Retinitis Pigmentosa Real-World Data

100,000 people in the U.S. are living with retinitis pigmentosa. Qdata® can help identify this patient population and change the future for those with inherited retinal diseases.

Qdata Retinitis Pigmentosa (RP) is a novel real-world dataset that can accurately analyze patients with retinitis pigmentosa and associated clinical outcomes through unstructured clinical notes.

About Qdata Retinitis Pigmentosa

Retinitis pigmentosa (RP) is a group of eye disorders caused by variations in more than 70 genes that affect the retina and are passed down from parent to child. RP makes cells in the retina break down over time, causing progressive vision loss. Onset of RP can occur at childhood or adulthood, and the first sign is usually loss of night vision. Retinitis pigmentosa is an inherited retinal disease (IRD), which is a rare, genetic disorder that can cause severe vision loss and blindness. 

ICD and SNOMED codes alone do not provide an accurate way to identify patients with RP. Data from electronic health records (EHRs), such as clinical notes and ophthalmic images, provide a more accurate way to analyze this patient population. 

Qdata Retinitis Pigmentosa is a curated, high-quality dataset from the American Academy of Ophthalmology IRIS® Registry (Intelligent Research in Sight) that provides an accurate way to mine EHR data to analyze patients with this inherited retinal disease, as well as their associated clinical outcomes through clinical notes and images. 

Qdata Retinitis Pigmentosa by Verana Health is a research-ready, fit-for-purpose real-world data module that can be used to analyze patients with RP and track their outcomes. These outcomes are derived from unstructured and semi structured fields in EHRs and include visual acuity and central subfield thickness.

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Exclusive Coverage of this Patient Population

Only available here from the IRIS Registry

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Accurately Analyze Patients with Retinitis Pigmentosa

Using clinical notes and ophthalmic images

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Detailed Understanding of Patient Outcomes

Visual acuity and central subfield thickness curated from semi-structured fields

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By the Numbers

K+

De-identified patients with retinitis pigmentosa confirmed from notes*

Years

Median length of follow-up

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*Defined as RP patients confirmed from notes with a structured IRD code or RP-related keyword, between January 2013 and July 2025. 

**Future iterations will leverage additional data sources, such as images and genetic information, to further refine RP patient identification.

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Key Variables in Retinitis Pigmentosa Research

Qdata Retinitis Pigmentosa is a dataset that accurately analyzes patients with retinitis pigmentosa (RP) and provides insights on patient outcomes. Verana Health provides curated variables extracted from EHR data to be utilized based on your research needs, including:

Visual Acuity

Measures how sharp your vision is at a distance; usually tested by reading an eye chart, and is an outcome measure from EHR semi-structured fields.

Central Subfield Thickness (CST)

Measures the thickness of the central part of the retina, typically determined using optical coherence tomographic imaging. CST is an outcome measure from EHR semi-structured fields.

Unstructured EHR Data and Images Can Classify Patients with Retinitis Pigmentosa

Artificial intelligence-powered techniques, such as machine learning and natural language processing, can extract key terms and phrases from unstructured EHR clinical notes, as well as classify patients via ophthalmic images to accurately identify the retinitis pigmentosa (RP) patient population. These techniques enable life sciences companies to more accurately classify this patient cohort for research beyond using ICD or SNOMED codes. In the future, these techniques can be applied to help analyze patients with other inherited retinal diseases.

With Qdata Retinitis Pigmentosa, life sciences companies can more easily identify this population, as well as evaluate patient outcomes using key outcomes, such as visual acuity and central subfield thickness.

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of Medical Data Remains Unstructured

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