lose 0.8, 2.1, and 4.1 dB MD under the same target IOPs and time frame. When using our tool to quantify the OAG progression dynamics for all 571 patients, we found no statistically significant differences over 5 years between progression for black versus white, male versus female, and CIGTS versus AGIS participants under different target IOPs (P > 0.05 for all).
conclusionsTo our knowledge, this is the first clinical decision-making tool that generates personalized forecasts of the trajectory of OAG progression at different target IOP levels. This approach can help clinicians determine appropriate, personalized target IOPs for patients with OAG.
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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.