
ASRS 2026: Partial EZ attenuation predicts intermediate AMD progression
A wider partial-total ellipsoid zone gap on OCT predicted faster total EZ loss and geographic atrophy in intermediate AMD—pointing to a biomarker for trial enrichment.
An imaging biomarker that flags early photoreceptor damage could help retina specialists identify which patients with
Ehlers, a retina specialist at the Cole Eye Institute, Cleveland Clinic, and colleagues assessed partial ellipsoid zone (EZ) attenuation—outer segment thinning—as a predictor of total EZ loss and
Study design and findings
The retrospective analysis included 512 eyes with intermediate AMD at baseline and 2-year follow-up. Certified readers segmented spectral-domain optical coherence tomography (SD-OCT) scans, and investigators quantified the partial-total EZ gap as the absolute difference between areas of partial (EZ-RPE thickness ≤20 µm) and total (EZ-RPE thickness = 0 µm) attenuation, then sorted eyes into quartiles.1
The median baseline gap measured 1.58 mm² in GA progressors versus 0.10 mm² in stable eyes (P <.001). Across quartiles, 2-year total EZ loss rose from 0.00 mm² to 1.14 mm², and GA conversion climbed from 0% to 44%. ROC analyses identified gap cutoffs of 0.70 mm² for predicting rapid total EZ progression (AUC = 0.91; sensitivity = 0.96; specificity = 0.75) and 0.44 mm² for predicting GA development (AUC = 0.85; sensitivity = 0.83; specificity = 0.73).1
What it means for clinical care
Enriching trials with eyes that carry a wider gap could isolate the patients most likely to progress and speed readouts on whether a therapy protects photoreceptors, Ehlers said. He noted no proven treatment yet exists for intermediate AMD beyond over-the-counter supplementation, which sharpens the focus on surveillance—he now images some higher-risk patients every 3 to 6 months rather than annually. Qualitative biomarkers, from hyperreflective foci to EZ loss, are "already here" for any retina specialist reading an OCT, Ehlers said, but quantitative measurement will require automated, embedded, or cloud-based AI segmentation that is not yet routine.






















