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

Area of Science:

• Ophthalmology
• Medical Technology
• Data Science

Background:

• Open-angle glaucoma (OAG) is a progressive optic neuropathy.
• Accurate prognostication of OAG progression is crucial for effective management.
• Personalized treatment strategies require predicting disease trajectory at various intraocular pressure (IOP) levels.

Purpose of the Study:

• To develop a predictive model for personalized open-angle glaucoma (OAG) progression.
• To forecast disease progression at different intraocular pressure (IOP) levels.
• To assist clinicians in establishing individualized target IOPs for OAG patients.

Main Methods:

• Utilized longitudinal data from the Collaborative Initial Glaucoma Treatment Study (CIGTS) and Advanced Glaucoma Intervention Study (AGIS).
• Developed and validated Kalman Filter (KF) models to predict OAG progression (fast, slow, non-progressors).
• Modeled changes in Mean Deviation (MD) over 5 years based on various target IOP levels (6-24 mmHg).

Main Results:

• KF models generated personalized forecasts of OAG progression under different target IOPs.
• Predicted average 5-year MD loss for fast progressors: 2.1 dB (6 mmHg IOP), 6.7 dB (15 mmHg), 11.2 dB (24 mmHg).
• No significant differences in progression were found based on race, sex, or study cohort across different target IOPs.

Conclusions:

• Introduced the first clinical decision-making tool for personalized OAG progression forecasting.
• The tool provides dynamic, individualized predictions of disease trajectory.
• Facilitates informed clinical decisions regarding personalized target IOPs for OAG management.