Abstract
Background
Shape memory polymers are materials that are manufactured in a certain shape, can be stored in a temporary deformed shape, and then return to – or remember – their original shape upon exposure to external stimuli such as temperature and moisture. This property lends itself to application in endovascular medical devices. Peripheral vasculature embolisation devices incorporating this novel technology have become commercially available and this case series, where the data were collected as part of a post market registry, outlines initial clinical experience with these novel devices.
Results
Eight cases are described in this series. The disease state/conditions for which embolisation was indicated were right common iliac artery aneurysms (n = 3), a type II endoleak into the thoracic aorta following thoracic endovascular aneurysm repair (n = 1), a left inferior gluteal artery aneurysm (n = 1), left internal iliac artery aneurysms (n = 2), and a case of splenomegaly, where splenectomy was planned after the embolisation procedure (n = 1). Target arteries were 5–10 mm in diameter. In each case, at least one IMPEDE Embolization Plug (IMP-Device) of an appropriate diameter was used. All procedures were technically successful and target vessel thrombosis was achieved in all cases. Follow-up imaging available during the 45–90-day data collection timeframe showed sustained vessel occlusion. This case series includes examples of situations commonly encountered when embolising the peripheral vasculature, namely, the use of one or multiple devices in a single vessel and in combination with the use of other embolic devices (e.g., microcoils, gelatin sponge, and PVA particles) in the same case. There were no adverse events related to the specific use of the device.
Conclusions
This small series illustrates the safety and efficacy of this novel sponge-based embolic device for the embolisation of small and medium sized arteries and further experience will demonstrate the utility of the shape memory polymer devices.
Machine learning based approach for shape memory polymer behavioural characterization