The effect of changes in intraocular pressure on the risk of primary open-angle glaucoma in patients with ocular hypertension: an application of latent class analysis

• Published in: BMC medical research methodology Vol. 12; no. 1; p. 151
• Main Authors: Gao, Feng, Miller, J Philip, Miglior, Stefano, Beiser, Julia A, Torri, Valter, Kass, Michael A, Gordon, Mae O
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 04.10.2012; BioMed Central; BMC
• Subjects: Blindness; Care and treatment; Cohort Studies; Disease susceptibility; Female; Glaucoma; Glaucoma, Open-Angle - diagnosis; Glaucoma, Open-Angle - etiology; Humans; Hypertension; Intraocular pressure; Intraocular Pressure - physiology; Intraocular pressure fluctuation; Latent class analysis; Latent variables; Longitudinal data; Male; Measurement; Middle Aged; Ocular Hypertension; Open-angle glaucoma; Ophthalmology; Prediction model; Primary open-angle glaucoma; Risk Factors; Standard deviation; Survival; Survival data; Time-dependent covariate; Tonometry, Ocular; Visual Fields
• ISSN: 1471-2288; 1471-2288
• DOI: 10.1186/1471-2288-12-151

Primary open-angle glaucoma (POAG) is one of the leading causes of blindness in the United States and worldwide. While lowering intraocular pressure (IOP) has been proven to be effective in delaying or preventing the onset of POAG in many large-scale prospective studies, one of the recent hot topics in glaucoma research is the effect of IOP fluctuation (IOP lability) on the risk of developing POAG in treated and untreated subjects. In this paper, we analyzed data from the Ocular Hypertension Treatment Study (OHTS) and the European Glaucoma Prevention Study (EGPS) for subjects who had at least 2 IOP measurements after randomization prior to POAG diagnosis. We assessed the interrelationships among the baseline covariates, the changes of post-randomization IOP over time, and the risk of developing POAG, using a latent class analysis (LCA) which allows us to identify distinct patterns (latent classes) of IOP trajectories. The IOP change in OHTS was best described by 6 latent classes differentiated primarily by the mean IOP levels during follow-up. Subjects with high post-randomization mean IOP level and/or large variability were more likely to develop POAG. Five baseline factors were found to be significantly predictive of the IOP classification in OHTS: treatment assignment, baseline IOP, gender, race, and history of hypertension. In separate analyses of EGPS, LCA identified different patterns of IOP change from those in OHTS, but confirmed that subjects with high mean level and large variability were at high risk to develop POAG. LCA provides a useful tool to assess the impact of post-randomization IOP level and fluctuation on the risk of developing POAG in patients with ocular hypertension. The incorporation of post-randomization IOP can improve the overall predictive ability of the original model that included only baseline risk factors.