Evaluation of a Primary Open-Angle Glaucoma Prediction Model Using Long-term Intraocular Pressure Variability Data

Gordon, Mae O.; Gao, Feng; Huecker, Julia; Miller, J. Philip; Margolis, Mathew; Kass, Michael A.; Miglior, Stefano; Torri, Valter

doi:10.1001/jamaophthalmol.2020.1902

Cited by 20 publications

(18 citation statements)

References 36 publications

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“…The significance of diurnal IOP fluctuation and the development of glaucomatous damage has yet to be fully established in the literature. [86][87][88][89][90][91][92][93][94][95][96] Additional IOP measurements may be indicated, either at different hours of the day on the same day or on different days. Larger inter-eye asymmetry of IOP has also been shown to be a risk for developing glaucoma.…”

Section: Intraocular Pressure Measurementmentioning

confidence: 99%

Primary Open-Angle Glaucoma Suspect Preferred Practice Pattern®

Gedde¹,

Lind

Wright

et al. 2021

Ophthalmology

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Section: Intraocular Pressure Measurementmentioning

confidence: 99%

Primary Open-Angle Glaucoma Suspect Preferred Practice Pattern®

Gedde¹,

Lind

Wright

et al. 2021

Ophthalmology

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“…Notably, patients in the medically treated group had a 3– to 4–mm Hg higher IOP than that of the surgically treated group, conversely suggesting that IOP variability is a more important factor in patients with higher IOP. While the above 2 studies investigated the role of IOP variability in patients with glaucoma, other studies evaluated the role of IOP variability in the development of glaucoma . Medeiros and colleagues found no association between the development of glaucoma and IOP variability in eyes with untreated ocular hypertensive .…”

Section: Discussionmentioning

confidence: 99%

“…Long-term IOP variability can be obtained from repeated IOP measurements taken at serial office visits over months to years. Findings from prior studies have been inconclusive based on assessment of the association between long-term IOP variability and visual field (VF) progression using subjective assessment of standard automated perimetry and/or photograph-based optic disc changes …”

Section: Introductionmentioning

confidence: 99%

Association of Intraocular Pressure With Retinal Nerve Fiber Layer Thinning in Patients With Glaucoma

et al. 2022

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ImportanceHigher intraocular pressure variability may be associated with faster structural changes in patients with glaucoma.ObjectivesTo investigate the association of mean intraocular pressure and intraocular pressure variability (defined as the SD of intraocular pressure and the intraocular pressure range) with the rate of retinal nerve fiber layer thinning over time in patients with glaucoma.Design, Setting, and ParticipantsIn this retrospective analysis of a longitudinal cohort, patients were enrolled from the Diagnostic Innovations in Glaucoma Study and the African Descent and Glaucoma Evaluation study. A total of 815 eyes (564 with perimetric glaucoma and 251 with preperimetric glaucoma) from 508 patients with imaging follow-up for a mean of 6.3 years from December 2008 to October 2020 were studied. Data were analyzed from November 2021 to March 2022.Main Outcomes and MeasuresIn this longitudinal study, eyes with at least 4 visits and 2 years of follow-up optical coherence tomography and intraocular pressure measurement were included. A linear mixed-effect model was used to investigate the association of intraocular pressure parameters with the rates of retinal nerve fiber layer thinning. Dominance analysis was performed to determine the relative importance of the intraocular pressure parameters.ResultsOf 508 included patients, 280 (55.1%) were female, 195 (38.4%) were African American, 24 (4.7%) were Asian, 281 (55.3%) were White, and 8 (1.6%) were another race or ethnicity; the mean (SD) age was 65.5 (11.0) years. The mean rate of retinal nerve fiber layer change was −0.67 (95% CI, −0.73 to −0.60) μm per year. In multivariable models adjusted for mean intraocular pressure and other confounding factors, faster annual rate of retinal nerve fiber layer thinning was associated with a higher SD of intraocular pressure (−0.20[ 95% CI, −0.26 to −0.15] μm per 1–mm Hg higher; P &lt; .001) or higher intraocular pressure range (−0.05 [95% CI, −0.06 to −0.03] μm per 1–mm Hg higher; P &lt; .001).Conclusions and RelevanceIn this study, intraocular pressure variability was independently associated with structural change in patients with glaucoma, even after adjustment for mean intraocular pressure, supporting its potential value in clinical management.

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“…Predicting glaucoma onset and progression is a major clinical challenge. Previous studies demonstrated that biometric parameters, such as baseline IOP, vertical cup-to-disc ratio, mean deviation (in the Humphrey visual field test), and pattern standard deviation are helpful in predicting glaucoma incidence and progression ( 6 – 12 ). However, IOP measurement and visual field tests are often not available in the primary healthcare setting.…”

Section: Introductionmentioning

confidence: 99%

A deep-learning system predicts glaucoma incidence and progression using retinal photographs

Li¹,

Su²,

Lin³

et al. 2022

Journal of Clinical Investigation

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Background Deep learning has been widely used for glaucoma diagnosis. However, there is no clinically validated algorithm for glaucoma incidence and progression prediction. This study aims to develop a clinically feasible deep-learning system for predicting and stratifying the risk of glaucoma onset and progression based on color fundus photographs (CFPs), with clinical validation of performance in external population cohorts. Methods We established data sets of CFPs and visual fields collected from longitudinal cohorts. The mean follow-up duration was 3 to 5 years across the data sets. Artificial intelligence (AI) models were developed to predict future glaucoma incidence and progression based on the CFPs of 17,497 eyes in 9346 patients. The area under the receiver operating characteristic (AUROC) curve, sensitivity, and specificity of the AI models were calculated with reference to the labels provided by experienced ophthalmologists. Incidence and progression of glaucoma were determined based on longitudinal CFP images or visual fields, respectively. Results The AI model to predict glaucoma incidence achieved an AUROC of 0.90 (0.81–0.99) in the validation set and demonstrated good generalizability, with AUROCs of 0.89 (0.83–0.95) and 0.88 (0.79–0.97) in external test sets 1 and 2, respectively. The AI model to predict glaucoma progression achieved an AUROC of 0.91 (0.88–0.94) in the validation set, and also demonstrated outstanding predictive performance with AUROCs of 0.87 (0.81–0.92) and 0.88 (0.83–0.94) in external test sets 1 and 2, respectively. Conclusion Our study demonstrates the feasibility of deep-learning algorithms in the early detection and prediction of glaucoma progression. FUNDING National Natural Science Foundation of China (NSFC); the High-level Hospital Construction Project, Zhongshan Ophthalmic Center, Sun Yat-sen University; the Science and Technology Program of Guangzhou, China (2021), the Science and Technology Development Fund (FDCT) of Macau, and FDCT-NSFC.

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Evaluation of a Primary Open-Angle Glaucoma Prediction Model Using Long-term Intraocular Pressure Variability Data

Cited by 20 publications

References 36 publications

Primary Open-Angle Glaucoma Suspect Preferred Practice Pattern®

Primary Open-Angle Glaucoma Suspect Preferred Practice Pattern®

Association of Intraocular Pressure With Retinal Nerve Fiber Layer Thinning in Patients With Glaucoma

A deep-learning system predicts glaucoma incidence and progression using retinal photographs

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