# Modeling of Acute Ischemic Stroke for Improving Mechanical Thrombectomy

> **NIH NIH R01** · PENNSYLVANIA STATE UNIVERSITY, THE · 2020 · $675,893

## Abstract

Summary
For the estimated 700,000 acute ischemic strokes (AIS) that occur each year in the United States, new stent
retriever devices have shown an increase in recanalization of occluded cerebral arteries. However, over 15%
of thromboemboli are still unable to be cleared and another 17% of patients die within 90 days despite
successful recanalization. To date, there is little understanding of the upstream thrombosis and embolization
processes that lead to AIS and why some thromboemboli are successfully removed and others are not. To
better understand the entire progression of AIS, we will develop computational models of the upstream
thrombosis, thrombus embolization, lodging and adhesion in the cerebral vasculature, and removal via applied
forces from a thrombectomy device. These models will be validated with ex vivo mock circulatory flow loops
that enable real-time tracking of thrombus growth and embolization and for AIS occlusion to be simulated in
physiologically accurate scenarios. Furthermore, patient-specific anatomy and blood chemistry will be used.
The results of these studies will provide insight to AIS occlusion but provide an opportunity to improve overall
patient outcomes.

## Key facts

- **NIH application ID:** 9885461
- **Project number:** 1R01HL146921-01A1
- **Recipient organization:** PENNSYLVANIA STATE UNIVERSITY, THE
- **Principal Investigator:** Francesco Costanzo
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $675,893
- **Award type:** 1
- **Project period:** 2020-05-04 → 2024-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9885461

## Citation

> US National Institutes of Health, RePORTER application 9885461, Modeling of Acute Ischemic Stroke for Improving Mechanical Thrombectomy (1R01HL146921-01A1). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/9885461. Licensed CC0.

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