Development and validation of an associative model for the detection of glaucoma using pupillography.

Abstract

To develop and validate an associative model using pupillography that best discriminates those with and without glaucoma. A prospective case-control study. We enrolled 148 patients with glaucoma (mean age 67 ± 11) and 71 controls (mean age 60 ± 10) in a clinical setting. This prototype pupillometer is designed to record and analyze pupillary responses at multiple, controlled stimulus intensities while using varied stimulus patterns and colors. We evaluated three approaches: (1) comparing the responses between the two eyes; (2) comparing responses to stimuli between the superonasal and inferonasal fields within each eye; and (3) calculating the absolute pupil response of each individual eye. Associative models were developed using stepwise regression or forward selection with Akaike information criterion and validated by fivefold cross-validation. We assessed the associative model using sensitivity, specificity and the area-under-the-receiver operating characteristic curve. Persons with glaucoma had more asymmetric pupil responses in the two eyes (P < 0.001); between superonasal and inferonasal visual field within the same eye (P = 0.014); and smaller amplitudes, slower velocities and longer latencies of pupil responses compared to controls (all P < 0.001). A model including age and these three components resulted in an area-under-the-receiver operating characteristic curve of 0.87 (95% CI 0.83 to 0.92) with 80% sensitivity and specificity in detecting glaucoma. This result remained robust after cross-validation. Using pupillography, we were able to discriminate among persons with glaucoma and those with normal eye examinations. With refinement, pupil testing may provide a simple approach for glaucoma screening.