A Study to Understand the Usage of an Artificial Intelligence Technology Called DARWEN for Eye Disorders and to Find Out the Factors Influencing Treatment Frequencies in People With nAMD and RVO
NCT Number: NCT06495203
Acronym: AIRSPEED
Status: COMPLETED
Study Type: OBSERVATIONAL
Conditions: Neovascular Age-related Macular Degeneration|Retinal Vein Occlusion
Interventions: DRUG: anti-VEGF treatment
Sponsor: Bayer
Sex: ALL
Age: ADULT, OLDER_ADULT
Enrollment: 1350
Study Design: Observational Model: |Time Perspective: p
Start Date: 2023-11-02
Completion Date: 2024-02-29
Locations: Bayer, Mississauga, Canada

Summary:
This is an observational study in which data already collected from people with the eye disorders below are studied. In observational studies, only observations are made without participants receiving any advice or any changes to healthcare.

Neovascular age-related macular edema (nAMD): an eye disorder caused by the lack of oxygen in the retina. The lack of oxygen leads to the increase of a protein called vascular endothelial growth factor (VEGF). VEGF causes new, weak blood vessels to grow. These vessels can leak fluid or blood into the central part of the retina at the back of the eye (macula). This leads to blurring or a blind spot in the central (straight ahead) vision needed for reading or threading a needle.

Retinal vein occlusion (RVO): an eye disorder where a blood vessel that carries blood away from the retina (vein) becomes blocked. The blocked vein causes a lack of oxygen in the retina which leads to the increase of VEGF and then vision disturbances.

These eye disorders can be treated with a type of medicine called anti-vascular endothelial growth factor (anti-VEGF). Anti-VEGF treatment helps control the growth of new blood vessels in the eye and is given via injection into the eye.

DARWEN AI is a system using artificial intelligence (AI) technology. It has been proven that DARWEN AI can be used to extract data accurately and efficiently in multiple disease areas, including breast cancer, lung cancer, ambulatory care diseases and skin disorders.

The main purpose of this study is to validate if DARWEN AI can extract and sort through information from participants' health records accurately to identify those who need more frequent injections and those who do not.

The secondary purpose of this study is to use DARWEN AI to describe the factors influencing treatment frequencies in participants with nAMD or influencing treatment discontinuation in participants with RVO.

The data will come from the participants' information stored in EyeDoc Electronic Medical Records (EMR) at St. Joseph Health Care in London. Data collected are from January 1, 2009, to May 31, 2023.

Primary Outcome Measures: Validation of DARWEN™ AI., Based on defined objective clinical eligibility criteria, the EMR data set was divided into four cohorts using Pentavere's proprietary AI engine DARWEN™. The following ophthalmology-specific variables were validated against a manual review: diagnosis of nAMD, diagnosis of RVO including CRVO and BRVO, treatment intervals of anti-VEGF treatment grouped as \< Q8 weeks or ≥ Q8 weeks for nAMD patients, discontinuation of anti-VEGF treatment for RVO patients, and the final outputted cohorts. The manual review was performed on a random selection of 25 patients from each of the four cohorts (100 patients total). Two trained manual abstractors classified these patients according to the rules outlined in the objective cohort criteria. They aligned on a consensus for each patient and compared with the DARWEN™cohorts. Pentavere then provided a success rate for the accuracy of the classification., Retrospective data analysis from January 1, 2009 to May 31, 2023.
Secondary Outcome Measures: Summary of baseline demographics reported as number of participants with different categories., Age, sex., Retrospective data analysis from January 1, 2009 to May 31, 2023.|Summary of clinical characteristics reported as number of participants with different categories., Baseline ophthalmology characteristics, ocular comorbidities, other comorbidities, family history, etc., Retrospective data analysis from January 1, 2009 to May 31, 2023.