Modeling Visual Acuity in Geographic Atrophy Secondary to Age-Related Macular Degeneration.

Research paper by Steffen S Schmitz-Valckenberg, Jennifer J Nadal, Rolf R Fimmers, Moritz M Lindner, Frank G FG Holz, Matthias M Schmid, Monika M Fleckenstein,

Indexed on: 19 Apr '16Published on: 19 Apr '16Published in: Ophthalmologica. Journal international d'ophtalmologie. International journal of ophthalmology. Zeitschrift fur Augenheilkunde


To analyze and model visual acuity (VA) in geographic atrophy (GA) secondary to age-related macular degeneration (AMD).The course of VA was analyzed using Turnbull's estimator in 226 eyes with uni- or bilateral GA due to AMD (151 patients; mean age 74.0 ± 7.6 years; mean follow-up time 33.4 ± 23.4 months) from the natural history FAM (Fundus-Autofluorescence Imaging in AMD) study. The variables 'age at baseline', 'gender', 'lesion size', 'diagnosis of the fellow eye', 'status of the fovea', 'focality of the lesion' and 'pattern' were evaluated for effects on predicting VA using linear mixed-effects models.Mean VA at baseline was 0.6 (Snellen 20/80) ± 0.4 logMAR [range -0.1 to 1.8 (20/17 to hand motions)], showing an estimated mean increase of 0.181 (95% CI 0.152-0.210) and 0.256 (0.214-0.300) after 2 and 4 years of follow-up, respectively. The percentage of eyes with a loss of ≥3 lines was 34% by 2 years and 47% by 4 years. Linear mixed model analysis suggested that 65% of VA variability could be explained by the assessed predictor variables. The strongest effect was found for the 'status of the fovea' (0.69 logMAR units between 'definitively spared fovea' and 'definitive foveal involvement', p < 0.001). The second strongest effect was identified for 'total lesion size' (effects between 0.02 and 0.09 logMAR units for each mm depending on foveal involvement, p < 0.001, square root transformed values).These findings underscore the importance of GA lesion characteristics as these have the strongest impact on VA. Natural history data and modeling VA to other variables will be helpful for refining outcome parameters and estimating possible benefits of therapeutic interventions.