Why Real-World Data is Key to Providing Insights Into Retinitis Pigmentosa

Retinitis pigmentosa (RP), a group of inherited retinal diseases (IRDs) that affects an estimated 100,000 people in the United States, is a condition that progressively robs individuals of their vision. RP is caused by variations in genes that lead to the gradual breakdown of retinal cells, resulting in vision loss over time. 

The journey for patients with this rare genetic disorder often begins with night vision problems and eventually progresses to severe vision loss. Though rare, the impact of RP is profound, and identifying patients affected by this condition has been difficult.

However, with advanced technology and real-world data (RWD), we can transform the future of  retinitis pigmentosa research and improve patient outcomes.

The Challenge of Identifying Retinitis Pigmentosa Patients

Traditional methods for identifying patients with retinitis pigmentosa (RP), such as using ICD and SNOMED codes, have proven insufficient. The complexity of RP—with its genetic variations and progressive nature—means that coding systems alone cannot reliably categorize the full spectrum of affected patients. 

Recently, the Verana Health Medical and Quantitative Sciences teams collaborated to develop Qdata^® Retinitis Pigmentosa, a research-ready dataset of 70,000 patients with retinitis pigmentosa confirmed from unstructured clinical notes within electronic health records (EHRs) to help describe patients—beyond coding—for a more accurate view of the patient population and disease progression. This unique dataset includes a median of 3.6 years of follow-up data. The Verana Health team is also working to incorporate other RWD sources, such as ophthalmic images, to help confirm diagnosis.

The Power of Real-World Data (RWD) for Retinitis Pigmentosa Research

By employing artificial intelligence techniques, such as machine learning and natural language processing, we can unlock RWD insights for retinitis pigmentosa research and key variables that are critical for helping life sciences companies understand disease progression.

Qdata Retinitis Pigmentosa is part of Verana Health’s Ophthalmology Qdata collection and is made possible through an exclusive partnership with the American Academy of Ophthalmology (Academy) IRIS^Ⓡ Registry (Intelligent Research in Sight)—the largest specialty society clinical data registry in all of medicine. 

By employing artificial intelligence techniques, such as machine learning and natural language processing, we can unlock RWD insights for retinitis pigmentosa research and key variables that are critical for helping life sciences companies understand disease progression. For example, variables such as visual acuity and central subfield thickness are extracted from semi-structured fields in EHRs, providing researchers with a rich dataset for clinical studies.

Additionally, ophthalmic images are another source of unstructured data that can be tapped into for patient identification. Artificial intelligence can be applied to ophthalmic images, specifically optical coherence tomography (OCT) and fundus autofluorescence (FAF), to categorize whether a patient has RP at scale.

Changing the Future for Patients: Retinitis Pigmentosa Research and Real-World Evidence

As the life sciences industry continues to embrace real-world data and real-world evidence in drug development, Qdata Retinitis Pigmentosa will be essential for accurately describing this patient population to optimize clinical trials, understand disease progression, and ultimately improve patient outcomes.

For the patients and families affected by RP, this development represents a step forward in the quest for better treatments—and one day—hopefully, a cure.

To learn more about Qdata Retinitis Pigmentosa, click here.