Johnson & Johnson announced that eight company-sponsored presentations will be featured during the Association for Research in Vision and Ophthalmology (ARVO) 2024 Annual Meeting  taking place in Seattle from May 5?9, 2024. The Company's two oral presentations will include one highlighting a real-world analysis on the economic value of early genetic testing in patients with inherited retinal diseases (IRDs), a group of rare eye disorders that can lead to serious vision impairment (Abstract #2154), and the second evaluating the role of automatic deep-learning based algorithms to measure precursors of geographic atrophy (GA), a late-stage and severe form of age-related macular degeneration (AMD) (Abstract #2770). Additionally, Johnson & Johnson will spotlight its EYE-RD Global Registry at the meeting, a first-of-its-kind observational, non-interventional global IRD registry created to make clinical information on IRDs more accessible to patients, providers, payers, and researchers.

The registry will serve as a centralized repository of longitudinal data collected on genetically tested patients who are diagnosed or have a suspected diagnosis of IRDs such as X-linked retinitis pigmentosa, a rare IRD estimated to impact one in 40,000 people globally. The registry has the potential to bridge the knowledge gap in IRDs through the collection of real-world data by collating more holistic insights about disease progression and patient experiences. Key results from two oral presentations include: Economic Value of Early Genetic Testing in Inherited Retinal Dystrophies Diagnosis (Abstract #2154).

Patients with delayed genetic testing incurred significantly higher healthcare costs than those with early genetic testing ($76,838 vs. $13,084 in total costs, respectively). Genetic testing is often delayed due to lack of awareness or cost.

The data reinforces the importance of early testing to potentially help improve IRD diagnosis and lower overall healthcare costs. Deep-Learning Based Algorithm for Automatic cRORA and Photoreceptor Loss Detection in Spectral Domain Optical Coherence Tomography (SD-OCT) Imaging (Abstract #2770). Due to slow and variable GA progression in patients, it can be challenging to measure the efficacy of treatments in clinical trials, often leading to long trial durations and large sample sizes, which could delay the availability of potential new therapies.

Automatic deep-learning based segmentation of multiple SD-OCT imaging biomarkers could potentially provide a time-saving and cost-effective method to quantify and predict disease progression and help clinicians more rapidly determine the efficacy of treatments in clinical trials.