Integrating genetics, age and imaging to predict treatment outcomes in neovascular age-related macular degeneration: a proof-of-concept study.

To evaluate the feasibility of integrating genetic, imaging, and demographic data for predictive modelling of treatment outcomes in neovascular age-related macular degeneration (nAMD). Proof-of-concept retrospective cohort study with prospective DNA collection. Patients with unilateral nAMD receiving anti-vascular endothelial growth factor (anti-VEGF) therapy on a treat-and-extend regimen at a single tertiary centre were recruited. Polygenic risk scores (PRS) for AMD were derived from genotyping data (NIHR Bioresource). Optical coherence tomography (OCT) biomarkers-intraretinal fluid (IRF), subretinal fluid (SRF), pigment epithelial detachment (PED), and subretinal hyperreflective material (SHRM)-were automatically quantified using a deep learning segmentation model. Predictors of treatment outcomes included PRS, age at first injection, and OCT feature volumes at baseline. XGBoost was used for binary outcomes and linear regression for continuous outcomes, employing five-fold cross-validation. (1) macular dryness (no IRF/SRF) at 24 months, (2) average treatment interval in year 2, and (3) age at first injection. 106 participants were included. The multimodal model integrating age, imaging, and PRS predicted macular dryness at 24 months with AUC = 0.903, outperforming imaging alone (AUC = 0.701). PRS was associated with younger age at first injection (β = –4.69, 95% CI [–8.93, –0.44], P = 0.031) but not with treatment burden (β = –6.39, P = 0.13). Integrating PRS with OCT-derived imaging biomarkers and patient age is technically feasible and improves predictive performance of modelling for anatomical treatment outcomes in nAMD. PRS reflects genetic susceptibility to nAMD and contextualizes the predictive value of imaging biomarkers for treatment response.