Intraocular Pressure Variability Does Not Strengthen Glaucoma Predictor Model, Analysis Finds

June 10, 2020

Evidence from 2 randomized clinical trials suggests that long-term intraocular pressure variability does not add substantial explanatory power to a prediction model determining which individuals with untreated ocular hypertension will develop primary open-angle glaucoma, according to a study published in JAMA Ophthalmology.

Evidence from 2 randomized clinical trials suggests that long-term intraocular pressure (IOP) variability does not add substantial explanatory power to a prediction model determining which individuals with untreated ocular hypertension will develop primary open-angle glaucoma (POAG), according to a study published in JAMA Ophthalmology.

Although higher IOP is an important risk factor for glaucoma progression and development and is currently the disease’s only modifiable risk factor, controversy still exists about the effects of long-term IOP variability. While some studies found variability was an independent glaucoma risk factor, other studies have not.

“Mean follow-up IOP does not capture the dynamic changes in pressure from visit to visit, such as peaks, troughs, and ranges,” authors explain. These features “may be independently associated with the development and course of the disease.”

To investigate the influence of long-term IOP variability on the development of POAG, researchers assessed data from 709 individuals participating in the Ocular Hypertension Treatment Study (OHTS) and 397 controls enrolled in the European Glaucoma Prevention Study (EGPS).

Both trials tested the safety and efficacy of topical ocular hypotensive medication in delaying or preventing the development of POAG in patients with ocular hypertension. In each trial, IOP assessments were carried out every 6 months.

In this post hoc secondary analysis, researchers tested whether substituting baseline IOP with mean follow-up IOP, SD of IOP, maximum IOP, range of IOP, or coefficient of variation of IOP improved predictive accuracy.

In total, 97 POAG end points were included from the 709 participants in OHTS. The cohort was 58.7% female, the mean (SD) age was 55.7 (9.59) years, and each patient had an average follow-up time of 6.9 years. In comparison, 44 POAG end points were reported in the EGPS study; 50.1% of the cohort was female, and mean (SD) age was 57.8 (9.76) years. Each participant was followed up with for an average of 4.9 years.

An adaptation of conventional Cox proportional hazards regression modeling was used to determine C statistics, which measured predictive accuracy.

Researchers found the C statistic for the original prediction model was 0.741. After a measure of follow-up IOP was substituted for baseline IOP, C statistics were recorded as:

  • 0.784 for mean follow-up IOP
  • 0.781 for maximum IOP
  • 0.745 for SD of IOP
  • 0.741 for range of IOP
  • 0.729 for coefficient of variation IOP

Investigators also found “no measure of IOP variability, when added to the prediction model that included mean follow-up IOP, age, central corneal thickness, vertical cup-disc ratio, and pattern SD, increased the C statistic by more than 0.007 in either cohort.”

Mean follow-up IOP, in addition to being easy to calculate, more consistent over time, and less dependent on duration follow-up, was a stronger predictive factor than any other measure of long-term IOP variability tested, authors concluded.

However, findings should not be generalized to patients with lower levels of IOP, treated patients, or patients with glaucoma, as all study participants were receiving no active ocular hypotensive therapy.

“Because IOP is currently the only modifiable risk factor for glaucoma, understanding how dynamic variation in IOP is associated with onset and progression of the disease may play a crucial role in management,” researchers note. “These findings suggest that the inclusion of data on long-term IOP variability are unlikely to improve prediction models for the development of POAG in individuals with untreated ocular hypertension.”

Reference

Gordon MO, Gao F, Huecker JB, et al. Evaluation of a primary open-angle glaucoma prediction model using long-term intraocular pressure variability data. JAMA Ophthalmol. Published online June 4, 2020. doi:10.1001/jamaophthalmol.2020.1902