biostudies-literatureDeRoos LNational Eye InstituteNEI NIH HHSNational Science Foundation251-259https://www.ebi.ac.uk/biostudies/studies/S-EPMC7965784biostudies-literatureUnknown4(3)<h4>Purpose</h4>To compare forecasted changes in mean deviation (MD) for patients with normal-tension glaucoma (NTG) and high-tension open-angle glaucoma (HTG) at different target intraocular pressures (IOPs) using Kalman filtering, a machine learning technique.<h4>Design</h4>Retrospective cohort study.<h4>Participants</h4>From the Collaborative Initial Glaucoma Treatment Study or Advanced Glaucoma Intervention Study, 496 patients with HTG; from Japan, 262 patients with NTG.<h4>Methods</h4>Using the first 5 sets of tonometry and perimetry measurements, each patient was classified as a fast progressor, slow progressor, or nonprogressor. Using Kalman filtering, personalized forecasts of MD changes over 2.5 years' follow-up were generated for fast and slow progressors with HTG and NTG with IOPs maintained at hypothetical IOP targets of 9 to 21 mmHg. Future MD loss with different percentage IOP reductions from baseline (0%-50%) were also assessed for the groups.<h4>Main outcome measures</h4>Mean forecasted MD change at different target IOPs.<h4>Results</h4>The mean (± standard deviation) patient age was 63.5 ± 10.5 years for NTG and 66.5 ± 10.9 years for HTG. Over the 2.5-year follow-up, at target IOPs of 9, 15, and 21 mmHg, respectively, the mean forecasted MD losses for fast progressors with NTG were 2.3 ± 0.2, 4.0 ± 0.2, and 5.7 ± 0.2 dB; for slow progressors with NTG, losses were 0.63 ± 0.02, 1.02 ± 0.03, and 1.49 ± 0.07 dB; for fast progressors with HTG, losses were 1.8 ± 0.1, 3.4 ± 0.1, and 5.1 ± 0.1 dB; and for slow progressors with HTG, losses were 0.55 ± 0.06, 1.04 ± 0.08, and 1.59 ± 0.10 dB. Fast progressors with NTG had greater MD decline than fast progressors with HTG at each target IOP (P ≤ 0.007 for all). The MD decline for slow progressors with HTG and NTG were similar (P ≥ 0.24 for all target IOPs). Fast progressors with HTG had greater MD loss than those with NTG with 0%-10% IOP reduction since baseline (P ≤ 0.01 for all), but not 25% (P = 0.07) or 50% (P = 0.76) reduction since baseline.<h4>Conclusions</h4>Machine learning algorithms using Kalman filtering techniques demonstrate promise at forecasting future MD values at different target IOPs for patients with NTG and HTG.Ophthalmology. GlaucomaComparing Perimetric Loss at Different Target Intraocular Pressures for Patients with High-Tension and Normal-Tension Glaucoma.PMC7965784R01 EY026641Lavieri MSKazemian PSugiyama KStein JDNitta KAndrews CADeRoos LVan Oyen MPfalseComparing Perimetric Loss at Different Target Intraocular Pressures for Patients with High-Tension and Normal-Tension Glaucoma.<h4>Purpose</h4>To compare forecasted changes in mean deviation (MD) for patients with normal-tension glaucoma (NTG) and high-tension open-angle glaucoma (HTG) at different target intraocular pressures (IOPs) using Kalman filtering, a machine learning technique.<h4>Design</h4>Retrospective cohort study.<h4>Participants</h4>From the Collaborative Initial Glaucoma Treatment Study or Advanced Glaucoma Intervention Study, 496 patients with HTG; from Japan, 262 patients with NTG.<h4>Methods</h4>Using the first 5 sets of tonometry and perimetry measurements, each patient was classified as a fast progressor, slow progressor, or nonprogressor. Using Kalman filtering, personalized forecasts of MD changes over 2.5 years' follow-up were generated for fast and slow progressors with HTG and NTG with IOPs maintained at hypothetical IOP targets of 9 to 21 mmHg. Future MD loss with different percentage IOP reductions from baseline (0%-50%) were also assessed for the groups.<h4>Main outcome measures</h4>Mean forecasted MD change at different target IOPs.<h4>Results</h4>The mean (± standard deviation) patient age was 63.5 ± 10.5 years for NTG and 66.5 ± 10.9 years for HTG. Over the 2.5-year follow-up, at target IOPs of 9, 15, and 21 mmHg, respectively, the mean forecasted MD losses for fast progressors with NTG were 2.3 ± 0.2, 4.0 ± 0.2, and 5.7 ± 0.2 dB; for slow progressors with NTG, losses were 0.63 ± 0.02, 1.02 ± 0.03, and 1.49 ± 0.07 dB; for fast progressors with HTG, losses were 1.8 ± 0.1, 3.4 ± 0.1, and 5.1 ± 0.1 dB; and for slow progressors with HTG, losses were 0.55 ± 0.06, 1.04 ± 0.08, and 1.59 ± 0.10 dB. Fast progressors with NTG had greater MD decline than fast progressors with HTG at each target IOP (P ≤ 0.007 for all). The MD decline for slow progressors with HTG and NTG were similar (P ≥ 0.24 for all target IOPs). Fast progressors with HTG had greater MD loss than those with NTG with 0%-10% IOP reduction since baseline (P ≤ 0.01 for all), but not 25% (P = 0.07) or 50% (P = 0.76) reduction since baseline.<h4>Conclusions</h4>Machine learning algorithms using Kalman filtering techniques demonstrate promise at forecasting future MD values at different target IOPs for patients with NTG and HTG.2021-01-01T00:00:00Z2021 May-Jun2025-04-04T10:17:31.973Z2025-04-04T10:17:31.973ZS-EPMC79657843295075310.1016/j.ogla.2020.09.009