Do long-term intraocular pressure variability data improve a prediction model for which individuals with untreated ocular hypertension will develop primary open-angle glaucoma?
In this post hoc secondary analysis of 2 randomized clinical trials that included 709 individuals, the mean follow-up intraocular pressure improved a prediction model for developing primary open-angle glaucoma that included the following baseline factors: age, central corneal thickness, vertical cup-disc ratio, and pattern SD. Adding intraocular pressure SD, maximum, range, or coefficient of variation to a model that included mean follow-up intraocular pressure and baseline factors did not significantly increase predictive accuracy.
These findings suggest that the inclusion of data on long-term intraocular pressure variability are unlikely to improve prediction models for the development of primary open-angle glaucoma in individuals with untreated ocular hypertension.
The contribution of long-term intraocular pressure (IOP) variability to the development of primary open-angle glaucoma is still controversial.
To assess whether long-term IOP variability data improve a prediction model for the development of primary open-angle glaucoma (POAG) in individuals with untreated ocular hypertension.
Design, Setting, and Participants
This post hoc secondary analysis of 2 randomized clinical trials included data from 709 of 819 participants in the observation group of the Ocular Hypertension Treatment Study (OHTS) followed up from February 28, 1994, to June 1, 2002, and 397 of 500 participants in the placebo group of the European Glaucoma Prevention Study (EGPS) followed up from January 1, 1997, to September 30, 2003. Data analyses were completed between January 1, 2019, and March 15, 2020.
The original prediction model for the development of POAG included the following baseline factors: age, IOP, central corneal thickness, vertical cup-disc ratio, and pattern SD. This analysis tested whether substitution of baseline IOP with mean follow-up IOP, SD of IOP, maximum IOP, range of IOP, or coefficient of variation IOP would improve predictive accuracy.
Main Outcomes and Measures
The C statistic was used to compare the predictive accuracy of multivariable landmark Cox proportional hazards regression models for the development of POAG.
Data from the OHTS consisted of 97 POAG end points from 709 of 819 participants (416 [58.7%] women; 177 [25.0%] African American and 490 [69.1%] white; mean [SD] age, 55.7 [9.59] years; median [range] follow-up, 6.9 [0.96-8.15] years). Data from the EGPS consisted of 44 POAG end points from 397 of 500 participants in the placebo group (201 [50.1%] women; 397 [100%] white; mean [SD] age, 57.8 [9.76] years; median [range] follow-up, 4.9 [1.45-5.76] years). The C statistic for the original prediction model was 0.741. When a measure of follow-up IOP was substituted for baseline IOP in this prediction model, the C statistics were as follows: mean follow-up IOP, 0.784; maximum IOP, 0.781; SD of IOP, 0.745; range of IOP, 0.741; and coefficient of variation IOP, 0.729. The C statistics in the EGPS were similarly ordered. 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.
Conclusions and Relevance
Evidence from the OHTS and the EGPS suggests that long-term variability does not add substantial explanatory power to the prediction model as to which individuals with untreated ocular hypertension will develop POAG.
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Gordon MO, Gao F, Huecker JB, et al. Evaluation of a Primary Open-Angle Glaucoma Prediction Model Using Long-term Intraocular Pressure Variability Data: A Secondary Analysis of 2 Randomized Clinical Trials. JAMA Ophthalmol. Published online June 04, 2020. doi:10.1001/jamaophthalmol.2020.1902
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