Evaluation of Clinical Progression, Prognostic Factors, and Quality of Life in Patients with Age-related Macular Degeneration.

The VERA-AMD study is a prospective, non-interventional, observational study investigating the clinical course, prognostic factors, and quality of life in patients with age-related macular degeneration (AMD). AMD is a leading cause of vision loss among the elderly, manifesting as either neovascular ("wet") AMD (nAMD) or geographic atrophy ("dry") AMD (GA-AMD). Despite advances in treatment, there is considerable variability in disease progression and response to therapy. This study aims to fill this gap by analyzing real-world data to identify predictors of disease progression and functional outcomes, ultimately improving clinical management strategies.

Study Objectives

The study's main objectives are to:

Assess the clinical progression of AMD in routine clinical practice. Identify prognostic factors associated with favorable or unfavorable disease outcomes.

Evaluate the impact of AMD on patient quality of life using validated questionnaires.

Methodology

Design: Prospective, non-profit, observational study. Population: 200 patients aged ≥50 years with confirmed AMD in at least one eye. Inclusion Criteria: Confirmed diagnosis of AMD, scheduled follow-up according to clinical practice.

Exclusion Criteria: Significant ocular or systemic diseases affecting visual outcomes.

Outcomes

Primary Outcome: Progression rate to advanced AMD, expressed as cases per 100 patient-years.

Secondary Outcomes: Changes in visual acuity, retinal morphology assessed by optical coherence tomography (OCT), and patient-reported quality of life.

Anti-VEGF Therapy and AI-Based Fluid Analysis

A significant part of the study focuses on the role of anti-VEGF treatment in controlling disease progression, especially in nAMD. Anti-VEGF agents such as aflibercept and ranibizumab are standard treatments for neovascular AMD, aiming to reduce fluid accumulation and neovascularization. The study will explore the relationship between treatment regimens (fixed vs. as-needed dosing) and clinical outcomes, including visual acuity and anatomical changes.

Artificial intelligence (AI)-based analysis of retinal fluid dynamics will be employed using OCT imaging data. Machine learning algorithms will be applied to identify patterns in fluid accumulation and disease progression, improving early detection and personalized treatment approaches.

Data Collection and Statistical Analysis

Data will be collected through regular clinical visits, OCT imaging, and visual acuity testing. Statistical analysis will include repeated measures models to evaluate disease progression and predictors of clinical outcomes. Sample size calculations anticipate a 5.2% annual progression rate in early/intermediate AMD and a 19.7% rate in advanced AMD, with an 80% statistical power at a 0.05 significance level.

Timeline

Interim analysis after 12 months (50 patients with follow-up). Primary analysis after 12 months (all patients). Follow-up analysis at 36 months. Consent and Ethics

Informed consent will be obtained from all participants. Data will be anonymized and handled according to ethical guidelines and institutional review board (IRB) protocols.

• Self-reported vision-targeted quality of life [Time frame: through study completion, 36 months]
• Fluid volume (nL) in the retinal compartments [Time frame: through study completion, 36 months]
• central retinal thickness [Time frame: through study completion, 36 months]