Long-term visual acuity (VA) forecasting after anti-VEGF therapy is important for counseling and follow-up planning in diabetic macular edema (DME), yet remains challenging when only early post-treatment findings are available. While prior OCT-based methods mainly focus on short-term response or single-endpoint prediction, multi-horizon VA forecasting from early longitudinal data remains insufficiently under-explored. In this study, we assembled a real-world cohort of 188 anti-VEGF--treated DME patients with paired baseline and month-1 OCT scans, along with tabular OCT-derived biomarkers and non-imaging clinical variables. Using only these early data, we formulate a multi-horizon VA forecasting problem aimed at predicting visual outcomes at 3, 6, 12, 18, and 24 months, reflecting clinically meaningful follow-up intervals. We propose ReVA, a response-aware multimodal framework that combines baseline and month-1 OCT features with tabular variables to capture disease status and early treatment response. ReVA integrates spatial OCT attention, dependency-aware tabular encoding, and cross-modal fusion to predict patient-specific long-term VA trajectories. The proposed framework achieves MAE=0.1246, RMSE=0.1621, and R^2=0.6064 for 24-month VA prediction, with consistent performance across all forecast horizons. Our findings show that incorporating early treatment-response signals enables clinically meaningful long-term visual acuity forecasting, supporting data-driven decision support for routine anti-VEGF management. Code and pretrained models will be released on https://github.com/nguyenpbui/ReVA.

翻译：长期抗VEGF治疗后的视力（VA）预测对于糖尿病黄斑水肿（DME）患者的咨询和随访计划至关重要，但仅基于早期治疗后的发现仍具挑战性。尽管既往基于OCT的方法主要关注短期反应或单一终点预测，利用早期纵向数据进行多时间跨度的VA预测仍未得到充分探索。本研究收集了188例接受抗VEGF治疗的DME患者真实世界队列，包含基线及第1月的OCT扫描数据，以及OCT衍生的表格化生物标志物和非影像临床变量。仅利用这些早期数据，我们构建了多时间跨度VA预测问题，旨在预测3、6、12、18及24个月时的视觉结局，对应临床有意义的随访间隔。我们提出ReVA，一种治疗应答感知的多模态框架，结合基线及第1月的OCT特征与表格变量，以捕获疾病状态和早期治疗反应。ReVA整合空间OCT注意力机制、依赖感知的表格编码及跨模态融合，预测患者特异性长期VA轨迹。所提框架在24个月VA预测中实现MAE=0.1246、RMSE=0.1621及R²=0.6064，且在所有预测时间跨度均保持一致性。研究结果表明，纳入早期治疗反应信号可实现临床有意义的长期视力预测，为常规抗VEGF管理提供数据驱动的决策支持。代码与预训练模型将发布于https://github.com/nguyenpbui/ReVA。