Multivariate Model of the Intersubject Variability of the Retinal Nerve Fiber Layer Thickness in Healthy Subjects.

Purpose: We present and validate a multivariate model that partially compensates for retinal nerve fiber layer (RNFL) intersubject variability.

Methods: A total of 202 healthy volunteers randomly attributed to a training (TS) and a validation (VS) sample underwent complete ophthalmic examination, including Fourier-domain optical coherence tomography (FD-OCT). We acquired FD-OCT data centered at the optic disc (OD) and the macula. Two-dimensional (2D) projection images were computed and registered, to determine the distance between fovea and OD centers (FD) and their respective angle (FA). Retinal vessels were automatically segmented in the projection images and used to calculate the circumpapillary retinal vessel density (RVD) profile. Using the TS, a multivariate model was calculated for each of 256 sectors of the RNFL, including OD ratio, orientation and area, RVD, FD, FA, age, and refractive error. Model selection was based on Akaike Information Criteria. The compensation effect was determined for 12 clock hour sectors, comparing the coefficients of variation (CoV) of measured and model-compensated RNFL thicknesses. The model then was applied to the VS, and CoV was calculated.

Results: The R value for the multivariate model was, on average 0.57 (max = 0.68). Compensation reduced the CoV on average by 18%, both for the TS and VS (up to 23% and 29%), respectively.

Conclusions: We have developed and validated a comprehensive multivariate model that may be used to create a narrower range of normative RNFL data, which could improve diagnostic separation between early glaucoma and healthy subjects. This, however, remains to be demonstrated in future studies.