Determinants of Quality of Life in Geographic Atrophy Secondary to Age-Related Macular Degeneration

Abstract

Purpose: To longitudinally evaluate vision-related quality of life (VRQoL) in geographic atrophy (GA) secondary to age-related macular degeneration (AMD) and define its relation to visual function and structural biomarkers.

Methods: Patients with GA secondary to AMD were recruited in the context of the prospective, non-interventional, natural-history Directional Spread in Geographic-Atrophy study (NCT02051998). Fundus autofluorescence and infrared reflectance images were semi-automatically annotated for GA. Linear mixed-effects models were applied to investigate the association of putative determinants with the National Eye Institute Visual Function Questionnaire 25 (NEI VFQ-25) VRQoL.

Results: A total of 87 patients with a mean age ± SD of 77.07 ± 7.49 years were included in the analysis. At baseline, median (IQR) best-corrected visual acuity (BCVA) was 0.3 (0.51) for the better eye and 0.89 (0.76) for the worse eye; 46% of the patients showed binocular and 25.3% monocular non-central GA. The VRQoL composite score was impaired: 69.96 (24.03). Sixty-six patients with a median of 2 (2) follow-up visits after 1.08 (0.78) years were examined longitudinally.

Conclusions: Vision-related quality of life is significantly impaired in patients with GA secondary to AMD. The cross-sectional and longitudinal association of VRQoL with visual functional and structural biomarkers supports the validity of the NEI VFQ-25 VRQoL.

Conflict of interest statement

Disclosure: S.H. Künzel, Heidelberg Engineering (F), Optos (F), Carl Zeiss Meditec (F), CenterVue (F), Novartis (F); P.T. Möller, Heidelberg Engineering (F), Optos (F), Carl Zeiss Meditec (F), CenterVue (F), Novartis (F); M. Lindner, Heidelberg Engineering (F), Optos (F), Carl Zeiss Meditec (F), CenterVue (F), Novartis (F); L. Goerdt, Heidelberg Engineering (F), Optos (F), Carl Zeiss Meditec (F), CenterVue (F), Novartis (F); J. Nadal, None; M. Schmid, None; S. Schmitz-Valckenberg, Acucela (F), Alcon/Novartis (C, F, R), Allergan (C, F, R), Bayer (F, R), Bioeq/Formycon (F, C), Carl Zeiss MedicTec (F, R), CenterVue (F), Galimedix (C), Genentech/Roche (F, R), Heidelberg Engineering (F), Katairo (F), Optos (F); F.G. Holz, Acucela (C, F, R), Allergan (F, R), Apellis (C, R), Bayer (C, F, R), Boehringer-Ingelheim (C), Bioeq/Formycon (F, C), CenterVue (F), Ellex (R), Roche/Genentech (C, F, R), Geuder (C), Grayburg Vision (C, R), Heidelberg Engineering (C, F, R), Kanghong (C, F), LinBioscience (C, R), NightStarX (F), Novartis (C, F, R), Optos (F), Pixium Vision (C, F, R), Oxurion (C, R), Stealth BioTherapeutics (C, R), Zeiss (F, R); M. Fleckenstein, Novartis (F, C), Heidelberg Engineering (F), STZ GRADE Reading Center (E), Genentech/Roche (C), Ophthalmo Update GmbH (C), pending patent US20140303013A1; M. Pfau, Heidelberg Engineering (F), Optos (F), Carl Zeiss Meditec (C, F), CenterVue (F), Novartis (F)

Figures

Figure 1.

Data distribution. The histograms show the cross-sectional distribution of age (A), VRQoL composite score (B), near vision subscore (C), distant vision subscore (D), binocular reading acuity (E), and BCVA of the better eye (F). The solid vertical line denotes the median value. Please note, the Radner reading acuity (B) score exhibits a marked floor effect and that, due to the design of the questionnaire, some values in C and D could not be attained.

Figure 2.

Cross-sectional determinates of vision-related quality of life. All plots show VRQoL composite scores on the y-axis and the corresponding determinants on the x-axis. The dashed green lines show the univariable linear regression lines. The shown R2 estimates were obtained from models fit to the complete data (cross-validated R2 estimates are provided in Table 2).

Figure 3.

Cross-sectional multivariable analysis of determinants of VRQoL. Dot plots of regression coefficients derived from the training splits for each of the outer cross-validation folds are shown for the composite score (A), near vision subscore (C), and distant vision subscore (E). Cross-validation within these training splits (inner resampling) was used to determine the optimal tuning parameter λ of the LASSO regression model. Note that the points were plotted semitransparently to avoid overplotting. The green vertical lines indicate the mean coefficient. A zero coefficient estimate effectively implies exclusion of the respective variable from the LASSO regression model. The coefficients dependent on tuning parameter λ for the complete dataset are shown for the composite score (B), near vision subscore (D), and distant vision subscore (F). The dashedgrayline indicates the optimal value of tuning parameter λ derived from the inner resampling. Biomarkers of the better eye are highlighted in blue and biomarkers of the worse eye in red.

Figure 4.

Longitudinal change of VRQoL. All plots show changes in VRQoL composite scores on the y-axis and changes in the corresponding determinants on the x-axis. The dashed green lines show the fitted linear regression lines derived from mixed-effects models with consideration of patients as a random factor. None of the features exhibited a significant change over the given follow-up time.

Figure 5.

Example patients. The figure shows the FAF and semiautomatically graded GA (blue areas) of three patients. The BCVA and LLVA (logMAR) and the reading acuity (logRAD) are shown. Patient A had high VRQoL scores and good visual function in both eyes. Patient B had low VRQoL scores and poor visual function in both eyes. Patient C had high VRQoL scores but only one eye with good function.