P222: Innovation in Times of Uncertainty: The OPTIMAL Trials Experience Conducting a Large-Scale Clinical Trial after COVID-19
Poster Presenter
Bruna Sampaio
Manager, Data management
Hospital Israelita Albert Einstein Brazil
Objectives
This work aims to reflect on the changes driven by COVID-19 pandemic to enable the continuity of a pair of blood pressure control trials, the prevalence of those changes post pandemic and their application in the making of the “new” era of thinking, designing, and conducting large-scale trials.
Method
Retrospective revision of study operations data, procedures, and narratives from personnel involved in both trials. Changes were compiled by year and organized according to the issue they were intended to solve, its relations to COVID-19 and the impact on study conduct during and post pandemic.
Results
While many of the investigating sites outpatient visits were restricted or fully interrupted, three study procedures were affected the most by the COVID-19 lockdown: recruitment, subject follow-up and, subsequently, study adherence assessment. Regulatory agencies around the world worked together to expedite much needed changes. Several strategies with varying degrees of assertiveness have been adopted by the OPTIMAL trials, either by amending the study protocol or reframing study operations:
Virtual Follow-up visits – With site shutting down, virtual contact was the alternative to keep study follow-up on track. The percentage of virtual visits during 2020 reached 35%, peaking at 88% in April of the same year. In 2023 (with 10%), this option remained relevant for subjects that would, otherwise, be at risk of lost to followup.
Home Blood Pressure monitoring – Distribution of BP monitor for patients (through sites) and protocol amendment to adapt target to method. In 2021, 2150 monitors were acquired, allowing for 14266 blood pressure self-measurements to be recorded from 2021 to 2023, reaching its maximum of records entered in 2023, with almost 6000 assessments.
Central data oversight – Central report created to assist with the study management, containing aggregated data such as follow-up overdue, queries trends and adherence to study treatment.
Remote monitoring visit – EDC adaptation for remote SDV. Almost 20% of the monitoring visits from 2020 to 2023 were performed remotely, starting with 30% in 2020, the proportion of virtual visit decreased with the end of travel restrictions. Later, in 2023, the resource was used again, when a cost optimization was required for both projects, representing 40% of site IMV performed that year.
Remote enrollment – at home visits or telephone contact for subject enrollment. This strategy affected less than 1% of the total screened subjects. The delayed implementation may be one of the reasons for the low adoption rate.
Conclusion
Affecting investigating sites and subject alike throughout its duration, the COVID-19 pandemic may also have acted as a catalyst for innovations that would otherwise been deemed high-risk. With the continuity of ongoing studies at stake, especially due to lockdowns, rapid thinking as well as rapid adaptation to the changed scenario was crucial for managing and conducting clinical trials in such conditions. From the OPTIMAL trials experience, the pandemic pushed for the adoption of different strategies, that end up being an important part of the studies’ operations going forward. The quick response from regulatory agencies was also of vital importance to move on with those strategies. From that period, investigating and coordinating sites were able to gain experience with virtual approaches for both subject follow-up and IMV visits (for those sites whose infrastructure allowed), presenting as a cost-effective alternative with no less quality, especially when combined with central data analysis. Far from being a temporary work-around, many of the strategies performed during this period served as a laboratory for the new, if not yet decentralized, hybrid reality, as far as the incorporation of virtual approach to pragmatic clinical trials is concerned.
