Computational Models of Mechanical Thrombectomy in Acute Ischemic Stroke Treatment

Stroke is the second leading cause of death worldwide, with acute ischemic strokes representing 87% of all stroke cases. Given the number of individuals experiencing acute ischemic strokes, there is a pressing need to improve therapies for this disease. A recent treatment for large vessel occlusion, which accounts for up to 46% of all acute ischemic strokes and 98.8% of all poststroke mortality, is mechanical thrombectomy. The main forms of thrombectomy include using a stent retriever to trap and extract the occlusive clot or an aspiration catheter to remove the clot via suction. While this is an effective treatment for large vessel occlusion, studies show that only 50% of treated patients achieve good functional outcomes. Additionally, up to 40% of patients experience distal embolization, where the clot breaks into fragments during removal; these fragments travel distally and cause further damage to the cerebral vasculature. Individual studies have shown that adjusting treatment parameters such as i) stent retriever length, size, and withdrawal speed, or ii) aspiration catheter size, distance from the clot, and suction pressure improve patient outcomes. These studies are limited though, and no study has thoroughly investigated the impact of all these treatment parameters on procedural outcomes such as i) degree of recanalization, ii) number of passes needed to recanalize, and iii) occurrence of distal embolization for clots of varied geometry, composition, and location. It has been noted that computational models could overcome this gap; they can be used to systematically test all treatment parameters for clots of varied geometry, composition, and location. The advantage of this approach is in the reduction of time and resources needed to study thrombectomy treatments, as experiments can be difficult to perform, costly, and time-consuming. Despite this, few computational models exist that capture the complex physics of thrombectomy, let alone investigate the sensitivity of procedural outcomes to treatment parameters. To fill this gap, the proposed project aims to develop and validate an open-source computational model of thrombectomy (Aim 1) and optimize thrombectomy for clots of varied geometry, composition, and location using computational models (Aim 2). The aims will use state-of- the-art techniques in experimental and computational biomechanics. Our current lack of knowledge is potentially withholding better treatment strategies for large vessel occlusion in acute ischemic stroke. Thus, my ultimate goal is to identify optimal thrombectomy treatment options and inspire the development of new thrombectomy devices and strategies. Along with the research strategy, the fellowship training plan is organized to offer the applicant professional development toward an independent research career in a top-tier institutional environment at the University of Texas at Austin under the sponsorship of a leading expert in blood clot biomechanics. Upon completion of the training program, the applicant will be ideally prepared for further postdoctoral training and eventually a faculty position in cardiovascular disease research. Project Number: 1F31HL176137-01A1 | Fiscal Year: 2025 | NIH Institute/Center: National Heart Lung and Blood Institute (NHLBI) | Principal Investigator: Matthew Lohr | Institution: UNIVERSITY OF TEXAS AT AUSTIN, AUSTIN, TX | Award Amount: $41,266 | Activity Code: F31 | Study Section: Special Emphasis Panel[ZRG1 F10C-B (20)] View on NIH RePORTER: https://reporter.nih.gov/project-details/1F31HL17613701A1