Key Takeaways

• Inferred sensitivity mapping predicts retinal function in GA with high spatial resolution, avoiding extensive psychophysical testing.
• Current GA treatments slow progression but lack functional benefits; traditional metrics fail due to foveal sparing.
• Machine-learning models, especially random forest, accurately predict retinal sensitivity and reduce test variability.
• This approach could improve clinical trial designs and natural history studies for retinal diseases.

Data from the recent OMEGA study¹ showed that “inferred sensitivity” mapping may be a potential functional surrogate end point for geographic atrophy (GA) based on its ability to predict retinal function with high spatial resolution and without extensive psychophysical testing, according to Georg Ansari, MD, first author of the report published in Investigative Ophthalmology and Visual Science. He is from the Institute of Molecular and Clinical Ophthalmology Basel and the Department of Ophthalmology, University Hospital Basel in Switzerland.

Currently, the treatments available for GA in the US––pegcetacoplan (Syfovre; Apellis Pharmaceuticals) and avacincaptad pegol (Izervay; Astellas Pharma US)––slow the disease progression. However, no treatment with a prospectively established functional treatment benefit is available.^2-4

“The challenge of selecting appropriate functional outcome measures for GA in treatment trials remains significant,” the researchers said.

While the best-corrected visual acuity (BCVA), low-luminance VA, and dark adaptometry have been evaluated as functional metrics, they have failed “to effectively capture the progression of GA, primarily because GA may form in the parafovea, leading to foveal sparing. This phenomenon results in patients maintaining high VA despite having substantial parafoveal scotomas. Consequently, traditional measures such as the BCVA are insufficient for accurately assessing the functional impact of GA,”^5,6 Ansari and colleagues commented…..

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Source: Ophthalmology Times

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