Increased Pupil Constriction Latency in Immunoglobulin G4-related Ophthalmic Disease.

PURPOSE: To examine the pupillary light reflex (PLR) in Immunoglobulin G4-related ophthalmic disease (IgG4-ROD) compared with healthy controls using virtual reality-based infrared pupillometry. DESIGN: Cross-sectional matched case-control study. PARTICIPANTS: Twenty-five eyes from 25 biopsy-proven IgG4-ROD patients without a history of optic neuropathy and 50 eyes from 50 age-, sex-, and smoking-matched healthy subjects were included. METHODS: Participants underwent PLR assessment using a virtual reality head-mounted display with integrated infrared pupillometry. Pupillary responses to three progressively increasing white-light stimuli were recorded at 120 Hz. Twenty-seven PLR parameters were extracted across response latency, constriction, and redilation phases. Group differences were analyzed using paired t-tests, multinomial logistic regression, and backward stepwise variable selection. Machine learning classifiers were evaluated using accuracy and area under the receiver operating characteristic curve (AUC). Main Outcome Measures Pupil constriction latency, constriction amplitude, velocity, redilation parameters, and machine learning classification performance metrics. RESULTS: Pupil constriction latency at the third (highest intensity) stimulus (LAT3rd) was significantly prolonged in IgG4-ROD eyes compared with controls (P < 0.05), while other PLR parameters were comparable between groups. LAT3rd remained a significant differentiating feature on multinomial logistic regression and backward stepwise selection. Machine learning models using all features showed good performance, with Random Forest achieving an accuracy/AUC of 0.875/0.900. When using bootstrapped LAT3rd-only data, K-nearest neighbors, decision tree, and random forest models also demonstrated good classification performance. CONCLUSION: IgG4-ROD eyes exhibit prolonged pupil constriction latency, suggesting subclinical involvement of the pupillary light reflex pathway. Virtual reality-based infrared pupillometry may serve as a useful non-invasive tool for detecting neural or autonomic dysfunction in IgG4-ROD.