# Quantitative model-based ESUS reclassification using cardiac and cerebral vessel wall MRI

> **NIH NIH R01** · UNIVERSITY OF WASHINGTON · 2022 · $797,438

## Abstract

Quantitative model-based ESUS reclassification using cardiac and cerebral vessel wall MRI
Stroke is a major cause of death and the leading cause of permanent disability worldwide. Ischemic stroke is the
dominant stroke variety, representing approximately 80+% of strokes in the United States. Defining the specific
underlying pathophysiology of ischemic strokes is critical for personalized secondary prevention treatments with
the goal of minimizing the risk of recurrent events. However, even with extensive diagnostic workup in current
clinical practice, a large portion of ischemic strokes are classified as embolic stroke of undetermined source
(ESUS), leaving these patients without optimal treatment tailored to their specific pathophysiology. Recent
literature has demonstrated that among subjects diagnosed with ESUS, there may be under-detected lesions of
atherosclerosis in intra/extracranial arteries or cardiac pathology on a path towards atrial fibrillation, a so called
“atrial cardiopathy”. This implies that there are opportunities to improve the sensitivity and accuracy of etiologic
diagnosis to reduce ischemic strokes classified into the ESUS category, allowing for more targeted, personalized
secondary prevention measures. New developments in magnetic resonance imaging (MRI) of intra/extracranial
atherosclerosis and atrial cardiopathy may provide new opportunities to detect these currently under-detected
lesions and allow reclassification of ESUS patients into large-artery atherosclerosis or cardioembolic categories
leading to focused treatment strategies.
However, there are still significant challenges to using these imaging methods in practice: 1) Specialized vessel
wall and cardiac MRI (ESUS-imaging) and image analysis algorithms need to be integrated into the standard of
care workflow of stroke patients; 2) A model-based analysis will be needed that combines new findings from
ESUS-imaging and findings from existing clinical workup so that new “risk features (RFs)” can be defined for
reclassification; and 3) The impact of using these RFs on stroke subtype reclassification needs to be studied
prospectively. In this proposal, we plan to develop a model-based analysis focused on ESUS-imaging and test
the hypothesis that among acute ischemic stroke subjects diagnosed as ESUS under current clinical workup, a
new set of RFs drawn from ESUS-imaging will allow reclassification of a subset of ESUS into large-artery
atherosclerosis or cardioembolic categories. The specific aims will: 1) establish new vessel wall and cardiac MRI
(ESUS-imaging) and image analysis techniques; 2) develop a multiparametric statistical model that combines
information from the standard stroke workup and new ESUS-imaging to identify a set of RFs that can reclassify
ischemic stroke etiology; and 3) evaluate the impact of the model on ischemic stroke subtype re-classification.
If successful, this proposal will help to establish a clinical workflow that includes ESUS-imaging in...

## Key facts

- **NIH application ID:** 10531502
- **Project number:** 1R01NS125635-01A1
- **Recipient organization:** UNIVERSITY OF WASHINGTON
- **Principal Investigator:** Nazem Akoum
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $797,438
- **Award type:** 1
- **Project period:** 2022-09-21 → 2027-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10531502

## Citation

> US National Institutes of Health, RePORTER application 10531502, Quantitative model-based ESUS reclassification using cardiac and cerebral vessel wall MRI (1R01NS125635-01A1). Retrieved via AI Analytics 2026-05-27 from https://api.ai-analytics.org/grant/nih/10531502. Licensed CC0.

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