Quantitative Foveal Structural Metrics as Predictors of Visual Acuity in Human Albinism

Abstract

Purpose: To assess the degree to which quantitative foveal structural measurements account for variation in best-corrected visual acuity (BCVA) in human albinism.

Methods: BCVA was measured and spectral domain optical coherence tomography (SD-OCT) images were acquired for 74 individuals with albinism. Categorical foveal hypoplasia grades were assessed using the Leicester Grading System for Foveal Hypoplasia. Foveal anatomical specialization (foveal versus parafoveal value) was quantified for inner retinal layer (IRL) thickness, outer segment (OS) length, and outer nuclear layer (ONL) thickness. These metrics, participant sex, and age were used to build a multiple linear regression of BCVA. This combined linear model's predictive properties were compared to those of categorical foveal hypoplasia grading.

Results: The cohort included three participants with type 1a foveal hypoplasia, 23 participants with type 1b, 33 with type 2, ten with type 3, and five with type 4. BCVA ranged from 0.08 to 1.00 logMAR (mean ± SD: 0.53 ± 0.21). IRL ratio, OS ratio, and ONL ratio were measured in all participants and decreased with increasing severity of foveal hypoplasia. The best-fit combined linear model included all three quantitative metrics and participant age expressed as a binary variable (divided into 0-18 years and 19 years or older; adjusted R2 = 0.500). This model predicted BCVA more accurately than a categorical foveal hypoplasia model (adjusted R2 = 0.352).

Conclusions: A quantitative model of foveal specialization accounts for more variance in BCVA in albinism than categorical foveal hypoplasia grading. Other factors, such as optical aberrations and eye movements, may account for the remaining unexplained variance.

Figure 1.

BCVA is generally worse with increasing subjective severity of foveal hypoplasia (FH), but there is considerable overlap between FH grades. Brackets indicate pairs of groups for which the Holm-Sidak multiple comparisons test showed a difference with P < 0.001 (***) or P < 0.0001 (****).

Figure 2.

IRL ratio (A), OS ratio (B), and ONL ratio (C) all decrease with increasing severity of categorical foveal hypoplasia (FH) grade. However, each quantitative metric overlaps considerably between FH grades that were assigned according to the Leicester system. Box limits show interquartile range; horizontal bars within boxes show median; whiskers show minimum and maximum. Brackets indicate pairwise comparisons for which Dunn's multiple comparisons test shows a difference with P < 0.05 (*), P < 0.01 (**), P < 0.001 (***), or P < 0.0001 (****).

Figure 3.

Foveal specialization and visual acuity (VA) varies between individuals with the same categorical grade of foveal hypoplasia. Shown are OCT scans with callouts of the foveal and parafoveal regions from pairs of participants who had the same foveal hypoplasia grade (as assigned according to the Leicester system) but highly disparate IRL ratio (A), OS ratio (B), or ONL ratio (C). Colored bars indicate the foveal and parafoveal IRL thickness (yellow), OS length (green), and ONL thickness (cyan). Parafoveal regions are from the nasal retina at the eccentricity (relative to the foveal center) where each measurement was taken (A, 1 mm; B and C, 1.75 mm). Scale bars: 200 µm.

Figure 4.

A combined linear model based on quantitative metrics of foveal structure predicts BCVA in albinism more accurately than categorical foveal hypoplasia grade. The correlation between predicted BCVA (BCVA_pred) and observed BCVA (BCVA_obs) was moderate for the predictive model based on categorical foveal hypoplasia grade (A, R = 0.622), but was stronger for the combined linear model based on quantitative structural metrics (B, R = 0.736). In panels A and B, dashed lines show where BCVA_pred = BCVA_obs (i.e., perfect agreement between predicted and observed BCVA). When we plotted the difference between predicted and observed BCVA (BCVA_pred – BCVA_obs) against the average of predicted and observed BCVA (BCVA_mean), there was a negative trend in the bias for both the categorical predictive model (C) and the combined linear model (D); however, this trend was reduced in the combined linear model. In panels C and D, solid lines show bias, dashed lines show limits of agreement, and shaded gray areas show the 95% confidence intervals of the bias and limits of agreement.