The first virtual patient-specific thrombectomy procedure.

Treatment of acute ischemic stroke has been recently improved with the introduction of endovascular mechanical thrombectomy, a minimally invasive procedure able to remove a clot using aspiration devices and/or stent-retrievers. Despite the promising and encouraging results, improvements to the procedure and to the stent design are the focus of the recent efforts. Computational studies can pave the road to these improvements, providing their ability to describe and accurately reproduce a real procedure. A patient with ischemic stroke due to intracranial large vessel occlusion was selected and after the creation of the cerebral vasculature from computed tomography images and a histologic analysis to determine the clot composition, the entire thrombectomy procedure was virtually replicated. As in the real situation, the computational replica showed that two attempts were necessary to remove the clot, as a result of the position of the stent retriever with respect to the clot. Furthermore, the results indicated that clot fragmentation did not occur as the deformations were mainly in a compressive state without the possibility for clot cracks to propagate. The accurate representation of the procedure can be used as an important step for operative optimization planning and future improvements of stent designs.

Impact of the Internal Carotid Artery Morphology on in silico Stent-Retriever Thrombectomy Outcome.

The aim of this work is to propose a methodology for identifying relationships between morphological features of the cerebral vasculature and the outcome of in silico simulations of thrombectomy, the mechanical treatment for acute ischemic stroke. Fourteen patient-specific cerebral vasculature segmentations were collected and used for geometric characterization of the intracranial arteries mostly affected by large vessel occlusions, i.e., internal carotid artery (ICA), middle cerebral artery (MCA) and anterior cerebral artery (ACA). First, a set of global parameters was created, including the geometrical information commonly provided in the clinical context, namely the total length, the average diameter and the tortuosity (length over head-tail distance) of the intracranial ICA. Then, a more exhaustive geometrical analysis was performed to collect a set of local parameters. A total of 27 parameters was measured from each patient-specific vascular configuration. Fourteen virtual thrombectomy simulations were performed with a blood clot with the same length and composition placed in the middle of the MCA. The model of TREVO ProVue stent-retriever was used for all the simulations. Results from simulations produced five unsuccessful outcomes, i.e., the clot was not removed from the vessels. The geometric parameters of the successful and unsuccessful simulations were compared to find relations between the vascular geometry and the outcome. None of the global parameters alone or combined proved able to discriminate between positive and negative outcome, while a combination of local parameters allowed to correctly identify the successful from the unsuccessful simulations. Although these results are limited by the number of patients considered, this study indicates a promising methodology to relate patient-specific geometry to virtual thrombectomy outcome, which might eventually guide decision making in the treatment of acute ischemic stroke.

Applicability assessment of a stent-retriever thrombectomy finite-element model.

An acute ischaemic stroke appears when a blood clot blocks the blood flow in a cerebral artery. Intra-arterial thrombectomy, a mini-invasive procedure based on stent technology, is a mechanical available treatment to extract the clot and restore the blood circulation. After stent deployment, the clot, trapped in the stent struts, is pulled along with the stent towards a receiving catheter. Recent clinical trials have confirmed the effectiveness and safety of mechanical thrombectomy. However, the procedure requires further investigation. The aim of this study is the development of a numerical finite-element-based model of the thrombectomy procedure. In vitro thrombectomy tests are performed in different vessel geometries and one simulation for each test is carried out to verify the accuracy and reliability of the proposed numerical model. The results of the simulations confirm the efficacy of the model to replicate all the experimental setups. Clot stress and strain fields from the numerical analysis, which vary depending on the geometric features of the vessel, could be used to evaluate the possible fragmentation of the clot during the procedure. The proposed in vitro/in silico comparison aims at assessing the applicability of the numerical model and at providing validation evidence for the specific in vivo thrombectomy outcomes prediction.

In-Silico Trials for Treatment of Acute Ischemic Stroke.

Despite improved treatment, a large portion of patients with acute ischemic stroke due to a large vessel occlusion have poor functional outcome. Further research exploring novel treatments and better patient selection has therefore been initiated. The feasibility of new treatments and optimized patient selection are commonly tested in extensive and expensive randomized clinical trials. in-silico trials, computer-based simulation of randomized clinical trials, have been proposed to aid clinical trials. In this white paper, we present our vision and approach to set up in-silico trials focusing on treatment and selection of patients with an acute ischemic stroke. The INSIST project (IN-Silico trials for treatment of acute Ischemic STroke, www.insist-h2020.eu) is a collaboration of multiple experts in computational science, cardiovascular biology, biophysics, biomedical engineering, epidemiology, radiology, and neurology. INSIST will generate virtual populations of acute ischemic stroke patients based on anonymized data from the recent stroke trials and registry, and build on the existing and emerging in-silico models for acute ischemic stroke, its treatment (thrombolysis and thrombectomy) and the resulting perfusion changes. These models will be used to design a platform for in-silico trials that will be validated with existing data and be used to provide a proof of concept of the potential efficacy of this emerging technology. The platform will be used for preliminary evaluation of the potential suitability and safety of medication, new thrombectomy device configurations and methods to select patient subpopulations for better treatment outcome. This could allow generating, exploring and refining relavant hypotheses on potential causal pathways (which may follow from the evidence obtained from clinical trials) and improving clinical trial design. Importantly, the findings of the in-silico trials will require validation under the controlled settings of randomized clinical trials.