Retinal fluid quantification using a novel deep learning algorithm in patients treated with faricimab in the TRUCKEE study.

Abstract

Background: Investigate retinal fluid changes via a novel deep-learning algorithm in real-world patients receiving faricimab for the treatment of neovascular age-related macular degeneration (nAMD).

Methods: Multicenter, retrospective chart review and optical coherence tomography (OCT) image upload from participating sites was conducted on patients treated with faricimab for nAMD from February 2022 to January 2024. The Notal OCT Analyzer (NOA) algorithm provided intraretinal, subretinal and total retinal fluid for each scan. Results were segregated based on treatment history and fluid compartments, allowing for multiple cross-sections of evaluation.

Results: A total of 521 eyes were included at baseline. The previous treatments prior to faricimab were aflibercept, ranibizumab, bevacizumab, or treatment-naive for 52.3%, 21.0%, 13.3%, and 11.2% of the eyes, respectively. Of all 521 eyes, 49.9% demonstrated fluid reduction after one injection of faricimab. The mean fluid reduction after one injection was -60.7nL. The proportion of eyes that saw reduction in fluid compared to baseline after second, third, fourth and fifth faricimab injections were 54.4%, 51.9%, 51.4% and 52.2%, respectively. The mean (SD) retreatment interval after second, third, fourth and fifth faricimab injection were 53.4 (34.3), 56.6 (36.0), 57.1 (35.3) and 61.5 (40.2) days, respectively.

Conclusion: Deep-learning algorithms provide a novel tool for evaluating precise quantification of retinal fluid after treatment of nAMD with faricimab. Faricimab demonstrates reduction of retinal fluid in multiple groups after just one injection and sustains this response after multiple treatments, along with providing increases in treatment intervals between subsequent injections.