Abstract Body: Introduction
Advancements in neuroendovascular surgery have revolutionized treatment of acute stroke and neurovascular diseases, with many procedures performed via transradial approach (TRA). Success relies on timely navigation of devices through the vasculature to the area of interest. However, this is often complicated by device herniation, a phenomenon in which the interventional system shifts from the intended arterial pathway, back into the aortic arch, preventing advancement. This is typically irrecoverable and requires restarting the procedure, risking vascular trauma and delaying intervention. The mechanistic steps underlying successful endovascular navigation vs herniation are not well understood.

Methods
A type-one 2.5D aortic arch model was designed. Endovascular access was simulated from the right radial artery to the left common carotid artery (LCCA). A flow system with physiological pulse and pressure parameters was connected (Fig 1). Commonly used catheters and guidewires were employed in the standard fashion and modeled to calculate the bending energy profiles associated with herniation and successful navigation. Next, this model was applied to new device combinations to predict outcomes by comparing the energy profiles associated with herniation vs success.

Results
Key mechanistic steps underlying herniation and successful navigation were revealed, including “initial” navigation through the radial artery to the LCCA, followed by “dropdown” of the catheter into the aortic arch, and “squeeze” as the catheter was advanced further and confined to the limits of the vessel walls. Depending on the mechanics of the catheter and guidewire, the system either herniated (Fig 2) or successfully navigated (Fig 3). Comparison of bending energy profiles associated with herniation and success for a given device combination accurately predicted outcome, as it followed the most energetically favorable path.

Conclusions
Herniation and successful navigation of neuroendovascular devices through TRA are associated with distinct mechanistic stages, characterized by specific energy profiles and patient anatomy. This novel testing framework and improved understanding of device herniation enables prediction of herniation risk and offers unique insight into testing models, opportunities to improve device design, and aids clinical decision making.