Tool for evaluating real world evidence in treatment of retinal disease

 

Randomized clinical trials have long been held as the gold standard for evidence based medical practices. However, there is a growing consciousness about the value of incorporating data from real world evidence (RWE) into clinical decision making. Information from real world sources such as registries, records, claims databases, observational or longitudinal cohort studies and health surveys are being recognized more and more for their value in clinical practice. These data types include patient groups that might otherwise be omitted due to the strict nature of inclusion and exclusion criteria in randomized clinical trials. Patient groups in real world datasets are diverse and more comprehensive, including comorbid factors and concomitant therapies that can alter overall treatment outcomes. Best used, real world evidence works in concert with randomized clinical trial data to expand upon the endpoints measured and provide the most extensive information relevant to decision making about the most effective treatment strategies. Some of the additional insights gained from including real world evidence include clinical effectiveness and safety, patterns of treatment use, resource utilization, cost, patient adherence and persistence, patient burden, and quality of life outcomes such as treatment satisfaction.

The Value of RWE

The value of real world evidence is especially significant in the field of ophthalmology regarding the treatment of retinal disease. For example, in the treatment of neovascular age-related macular degeneration, real world data has unveiled challenges regarding treatment with the new and highly effective anti-vascular endothelial growth factor (anti-VEGF) agents. Specifically, real world evidence has helped clinicians provide superior and prolonged patient outcomes by striking a harmonious balance between treatment burden and clinical results. Real world evidence has identified that delays in early diagnosis and treatment are common and often result in poorer visual outcomes. So, the data in this patient population supports the use of early treatments and with extended dosing to achieve the best visual outcomes while simultaneously allowing for the most time between injections.

Improvements to clinical practice can be attained when randomized clinical trial data results are combined with real world outcomes, so it stands to reason these complementary treatment outcomes are becoming of more and more interest to clinicians as well as health insurance payers and other stakeholders.

Like randomized clinical studies, the results of real world evidence vary in quality. Bias is possible. Valid interpretation by ophthalmologists is necessary to enable the utilization of real world information in clinical practice. An effort has been put forth by various entities to try and provide guides for the use of real world data. For example, the International Consortium for Health Outcomes Measures defined a minimum set of standardized patient outcomes that should be collected and measured as part of outcomes associated with the treatment of macular degeneration. Similarly, a STROBE (Strengthening The Reporting of OBservational Studies in Epidemiology) checklist was developed, but as its name implies, was focused upon the consistent reporting of data as opposed to its useful interpretation by the clinician. Other small guidelines have also been developed, but are limited to the type of real world evidence that was collected. These standardization attempts speak to the historical lack of consensus on how quality is determined in real world studies. Recently, a comprehensive literature review of tools for real world data interpretation has successfully identified a thoroughly substantiated, highly sensitive and specific framework called the GRACE (Good ReseArch for Comparative Effectiveness) guidelines. This powerful GRACE checklist is a set of high-level questions used to compare the quality of studies, and it is endorsed by the International Society for Pharmacoepidemiology. To further aid in the productive use of this tool, its authors have permitted an adaptation of this checklist specific to the treatment of neovascular age-related macular degeneration, diabetic macular edema, and retinal vein occlusion. This checklist for retinal diseases, based on solid GRACE guidelines, provides a simple and effective tool for quality analysis for use within the ophthalmological community. Link to the tool is provided below. 

Context for Questions

Some background and context information relating to the checklist questions should be considered by the practitioner when using this tool. When comparing study outcomes, such as therapeutic effectiveness in retinal disease, attention should be paid to ensure the duration of follow-up is adequate. Since retinal diseases are often chronic, treatments like anti-VEGF should provide at least a year of follow-up to evaluate the impact on vision. Treatment dosing regimens also vary and might require over a year of follow-up to adequately compare their outcomes. How patients were followed during the study, and drop-out rates and reasons should be included. Reported study outcomes should be provided via an objective standard method of testing as opposed to opinion or observation only. Study outcomes should reflect the usual variety of patients seen in clinical practice. Patients with retinal disease often switch between treatments, so it is appropriate to include them in outcome results as long as the change in therapy is appropriately accounted for. The outcomes should be compared for patients treated during the same time frame, or an acknowledgment of historical data should be included, as therapy advancements are marked. All variables influencing study outcomes should be reported, and confounding variables should be adjusted statistically to reduce the risk of bias.

 

The GRACE Checklist is designed to guide the assessment of observational studies of comparative effectiveness in terms of their quality and usefulness for decision-making.

Link to tool (Download)

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