A systematic review of the Woven EndoBridge device-do findings in pre-clinical animal models compare to clinical results?

Background: The Woven Endobridge (WEB) is designed to treat intracranial wide-neck bifurcation aneurysms, preventing subarachnoid hemorrhage. The translational value of animal models used for WEB device testing is unknown. With this systematic review, we aim to identify the existing animal models used in testing the WEB device and compare the efficacy and safety outcomes to those of prospective clinical studies.
Methods: This study was funded by ZonMw: project number 114024133. A comprehensive search was performed in PubMed and in EMBASE via the Ovid interface. The following exclusion criteria were used: 1) not an original full-length research paper, 2) not an in vivo animal study or a human study, 3) no WEB implantation, 4) if in humans: not a prospective study. The SYRCLE risk of bias tool (animal studies) and the Newcastle-Ottawa quality assessment scale for cohort studies (clinical studies) were used to assess risks of bias. A narrative synthesis was performed.
Results: Six animal studies and 17 clinical studies met the inclusion criteria. The rabbit elastase aneurysm model was the only animal model used to assess WEB device performance. Safety outcomes were never reported in animal studies. Efficacy outcomes were more heterogeneous in animal studies than in clinical studies, which could be due to limited external validity of the animal models in terms of aneurysm induction and dimensions. Both animal and clinical studies were predominantly single-arm studies, and were at unclear risk of several types of bias.
Conclusions: The rabbit elastase aneurysm model was the only pre-clinical animal model used to assess WEB device performance. Safety outcomes were not evaluated in animal studies and could therefore not be compared to clinical outcomes. Efficacy outcomes were more heterogeneous in animal studies than in clinical studies. Future research should focus on improving methodology and reporting in order to draw accurate conclusions on the performance of the WEB device.
(© 2023. The Author(s).)