Detecting glaucoma using automated pupillography

Abstract

Objective: To evaluate the ability of a binocular automated pupillograph to discriminate healthy subjects from those with glaucoma.

Design: Cross-sectional observational study.

Participants: Both eyes of 116 subjects, including 66 patients with glaucoma in at least 1 eye and 50 healthy subjects from the Diagnostic Innovations in Glaucoma Study. Eyes were classified as glaucomatous by repeatable abnormal standard automated perimetry (SAP) or progressive glaucomatous changes on stereophotographs.

Methods: All subjects underwent automated pupillography using the RAPDx pupillograph (Konan Medical USA, Inc., Irvine, CA).

Main outcome measures: Receiver operating characteristic (ROC) curves were constructed to assess the diagnostic ability of pupil response parameters to white, red, green, yellow, and blue full-field and regional stimuli. A ROC regression model was used to investigate the influence of disease severity and asymmetry on diagnostic ability.

Results: The largest area under the ROC curve (AUC) for any single parameter was 0.75. Disease asymmetry (P <0.001), but not disease severity (P = 0.058), had a significant effect on diagnostic ability. At the sample mean age (60.9 years), AUCs for arbitrary values of intereye difference in SAP mean deviation (MD) of 0, 5, 10, and 15 dB were 0.58, 0.71, 0.82, and 0.90, respectively. The mean intereye difference in MD was 2.2±3.1 dB. The best combination of parameters had an AUC of 0.85; however, the cross-validated bias-corrected AUC for these parameters was only 0.74.

Conclusions: Although the pupillograph had a good ability to detect glaucoma in the presence of asymmetric disease, it performed poorly in those with symmetric disease.