Gian-Gabriel P Garcia1, Mariel S Lavieri1, Chris Andrews2,3, Xiang Liu1, Mark P Van Oyen1, Michael A Kass4, Mae O Gordon4, Joshua D Stein2,3,5. 1. Department of Industrial and Operations Engineering, University of Michigan College of Engineering, Ann Arbor. 2. Department of Ophthalmology and Visual Sciences, University of Michigan Medical School, Ann Arbor. 3. Center for Eye Policy and Innovation, University of Michigan, Ann Arbor. 4. Ophthalmology and Visual Sciences, Washington University School of Medicine in St Louis, St Louis, Missouri. 5. Department of Health Management and Policy, University of Michigan School of Public Health, Ann Arbor.
Abstract
Importance: Techniques that properly identify patients in whom ocular hypertension (OHTN) is likely to progress to open-angle glaucoma can assist clinicians with deciding on the frequency of monitoring and the potential benefit of early treatment. Objective: To test whether Kalman filtering (KF), a machine learning technique, can accurately forecast mean deviation (MD), pattern standard deviation, and intraocular pressure values 5 years into the future for patients with OHTN. Design, Setting, and Participants: This cohort study was a secondary analysis of data from patients with OHTN from the Ocular Hypertension Treatment Study, performed between February 1994 and March 2009. Patients underwent tonometry and perimetry every 6 months for up to 15 years. A KF (KF-OHTN) model was trained, validated, and tested to assess how well it could forecast MD, pattern standard deviation, and intraocular pressure at up to 5 years, and the forecasts were compared with results from the actual trial. Kalman filtering for OHTN was compared with a previously developed KF for patients with high-tension glaucoma (KF-HTG) and 3 traditional forecasting algorithms. Statistical analysis for the present study was performed between May 2018 and May 2019. Main Outcomes and Measures: Prediction error and root-mean-square error at 12, 24, 36, 48, and 60 months for MD, pattern standard deviation, and intraocular pressure. Results: Among 1407 eligible patients (2806 eyes), 809 (57.5%) were female and the mean (SD) age at baseline was 57.5 (9.6) years. For 2124 eyes with sufficient measurements, KF-OHTN forecast MD values 60 months into the future within 0.5 dB of the actual value for 696 eyes (32.8%), 1.0 dB for 1295 eyes (61.0%), and 2.5 dB for 1980 eyes (93.2%). Among the 5 forecasting algorithms tested, KF-OHTN achieved the lowest root-mean-square error (1.72 vs 1.85-4.28) for MD values 60 months into the future. For the subset of eyes that progressed to open-angle glaucoma, KF-OHTN and KF-HTG forecast MD values 60 months into the future within 1 dB of the actual value for 30 eyes (68.2%; 95% CI, 54.4%-82.0%) and achieved the lowest root-mean-square error among all models. Conclusions and Relevance: These findings suggest that machine learning algorithms such as KF can accurately forecast MD, pattern standard deviation, and intraocular pressure 5 years into the future for many patients with OHTN. These algorithms may aid clinicians in managing OHTN in their patients.
Importance: Techniques that properly identify patients in whom ocular hypertension (OHTN) is likely to progress to open-angle glaucoma can assist clinicians with deciding on the frequency of monitoring and the potential benefit of early treatment. Objective: To test whether Kalman filtering (KF), a machine learning technique, can accurately forecast mean deviation (MD), pattern standard deviation, and intraocular pressure values 5 years into the future for patients with OHTN. Design, Setting, and Participants: This cohort study was a secondary analysis of data from patients with OHTN from the Ocular Hypertension Treatment Study, performed between February 1994 and March 2009. Patients underwent tonometry and perimetry every 6 months for up to 15 years. A KF (KF-OHTN) model was trained, validated, and tested to assess how well it could forecast MD, pattern standard deviation, and intraocular pressure at up to 5 years, and the forecasts were compared with results from the actual trial. Kalman filtering for OHTN was compared with a previously developed KF for patients with high-tension glaucoma (KF-HTG) and 3 traditional forecasting algorithms. Statistical analysis for the present study was performed between May 2018 and May 2019. Main Outcomes and Measures: Prediction error and root-mean-square error at 12, 24, 36, 48, and 60 months for MD, pattern standard deviation, and intraocular pressure. Results: Among 1407 eligible patients (2806 eyes), 809 (57.5%) were female and the mean (SD) age at baseline was 57.5 (9.6) years. For 2124 eyes with sufficient measurements, KF-OHTN forecast MD values 60 months into the future within 0.5 dB of the actual value for 696 eyes (32.8%), 1.0 dB for 1295 eyes (61.0%), and 2.5 dB for 1980 eyes (93.2%). Among the 5 forecasting algorithms tested, KF-OHTN achieved the lowest root-mean-square error (1.72 vs 1.85-4.28) for MD values 60 months into the future. For the subset of eyes that progressed to open-angle glaucoma, KF-OHTN and KF-HTG forecast MD values 60 months into the future within 1 dB of the actual value for 30 eyes (68.2%; 95% CI, 54.4%-82.0%) and achieved the lowest root-mean-square error among all models. Conclusions and Relevance: These findings suggest that machine learning algorithms such as KF can accurately forecast MD, pattern standard deviation, and intraocular pressure 5 years into the future for many patients with OHTN. These algorithms may aid clinicians in managing OHTN in their patients.
Authors: Gabriel A Villasana; Chris Bradley; Tobias Elze; Jonathan S Myers; Louis Pasquale; C Gustavo De Moraes; Sarah Wellik; Michael V Boland; Pradeep Ramulu; Greg Hager; Mathias Unberath; Jithin Yohannan Journal: Transl Vis Sci Technol Date: 2022-05-02 Impact factor: 3.048
Authors: Brian C Stagg; Joshua D Stein; Felipe A Medeiros; Barbara Wirostko; Alan Crandall; M Elizabeth Hartnett; Mollie Cummins; Alan Morris; Rachel Hess; Kensaku Kawamoto Journal: Ophthalmol Glaucoma Date: 2020-08-15
Authors: Mohammad Zhalechian; Mark P Van Oyen; Mariel S Lavieri; Carlos Gustavo De Moraes; Christopher A Girkin; Massimo A Fazio; Robert N Weinreb; Christopher Bowd; Jeffrey M Liebmann; Linda M Zangwill; Christopher A Andrews; Joshua D Stein Journal: Ophthalmol Sci Date: 2021-12-21