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

Abstract

Purpose: To generate personalized forecasts of how patients with open-angle glaucoma (OAG) experience disease progression at different intraocular pressure (IOP) levels to aid clinicians with setting personalized target IOPs.

Design: Secondary analyses using longitudinal data from 2 randomized controlled trials.

Participants: Participants with moderate or advanced OAG from the Collaborative Initial Glaucoma Treatment Study (CIGTS) or the Advanced Glaucoma Intervention Study (AGIS).

Methods: By using perimetric and tonometric data from trial participants, we developed and validated Kalman Filter (KF) models for fast-, slow-, and nonprogressing patients with OAG. The KF can generate personalized and dynamically updated forecasts of OAG progression under different target IOP levels. For each participant, we determined how mean deviation (MD) would change if the patient maintains his/her IOP at 1 of 7 levels (6, 9, 12, 15, 18, 21, or 24 mmHg) over the next 5 years. We also model and predict changes to MD over the same time horizon if IOP is increased or decreased by 3, 6, and 9 mmHg from the level attained in the trials.

Main outcome measures: Personalized estimates of the change in MD under different target IOP levels.

Results: A total of 571 participants (mean age, 64.2 years; standard deviation, 10.9) were followed for a mean of 6.5 years (standard deviation, 2.8). Our models predicted that, on average, fast progressors would lose 2.1, 6.7, and 11.2 decibels (dB) MD under target IOPs of 6, 15, and 24 mmHg, respectively, over 5 years. In contrast, on average, slow progressors would lose 0.8, 2.1, and 4.1 dB MD under the same target IOPs and time frame. When using our tool to quantify the OAG progression dynamics for all 571 patients, we found no statistically significant differences over 5 years between progression for black versus white, male versus female, and CIGTS versus AGIS participants under different target IOPs (P > 0.05 for all).

Conclusions: 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.

Figure 1. Prediction Errors of K_F, K_S, and K_N Models for All Patients in the Testing Dataset after Stratification by Progression Status

MD = mean deviation; dB = decibels. Each 6-month prediction interval is from Period 5. The figure shows prediction errors for the three customized Kalman filter models separately, as well as all combined. It shows that overall and for all 3 progression status groups, the mean MD prediction error is rather small with the forecasts within 1 dB of the actual observations from the trial. The largest prediction errors were when the model forecasted far into the future for subset of patients who were fast-progressors. Patients were required to have 10 or more IOP, MD, and PSD measurements for inclusion in this analysis.

Figure 2. Example of Forecasting Glaucoma Progression Under Different Target Intraocular Pressure Levels for a Fast Progressing Patient in the Sample

IOP = intraocular pressure; MD = mean deviation; mmHg = millimeter of mercury; dB = decibels. The figure shows the K_F forecast of what would happen to MD over the next 5 years if this particular patient’s intraocular pressure were maintained at levels of 6, 9, 12, 15, 21, or 24 mm Hg beginning at period 6. Raw readings are observed values of IOP and MD from the AGIS and CIGTS trials. Filtered readings are the estimated values by our Kalman filter models.

Figure 3. Example of Forecasting Glaucoma Progression Under Different Target Intraocular Pressure Levels for a Slow Progressing Patient in the Sample

IOP = intraocular pressure; MD = mean deviation; mmHg = millimeter of mercury; dB = decibels. The figure shows the K_S forecast of what would happen to MD over the next 5 years if the patient’s intraocular pressure were maintained at levels of 6, 9, 12, 15, 21, or 24 mm Hg beginning at period 6. Raw readings are observed values of IOP and MD from the AGIS and CIGTS trials. Filtered readings are the estimated values by our Kalman filter models.

Figure 4. Kalman Filter Forecasts of How Changes in Intraocular Pressure of Different Magnitudes Affect Changes in Mean Deviation Over Time for Fast and Slow Progressing Patients

MD = mean deviation; IOP = intraocular pressure; mmHg = millimeter of mercury; dB = decibels; Avg = average. Prediction period 0 corresponds to 2.5 years after enrollment in the trials. The lines and the bounds around each line represent the mean and the 95% confidence interval around it, respectively. The mean starting MD (i.e., MD in prediction period 0) for fast and slow-progressors are −12.2 dB and −6.8 dB, respectively. Both groups have equal mean IOPs of 17.5 mmHg in prediction period 0. The figure shows forecasts of what would happen to MD over the next 5 years for fast and slow-progressors (using the K_F and K_S models, respectively) if their IOPs remained unchanged, increased 3, 6, or 9 mmHg or decreased 3, 6, 9, or 12 mmHg from the level measured at prediction period 0. For all levels of change in IOP, fast-progressors will have statistically significant lower MD after 5 years (P < 0.0001).