Personalized Prediction of Glaucoma Progression Under Different Target Intraocular Pressure Levels Using Filtered Forecasting Methods

Kazemian, Pooyan; Lavieri, Mariel S.; Oyen, Mark P. Van; Andrews, Chris; Stein, Joshua D.

doi:10.1016/j.ophtha.2017.10.033

Cited by 51 publications

(35 citation statements)

References 24 publications

(21 reference statements)

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“…Although intraocular pressure (IOP)-lowering has been shown to be therapeutically effective in delaying glaucoma progression, some demonstrated that disease progression is still inevitable,54–56 suggesting that we have not arrived at optimised treatment regimens for the various forms of glaucoma. Kazemian et al 57 developed a clinical forecasting tool that uses tonometric and VF data to project disease trajectories at different target IOPs. Further refinement of this tool that integrates other ophthalmic and non-ophthalmic data would be useful to establish target IOPs and the best strategies to achieve them on a case-by-case basis.…”

Section: Applications In Ophthalmologymentioning

confidence: 99%

Artificial intelligence and deep learning in ophthalmology

et al. 2018

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Artificial intelligence (AI) based on deep learning (DL) has sparked tremendous global interest in recent years. DL has been widely adopted in image recognition, speech recognition and natural language processing, but is only beginning to impact on healthcare. In ophthalmology, DL has been applied to fundus photographs, optical coherence tomography and visual fields, achieving robust classification performance in the detection of diabetic retinopathy and retinopathy of prematurity, the glaucoma-like disc, macular oedema and age-related macular degeneration. DL in ocular imaging may be used in conjunction with telemedicine as a possible solution to screen, diagnose and monitor major eye diseases for patients in primary care and community settings. Nonetheless, there are also potential challenges with DL application in ophthalmology, including clinical and technical challenges, explainability of the algorithm results, medicolegal issues, and physician and patient acceptance of the AI ‘black-box’ algorithms. DL could potentially revolutionise how ophthalmology is practised in the future. This review provides a summary of the state-of-the-art DL systems described for ophthalmic applications, potential challenges in clinical deployment and the path forward.

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Section: Applications In Ophthalmologymentioning

confidence: 99%

Artificial intelligence and deep learning in ophthalmology

et al. 2018

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“…1. self-reported white British ancestry ("in_white_British_ancestry_subset" column) 2. used to compute principal components ("used_in_pca_calculation" column) 3. not marked as outliers for heterozygosity and missing rates ("het_missing_outliers" column)…”

Section: Genome-wide Association Analysis In Uk Biobankmentioning

confidence: 99%

“…Intraocular pressure (IOP) is a modifiable risk factor and predictive measure for glaucoma [1][2][3][4] (S1 Fig). Genome-wide association studies (GWAS) have commonly used this endophenotype that exhibits high genetic correlation (rg = 0.71) to glaucoma, as an approach to prioritize genetic variants likely to contribute to disease risk [5].…”

Section: Introductionmentioning

confidence: 99%

Rare protein-altering variants in ANGPTL7 lower intraocular pressure and protect against glaucoma

et al. 2020

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Protein-altering variants that are protective against human disease provide in vivo validation of therapeutic targets. Here we use genotyping data from UK Biobank (n = 337,151 unrelated White British individuals) and FinnGen (n = 176,899) to conduct a search for protein-altering variants conferring lower intraocular pressure (IOP) and protection against glaucoma. Through rare protein-altering variant association analysis, we find a missense variant in ANGPTL7 in UK Biobank (rs28991009, p.Gln175His, MAF = 0.8%, genotyped in 82,253 individuals with measured IOP and an independent set of 4,238 glaucoma patients and 250,660 controls) that significantly lowers IOP (β =-0.53 and-0.67 mmHg for heterozygotes,-3.40 and-2.37 mmHg for homozygotes, P = 5.96 x 10 −9 and 1.07 x 10 −13 for corneal compensated and Goldman-correlated IOP, respectively) and is associated with 34% reduced risk of glaucoma (P = 0.0062). In FinnGen, we identify an ANGPTL7 missense variant at a greater than 50-fold increased frequency in Finland compared with other populations (rs147660927, p.Arg220Cys, MAF Finland = 4.3%), which was genotyped in 6,537 glaucoma patients and 170,362 controls and is associated with a 29% lower glaucoma risk (P = 1.9 x 10 −12 for all glaucoma types and also protection against its subtypes including exfoliation, primary openangle, and primary angle-closure). We further find three rarer variants in UK Biobank, including a protein-truncating variant, which confer a strong composite lowering of IOP (P = 0.0012 and 0.24 for Goldman-correlated and corneal compensated IOP, respectively), suggesting the protective mechanism likely resides in the loss of interaction or function. Our results support inhibition or down-regulation of ANGPTL7 as a therapeutic strategy for glaucoma.

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“…This would be useful information before a resident embarks on a rotation as the primary surgeon in a high volume general ophthalmology rotation. This pooling of data has been successfully done with assessment of glaucoma progression 14,15 as well as many unrelated disciplines with the commonality of multiple inputs, measurable outcomes, and many data points. Finally, providing the residents with statistically based feedback from learning studies can assist in changing behavior.…”

Section: Discussionmentioning

confidence: 99%

Relationship between Didactic Attendance and Average Ophthalmic Knowledge Assessment Program Performance

Altenbernd

2020

Journal of Academic Ophthalmology

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Background The Accreditation Council for Graduate Medical Education's ophthalmology-specific program requirements indicate that a resident must participate in 360 hours of educational activities over the course of residency. The requirements allow a variety of options by which this educational process may occur. Furthermore, the current generation of learners has used a large array of learning tools in their careers: digital media, online resources, question banks, flipped classrooms, problem-based learning, and asynchronous lectures in addition to the traditional face-to-face didactic lectures. Objectives This article determines if the traditional face-to-face didactic lectures play a significant role in the education of ophthalmology residents, attempts to quantitate this role, and estimates the relative importance of this method. Methods Lecture hours beginning immediately after an Ophthalmic Knowledge Assessment Program (OKAP) examination and up to the subsequent exam was tabulated for 40 residents over 8 years. From 2012 through 2019 each of the residents took the OKAP exam one to three times. The average overall OKAP score for each resident was then regressed against his or her logged hours of didactics that year. This rate of average OKAP score to lecture hours was then evaluated for significant differences on the basis of postgraduate year (PGY). “STATA 13.1 IC,” was used for all statistical analysis. Conclusion Traditional synchronous didactic lectures play a statistically significant role in the education of ophthalmology residents. For each 3.5 hours of lectures per year there is a 1% increase in the average OKAP score, p = 0.0064. However, the amount of variability explained by this single factor is low, R 2 = 0.067, and other factors are presumed to play a large part in learning. PGY subgroup analysis showed significant difference in the OKAP performance to lecture hour relationship, the number of lecture hours attended, and the OKAP performance.

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Personalized Prediction of Glaucoma Progression Under Different Target Intraocular Pressure Levels Using Filtered Forecasting Methods

Abstract: To 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.

Cited by 51 publications

References 24 publications

Artificial intelligence and deep learning in ophthalmology

Artificial intelligence and deep learning in ophthalmology

Rare protein-altering variants in ANGPTL7 lower intraocular pressure and protect against glaucoma

Relationship between Didactic Attendance and Average Ophthalmic Knowledge Assessment Program Performance

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