University of Wisconsin–Madison

Characterizing Intermediate AMD Biomarkers on OCT: Methods and Reproducibility

Research poster titled “Characterizing Intermediate AMD Biomarkers on OCT: Methods and Reproducibility” presented at ARVO 2025 by Heathcote, et al. Jennifer Heathcote, Jordan Winkler, Jeong W. Pak, Rick Voland, Barbara Blodi, Amitha Domalpally

Abstract

Purpose: To define evaluation criteria and assess intergrader agreement for intermediate Age-related Macular Degeneration (iAMD) optical coherence tomography (OCT) biomarkers in eyes with nonexudative AMD.

Methods: Trained graders independently evaluated Heidelberg OCT volume scans (20°x20°, 97 B scans) in eyes with intermediate AMD enrolled in the AREDS2 study at UW Madison. Graders assessed the presence of high risk markers: large drusen, hyporeflective core drusen, hyperreflective foci (HRF), reticular pseudodrusen (RPD), nascent geographic atrophy (nGA), incomplete retinal pigment epithelium and outer retinal atrophy (iRORA), and outcomes including complete retinal pigment epithelium and outer retinal atrophy (cRORA), and hypertransmission defect (HTD). Graders also assessed the area of iRORA, cRORA, HTD, ellipsoid zone (EZ) loss, and RPD, as well as HRF count. Reproducibility of presence was analyzed as exact agreement with kappa value and area was compared by Intraclass correlation (ICC).

Results: Of 37 eyes from 28 participants, large drusen were present in all eyes (100%) with hyporeflective core drusen in 21 (57%), HRF in 34 (92%) with mean count (SD) as 7.24 (8.98), RPD in 12 (32%) with mean area (SD, mm2) as 4.05 (10.3), nGA in 14 (38%), iRORA in 13 (35%) with mean area (SD) as 0.012 (0.022), cRORA in 14 (38%) with mean area (SD) as 0.173 (0.407), HTD in 16 (43%) with mean area (SD) as 0.232 (0.508), and mean area of EZ loss (SD) was 0.841 (1.164). Table 1 shows the intergrader agreement on these OCT biomarkers.

Conclusions: Trained graders can evaluate iAMD OCT biomarkers with high reproducibility. Establishing reproducible ground truth evaluation is important for eligibility assessment or structural endpoints in iAMD clinical trials for potential treatments and further, developing AI algorithms.