Dry Eye Disease Real-World Data
Approximately 5-15% of Americans have dry eye disease.
Qdata® can help identify these patients and change the future of managing this condition.
Qdata Dry Eye Disease is the largest dataset of its kind with outcome variables, made up of nearly 25M+ patients with dry eye disease and other ocular surface conditions.
Key Data Elements
Schirmer's Test
Tear Break-Up Time
Visual Acuity
By the Numbers
About Qdata® Dry Eye Disease
Dry eye disease (DED) is a common disease of the ocular surface. It's characterized primarily by insufficient tear production or quality. Producing inadequate tears results in symptoms such as eye discomfort, redness, blurred vision, light sensitivity, paradoxal excessive tearing, and dryness. Severe DED can have a significant impact on visual acuity, daily activities, physical functioning and more. ICD-10 codes alone do not provide a complete picture of DED. Results from tests conducted by an ophthalmologist and stored within electronic health record (EHR) data can help determine the types of DED, severity, clinical outcomes, and provide greater segmentation of patients in real-world settings for research.
Verana Health is the exclusive real-world data curation and analytics partner of the American Academy of Ophthalmology IRIS® Registry (Intelligent Research in Sight). Qdata Dry Eye Disease is a curated, high-quality dataset from the IRIS Registry that provides researchers with information on treatment patterns and outcomes for this patient population.
Qdata Dry Eye Disease by Verana Health is a research-ready, fit-for-purpose data module that can be used to gain better insights into the dry eye disease population. This includes providing information about the severity of DED, the distinction between evaporative or aqueous deficient, and a detailed understanding of patient outcomes through results from the Schirmer's test and tear break-up time (TBUT).
Evidence in Action
Exclusive Coverage of the DED Population
Only found here from the IRIS Registry® (Intelligent Research in Sight).
Granular Stratification of DED Patients
DED patient types can be distinguished based on severity and test results
Detailed Understanding of Patient Outcomes
Schirmer's test and TBUT data offer a clearer picture of DED signs and outcomes
Custom Data Curation
Need data tailored to your exact study design requirements?
Our purpose-built, fit-for-purpose datasets are designed to support rigorous research and confident decision-making. These datasets can be tailored
around custom inclusion and exclusion criteria, in addition to including all standard abstracted data elements.
Ready to Explore the Dry Eye Disease Dataset?
Connect with our team to discuss your research objectives and receive a tailored data overview.