DIABETIC RETINOPATHY

1 in 4 Americans Over 40 with Diabetes have Diabetic Retinopathy. Qdata® Can Help Improve Their Treatment.

Qdata Diabetic Retinopathy is an exclusive dataset of 2.1M+ patients with a detailed view of the patient journey including disease progression to Diabetic Macular Edema (DME), Anti-VEGF treatment details, and more.

About Qdata

Qdata® by Verana Health are research ready, fit-for-purpose data modules designed to confidently drive business insights and inform research outcomes. Qdata spans ophthalmology, neurology, and urology to help unlock quality insights across the entire drug and device development cycle.

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Exclusive Diabetic Retinopathy Insights

Eye

Comprehensive View into the Patient Journey

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Detailed View of Anti-VEGF Treatment

Only found here from the IRIS® Registry (Intelligent Research in Sight)

Key variables including visual acuity and CST

Across diabetic retinopathy and DME patients

Qdata_Diabetic_Retinopathy

By the Numbers

M+

Total de-identified patients with diabetic retinopathy (without DME)*

K+

Total de-identified patients with diabetic retinopathy with DME**

+ Years

Average length of follow-up for diabetic retinopathy patients

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*Defined as patients >=18 with >=1 ICD-10 code for Diabetic Retinopathy from January 2016 to September 2023, with a corresponding CPT or HCPCS code on the same date as the diagnosis.

**Patients are not mutually exclusive

Meet with a Qdata Expert or Request a Data Count

Key Variables in Qdata Diabetic Retinopathy

Curated variables extracted from EHR data and/or images to be utilized based on your research needs.

Visual Acuity

Central Subfield Thickness (CST)

Unstructured Data Enables Deeper Understanding

Qdata modules consist of carefully curated datasets that are readily available for research purposes, enabling the generation of real-world evidence (RWE).

Verana Health applies clinician-informed algorithms to unstructured EHR data at scale to help extract key information on visual acuity, CST, anti-VEGF procedure details and more, which can then be analyzed across large populations in a reliable manner.

This can give life sciences companies unprecedented insights to better understand treatment patterns and outcomes, run comparative effectiveness studies, monitor market uptake, and more.

0%

of Medical Data Remains Unstructured

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