Emulating VIEW 1 and VIEW 2 Clinical Trial Outcome Data Using the American Academy of Ophthalmology IRIS Registry

Abstract

Background and Objective: A retrospective, noninterventional cohort study of the American Academy of Ophthalmology IRIS Registry, an electronic health record (EHR)-based comprehensive eye disease and condition registry, intended to assess whether the IRIS® Registry (Intelligent Research in Sight) could emulate the VIEW randomized clinical trials (VIEW RCTs) eligibility criteria, treatment protocol regimen, and primary endpoint. Patients and Methods: Deidentified patients having an anti-VEGF injection of aflibercept or ranibizumab between January 1, 2013, and December 31, 2018, from the IRIS Registry. Patients were treated in accordance with one of three treatment regimens from the VIEW RCT: monthly intravitreal aflibercept injection (IAI 2Q4), intravitreal aflibercept every 2 months after 3 initial monthly doses (IAI 2Q8), or monthly ranibizumab (RQ4) injection. The main outcome measures are the number and proportion of patients meeting VIEW RCT eligibility and treatment group criteria, demographic, and clinical differences between IRIS Registry treatment groups, mean change in best documented visual acuity at one year, and evaluation of the primary endpoint of the VIEW RCT: difference in the proportion of patients maintaining vision. Results: Among the 90,900 patients who met VIEW RCT eligibility criteria, 4,457 (4.85%) met treatment group criteria. The percentage of patients maintaining vision at one year was over 90%. No statistically significant difference was observed when comparing the proportion of patients maintaining vision among the RQ4 treatment group to the IAI 2Q4 or IAI 2Q8 treatment group. Conclusions: A small percentage of real-world patients met VIEW RCT study eligibility criteria and treatment protocol regimen. Among patients meeting all available criteria, the primary endpoint interpretation yielded by an observational EHR-based dataset suggested comparable results to the VIEW RCT. [ Ophthalmic Surg Lasers Imaging Retina 2023;54:6–14.]

Ophthalmology at the Forefront of Big Data Integration in Medicine: Insights from the IRIS Registry Database

“…Again, ophthalmology has been at the forefront of the application of machine learning and deep learning approaches thanks in part to our utilization of noninvasive imaging technology. Deep learning algo-cited clinical trial for treatment of nAMD and was able to produce a study population size capable of emulating the primary endpoint [28]. This highlights another potential utilization of big data -to create synthetic control arms, ie, digital twins, to decrease time and cost of clinical trials.…”

“…This study established a new methodology for using big data and patient level clinical trial data. Another analysis utilized the IRIS registry to replicate a widely cited clinical trial for treatment of nAMD and was able to produce a study population size capable of emulating the primary endpoint [ 28 ]. This highlights another potential utilization of big data – to create synthetic control arms, ie, digital twins, to decrease time and cost of clinical trials.…”