Clinical factors affecting pupillary light reflex parameters: a single‐centre, cross‐sectional study

• Published in: Ophthalmic & physiological optics Vol. 41; no. 5; pp. 952 - 960
• Main Author: Ishikawa, Masaaki
• Format: Journal Article
• Language: English
• Published: England Wiley Subscription Services, Inc 01.09.2021
• Subjects: Aging; Cross-Sectional Studies; Humans; Light; light stimulus intensity; linear mixed‐effects models; Pupil; pupillary light reflex; Reflex, Pupillary; Regression analysis; Sex; Smoking; Symmetry; Vision, Ocular; light stimulus intensity; pupillary light reflex; aging; linear mixed-effects models; sex; smoking
• ISSN: 0275-5408; 1475-1313; 1475-1313
• DOI: 10.1111/opo.12858

Purpose To evaluate the effects of stimulus intensity, aging, sex, smoking and eye symmetry on pupillary light reflex (PLR) parameters. Methods We evaluated 2812 eyes from 1406 subjects in a single‐centre, cross‐sectional study. PLR data were collected using four different stimulus intensities. We prepared two models for each of the eight PLR parameters, and defined the model with the lowest values of Akaike's information criterion (AIC) as being the best‐fit. Model A was a linear regression model without adjustment for among‐individual variability, while the Model B linear mixed‐effects models (LMMs) were adjusted for among‐individual variability. The regression coefficients of the two models were compared. Results Model B showed the lowest AIC values for all parameters and the best fit. For light stimulus intensity, age and eye symmetry, the two models yielded similar results for all PLR parameters. For sex and smoking index, some PLR parameters showed the opposite results, i.e., Model A showed significant effects while Model B did not. Conclusion These results indicate that light stimulus intensity, aging, sex, smoking and eye symmetry are factors that affect PLR parameters. These should be adjusted when evaluating the clinical potential of PLR as a diagnostic tool. In addition, adjusting for among‐individual variability due to LMMs can improve the model fit and reduce false positives. This can reveal the association between clinical factors and PLR parameters with increased accuracy.