Modeling Progression in Terms of Visual Loss in Geographic Atrophy Secondary to Age-related Macular Degeneration

Abstract

 

Purpose

 

To analyze and model the disease progression in terms of visual acuity (VA) loss in patients with geographic atrophy (GA) secondary to age-related macular degeneration (AMD).

 

Methods

 

A total of 226 eyes of 151 patients (63.6% women, mean age at baseline 74.0 ± 7.6 years; median follow-up time 808 days [IQR 940 days]) participating in the prospective natural history FAM (Fundus Autofluorescence in AMD)-Study with uni- or multifocal GA secondary to AMD and at least six months of serial standardized examinations were analyzed. Data assessment included ETDRS best-corrected central visual acuity, routine ophthalmological examination and confocal scanning laser ophthalmoscopy fundus autofluorescence (FAF) and infrared reflection imaging. Total size of atrophy was determined using semi-automated image analysis software. The course of VA was analyzed using a linear mixed-effects model with LogMAR as the response variable. Predictor variables included age at baseline, gender, time, GA size, diagnosis of the fellow eye, foveal involvement of GA, focality of the GA lesion and FAF pattern.

 

Results

 

At baseline, median VA was 0.5 [IQR 0.7] LogMAR. There was high variability of VA among patients and study eyes. Results from linear mixed model analysis suggested that approximately 65% of the variance of the LogMAR values could be explained by the assessed predictor variables. Variables with significant effect on VA (likelihood ratio tests) included time, with an estimated overall time trend of 0.029 units of LogMAR increase per year (p = 0.012) and GA size, with increase of the average LogMAR value by 0.06 units with each mm (square-root transformed values) (p < 0.001). Furthermore, age at baseline (increase of 0.00467 LogMAR units per year (p = 0.041)) and foveal involvement of GA (0.86 LogMAR units lower in eyes with definitively ‘spared fovea’ as compared to definitive foveal involvement (p<0.001)) had a significant effect on VA loss.

 

Conclusions

 

Variance of VA in eyes with GA secondary to AMD can be widely explained by known variables which include time, GA size, age at baseline and foveal involvement of GA. Modeling the course of VA with identification of influencing factors will help to estimate the patient specific disease progression and to better appraise potential therapeutic effects on VA in interventional trials on GA.