Identifying the critical factors that influence intraocular pressure using an automated regression tree

Nishanee Rampersad,Rekha Hansraj

doi:10.4102/aveh.v76i1.364

Abstract

Background: Assessment of intraocular pressure (IOP) is an important test in glaucoma. In addition, anterior segment variables may be useful in screening for glaucoma risk. Studies have investigated the associations between IOP and anterior segment variables using traditional statistical methods. The classification and regression tree (CART) method provides another dimension to detect important variables in a relationship automatically.Aim: To identify the critical factors that influence IOP using a regression tree.Methods: A quantitative cross-sectional research design was used. Anterior segment variables were measured in 700 participants using the iVue100 optical coherence tomographer, Oculus Keratograph and Nidek US-500 ultrasonographer. A Goldmann applanation tonometer was used to measure IOP. Data from only the right eyes were analysed because of high levels of interocular symmetry. A regression tree model was generated with the CART method and Pearson’s correlation coefficients were used to assess the relationships between the ocular variables.Results: The mean IOP for the entire sample was 14.63 mmHg ± 2.40 mmHg. The CART method selected three anterior segment variables in the regression tree model. Central corneal thickness was the most important variable with a cut-off value of 527 µm. The other important variables included average paracentral corneal thickness and axial anterior chamber depth. Corneal thickness measurements increased towards the periphery and were significantly correlated with IOP (r ≥ 0.50, p ≤ 0.001).Conclusion: The CART method identified the anterior segment variables that influenced IOP. Understanding the relationship between IOP and anterior segment variables may help to clinically identify patients with ocular risk factors associated with elevated IOPs.

Highlights

Glaucoma is an optic neuropathy that sometimes results in irreversible blindness.[1]
Investigating the relationship between intraocular pressure (IOP) and anterior segment ocular variables may be useful for better understanding the association between these ocular variables and IOP.[8]. This knowledge may help to clinically identify patients with ocular risk factors associated with elevated IOPs.[21]
The results showed that four anterior segment variables influenced IOP

Summary

Introduction

Glaucoma is an optic neuropathy that sometimes results in irreversible blindness.[1]. The assessment of intraocular pressure (IOP) is a fundamental clinical test used for the screening, diagnosis and management of glaucoma.[1,8] IOP is still considered as an important risk factor for glaucoma,[1,9] but previous studies have reported that other ocular anterior segment variables are useful in screening for individuals at risk for glaucoma.[10,11,12,13] For example, the Ocular Hypertension Treatment Study highlighted the importance of central corneal thickness (CCT) in evaluating risk for POAG.[10] Some studies have indicated that other anterior chamber variables (such as depth and angle width) may be useful for evaluating risk for developing angle closure glaucoma.[12,13,14] As a result, the relationship between IOP and anterior segment ocular variables has been investigated in both population-based[15,16,17] and clinic-based[18,19,20] studies. Assessment of intraocular pressure (IOP) is an important test in glaucoma. Studies have investigated the associations between IOP and anterior segment variables using traditional statistical methods. The classification and regression tree (CART) method provides another dimension to detect important variables in a relationship automatically

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Conclusion