PURPOSE: To model the natural course of geographic atrophy (GA) in patients with age-related macular degeneration (AMD). METHODS: Data on the natural course of GA were collected in the multi-center, longitudinal, prospective observational FAM study. The size of GA was measured by autofluorescence scanning laser ophthalmoscopy. The natural course of GA is modelled by two different mixed effect models (MEM). Both models are compared with respect to the correctness of the model assumptions, goodness of fit, and predictive behavior. RESULTS: The linear model results in better prediction, the non-linear model is more in agreement with the model assumptions. The non-linear model fits the data for small and large areas of GA better, while the linear model seems to be more adequate for the medial areas. More data will be needed to study the interplay of both models in more detail. CONCLUSIONS: The natural course of GA varies extremely between individuals. However, reliable factors for the explanation of this variability have so far not been established. MEM are useful for describing "inter-individual" as well as "intra-individual" influences without the need for precise knowledge of the influencing factors. Using MEM to evaluate data on the natural history of GA allows one to derive parameter estimates, which could be used to design interventional trials for modes of therapy with a potential to reduce or stop the progression of GA in patients with AMD.
PURPOSE: To model the natural course of geographic atrophy (GA) in patients with age-related macular degeneration (AMD). METHODS: Data on the natural course of GA were collected in the multi-center, longitudinal, prospective observational FAM study. The size of GA was measured by autofluorescence scanning laser ophthalmoscopy. The natural course of GA is modelled by two different mixed effect models (MEM). Both models are compared with respect to the correctness of the model assumptions, goodness of fit, and predictive behavior. RESULTS: The linear model results in better prediction, the non-linear model is more in agreement with the model assumptions. The non-linear model fits the data for small and large areas of GA better, while the linear model seems to be more adequate for the medial areas. More data will be needed to study the interplay of both models in more detail. CONCLUSIONS: The natural course of GA varies extremely between individuals. However, reliable factors for the explanation of this variability have so far not been established. MEM are useful for describing "inter-individual" as well as "intra-individual" influences without the need for precise knowledge of the influencing factors. Using MEM to evaluate data on the natural history of GA allows one to derive parameter estimates, which could be used to design interventional trials for modes of therapy with a potential to reduce or stop the progression of GA in patients with AMD.
Authors: Annal D Meleth; Pradeep Mettu; Elvira Agrón; Emily Y Chew; Srinivas R Sadda; Frederick L Ferris; Wai T Wong Journal: Invest Ophthalmol Vis Sci Date: 2011-02-28 Impact factor: 4.799
Authors: Zohar Yehoshua; Philip J Rosenfeld; Giovanni Gregori; William J Feuer; Manuel Falcão; Brandon J Lujan; Carmen Puliafito Journal: Ophthalmology Date: 2010-10-29 Impact factor: 12.079
Authors: Janet S Sunness; Eyal Margalit; Divya Srikumaran; Carol A Applegate; Yan Tian; Daniel Perry; Barbara S Hawkins; Neil M Bressler Journal: Ophthalmology Date: 2007-02 Impact factor: 12.079
Authors: Anne S Lindblad; Patricia C Lloyd; Traci E Clemons; Gary R Gensler; Frederick L Ferris; Michael L Klein; Jane R Armstrong Journal: Arch Ophthalmol Date: 2009-09
Authors: Hendrik P N Scholl; Monika Fleckenstein; Lars G Fritsche; Steffen Schmitz-Valckenberg; Arno Göbel; Christine Adrion; Christine Herold; Claudia N Keilhauer; Friederike Mackensen; Andreas Mössner; Daniel Pauleikhoff; Andreas W A Weinberger; Ulrich Mansmann; Frank G Holz; Tim Becker; Bernhard H F Weber Journal: PLoS One Date: 2009-10-12 Impact factor: 3.240