How EHR and Genomics Data Can Provide a Better Understanding of Inherited Retinal Diseases

In the quest to combat inherited retinal diseases (IRDs) and other vision-related conditions, the integration of multimodal real-world data (RWD) is helping transform ophthalmic research. By merging electronic health record (EHR), imaging and genetic testing data, as well as patient-reported outcomes, Verana Health and the Foundation Fighting Blindness are uncovering critical insights that can help advance the understanding of IRDs, which can lead to improved treatment and patient care.

Real-World Data Can Help Transform IRD Research

This powerful combination of RWD is invaluable for various aspects of IRD research and treatment development, including validating patient populations, characterizing disease progression, and developing predictive models.

1. Validating Genetically-Confirmed IRD Populations

Thanks to broader availability of sponsored genetic testing programs, nearly 300 genes have been linked to IRDs, and genetic testing is now used in nearly two thirds of patients to confirm their molecular diagnosis. In addition to benefiting patients and their families through genetic counseling, tailored clinical care, and access to relevant therapies and clinical trials, genetic testing information is crucial in validating a patient’s clinical diagnosis.

By combining genetic testing information from IRD patient registries with EHR data, life sciences companies can more accurately define IRD patient populations by specific disease (e.g., retinitis pigmentosa, Stargardt disease), and even by specific variant, to better understand prevalence of disease. The combined dataset can be critical in optimizing clinical trial design and speeding trial recruitment.

2. Integrating Genetic and Phenotypic Data to Better Understand IRD Progression 

Beyond genetic insights, integrating phenotypic data, such as ophthalmic images with clinical EHR data enhances the understanding of how genetic mutations manifest in different individuals. This multimodal data provides a nuanced view of disease presentation, revealing subtle phenotypic differences in patient subpopulations.

Combining genetic and deep clinical data, in turn, enables life sciences companies to characterize the natural history of IRDs in patient subgroup populations. By studying disease progression in individuals with specific genetic mutations and deep clinical and phenotypic data, patterns can be identified to better predict the onset and development of symptoms over time, and identify the most impactful molecular targets to inform drug discovery.

Understanding these disease trajectories is critical for designing clinical trials that can demonstrate treatment efficacy in well-defined patient populations and accelerate a therapy’s time to market. With rich datasets that include imaging and EHR data, life sciences companies can draw a detailed picture of how IRDs impact patients, and create a more comprehensive approach to tackling these diseases.

3. Building Diagnostic and Predictive Models

Finally, the integration of these datasets can support the development of advanced diagnostic and predictive models. High-depth clinical and genetic data allows for the creation of algorithms that predict disease onset, progression, and response to treatment. These models have the potential to enable earlier diagnosis, optimize treatment plans, and improve patient outcomes.

In a world where data-driven decisions are becoming increasingly essential, these models can help bring new therapies to market faster, and tailor treatment plans and therapies to individuals to maximize the likelihood of positive patient outcomes..

Partnerships are Key in Identifying Potential IRD Treatments

As the field of ophthalmic research evolves, the collaboration between Verana Health and the Foundation Fighting Blindness is paving the way for more effective IRD treatments. By harnessing the power of combined EHR and genomics data, researchers are making significant strides toward better understanding these conditions and ultimately helping to improve patient outcomes.