Stroke risk prediction for patients with atrial fibrillation

Overview

https://research.qut.edu.au/accterm/https://research.qut.edu.au/accterm/

The presence and severity of an atrial myopathy, not atrial fibrillation (AF), is the primary driver of the risk of stroke. However, current stroke risk stratification and screening focuses only on AF, using risk scoring systems (such as CHA₂DS₂-VASc) derived from demographic and clinical profiles. These systems do not incorporate any personalised metrics of altered atrial structure or function and are limited in their overall accuracy (c statistics 0.675). Importantly, patients with atrial myopathy and a high risk of stroke but who do not have AF could be missed if only these clinical scores are used. There is a lack of a robust technology to reproducibly and non-invasively assess atrial function, such as stasis and strain, two critical properties that are associated with thrombus formation and subsequent stroke. The project aims to develop a novel one-stop-shop technology to assess atrial strain as a more direct marker of a myopathic atrium and stasis as a more direct marker of clinically significant thrombus formation. Our hypothesis is that atrial myopathy is associated with cardioembolic stroke, and the CT-defined atrial myopathy will improve the delineation of cardioembolic stroke risk. Success of this project will establish a novel strain and stasis mapping paradigm.

Research engagement

literature review

lab-based work

computational modeling

Research activities

work woth a multidisciplinary team.

Home - Cardiovascular Technology Laboratory (qut.edu.au)

Outcomes

This project has already been granted ethical approval and has access to dozens of patient medical records.

The aim of this project is to:

• develop a medical image 3D reconstruction procedure
• 3D print heart models which are visually transparent at high resolution and deformable with a realistic, surgical feel
• develop a computational and/or experimental fluid dynamics pipeline to simulate blood flow

Skills and experience

To be considered for this project, you need to have completed or be completing a degree in an engineering, computational or physics discipline. Relevant computational experience (eg. AutoCAD, MIMICS, Amira, MATLAB) is useful, but not required.

Start date

1 November, 2024

End date

21 February, 2025

Location

GP O401H

Keywords

• AI
• cardiovascular disease
• medical image
• computational model
• stroke
• atrial fibrillation
• heart function

Contact

zhiyong.li@qut.edu.au