Comparing Perimetric Loss at Different Target Intraocular Pressures for Patients with High Tension and Normal Tension Glaucoma

Abstract Objective To compare forecasted changes in mean deviation (MD) on perimetry for patients with normal-tension glaucoma (NTG) and high-tension glaucoma (HTG) at different target intraocular pressures (IOPs) using a machine-learning technique called Kalman Filtering (KF). Design Retrospective cohort study. Participants 496 patients with HTG from the Collaborative Initial Glaucoma Treatment Study or the Advanced Glaucoma Intervention Study and 262 patients with NTG from Japan. Methods Using the first 5 sets of tonometry and perimetry measurements, we classified each patient as a fast-progressor, slow-progressor, or non-progressor. Using KF, we generated personalized forecasts of MD changes on perimetry over 2.5 years of follow-up for fast-progressors and slow-progressors with HTG and NTG whose IOPs were maintained at hypothetical IOP targets of 9-21 mmHg. We also assessed future MD loss with different percentage reductions in IOP from baseline (0-50%) for the groups. Main Outcome Measures Mean change in forecasted MD at different target IOPs. Results The mean ± SD age of patients with NTG and HTG were 63.5±10.5 years and 66.5±10.9 years, respectively. At target IOPs of 9, 15, and 21, fast progressors with NTG had mean forecasted MD losses of 2.3±0.2, 4.0±0.2, and 5.7±0.2 dB and slow progressors had mean forecasted MD losses of 0.63±0.02, 1.02±0.03, and 1.49±0.07 dB over 2.5 years of follow up, respectively. At target IOPs of 9, 15, and 21, fast progressors with HTG had mean forecasted MD losses of 1.8±0.1, 3.4±0.1, and 5.1±0.1 dB and slow progressors had mean forecasted MD losses of 0.55±0.06, 1.04±0.08, and 1.59±0.10 dB over 2.5 years of follow up. Fast progressors with NTG experienced a 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 at each target IOP (p≥0.24 for all). Fast progressors with HTG experienced greater MD loss than those with NTG with IOP reductions of 0-10% from baseline (p≤0.01 for all) but not 25% (p=0.07) or 50% (p=0.76). Conclusions Machine learning algorithms using KF techniques demonstrate promise at forecasting future values of MD at different target IOPs for patients with NTG and HTG.