Evaluation of a Primary Open-Angle Glaucoma Prediction Model Using Long-term Intraocular Pressure Variability Data: A...

Area of Science:

• Ophthalmology
• Glaucoma Research
• Predictive Modeling

Background:

• The role of long-term intraocular pressure (IOP) variability in primary open-angle glaucoma (POAG) development remains debated.
• Accurate prediction of POAG is crucial for timely intervention in individuals with ocular hypertension.

Purpose of the Study:

• To determine if incorporating long-term IOP variability metrics enhances the prediction of POAG development.
• To evaluate the predictive accuracy of models using different IOP variability measures compared to baseline IOP.

Main Methods:

• Secondary analysis of two randomized clinical trials (Ocular Hypertension Treatment Study and European Glaucoma Prevention Study).
• Cox proportional hazards regression models were used to compare prediction models with and without IOP variability data.
• The C statistic was employed to assess and compare the predictive accuracy of the models.

Main Results:

• The original prediction model achieved a C statistic of 0.741.
• Substituting baseline IOP with mean follow-up IOP improved the C statistic to 0.784.
• No IOP variability measure, when added to a model with mean follow-up IOP, significantly increased predictive accuracy (increase < 0.007).

Conclusions:

• Long-term IOP variability does not provide substantial additional explanatory power for POAG prediction beyond mean IOP.
• Current prediction models using baseline and mean follow-up IOP are adequate for identifying individuals at risk of POAG.
• Further research may explore other factors influencing POAG development in ocular hypertension.