Predictive model for individualized stent placement to improve patient outcomes.
JHU Ref #: C17923
Value Proposition:
• Technology generates individualized prediction of graft success for a given patient
• Model performs an evaluation of stent design (i.e. porosity, material) and predicts individualized flow dynamics measurements
• Assesses the need for anticoagulation therapy in each patient
Technology Description
Researchers at Johns Hopkins have developed a predictive model for aneurysmal occlusion resulting from placement of flow diverting stents. This model utilizes flow dynamics and can estimate the potential for thrombotic occlusion and graft failure. Further, this model can be used to select the appropriate flow diverting stent based on patient-specific imaging data.
Unmet Need
Flow diverting stents are a rapidly expanding market for the treatment of abdominal and thoracic aneurysms, but selection of the appropriate stent for treatment requires careful consideration of complex factors that are often patient-specific. Current simulation tools can estimate the placement and anticipate the behavior of these stents prior to implantation. However, they do not consider flow dynamics and fail to predict graft outcomes after implantation. Therefore, there is a strong need for a predictive model to evaluate stent selection and the resulting risk of aneurysmal occlusion.
Stage of Development
• All elements of the software have been developed.
• Initial testing has begun on a small cohort of patient-specific cases.
Data Availability
Data available upon request.
Publication
Alamlah et al. Computational Modeling and Analysis of Flow Diverter Stents for Cerebral Aneurysm. Bulletin of the American Physical Society (2023).
W02025106976A1 application: A method and system for predicting aneurysmal occlusion resulting from flow diverting stents
