One and Two Year Visual Outcomes from the Moorfields AMD Database - an Open Science Resource for the Study of Neovascular Age-related Macular Degeneration

Objectives: To analyse treatment outcomes and share clinical data from a large, single-center, well-curated database (174 eyes / 6664 patients with 120,756 single entries) of patients with neovascular age related macular degeneration (AMD) treated with anti-vascular endothelial growth factor (VEGF). By making our depersonalised raw data openly available, we aim to stimulate further research in AMD, as well as setting a precedent for future work in this area. Setting: Retrospective, comparative, non-randomised electronic medical record (EMR) database cohort study of the UK Moorfields AMD database with data extracted between 2008 and 2018. Participants: 3357 eyes/patients (61% female). Extraction criteria were >= 1 ranibizumab or aflibercept injection, entry of AMD in the diagnosis field of the EMR, and a minimum of one year of follow-up. Exclusion criteria were unknown date of first injection and treatment outside of routine clinical care at Moorfields before the first recorded injection in the database. Main outcome measures: Primary outcome measure was change in VA at one and two years from baseline as measured in Early Treatment Diabetic Retinopathy Study (ETDRS) letters. Secondary outcomes were the number of injections and predictive factors for VA gain. Results: Mean VA gain at one-year and two years were +5.5+/-0.5 and +4.9+/-0.68 letters respectively. Fifty-four percent of eyes gained >=5 letters at two years, 63% had stable VA (within+/-14 letters), forty-four percent of eyes maintained good VA (>=70 letters). Patients received a mean of 7.7+/-0.06 injections during year one and 13.0+/-0.2 injections over two years. Younger age, lower baseline VA, and more injections were associated with higher VA gain at two years. Conclusion: This study benchmarks high quality EMR study results of real life AMD treatment and promotes open science in clinical AMD research by making the underlying data publicly available.