Principal Investigator: Ms Doyin Odunmbaku-Mansell
Institution: University of Bath
Professor Raimondo Ascione – University of Bristol
Dr Jonathan Rodrigues, Royal United Hospital Bath NHS Foundation TrustTags: 52530, cardiovascular, Heart failure, myocardial infarction, patient-specific, prognostics, strain
This project aims to use the UK Biobank’s dataset of cardiac magnetic resonance images in order to validate a simple and robust method for quantifying the mechanical function of a patient’s heart following a heart attack.
Strain measures the change in size of the heart throughout the cardiac cycle relative to its size at end-diastole (when the left ventricle contains its largest volume of blood), can be done on a local or global scale relative to the left ventricle. There are strain models based on dividing the heart into 17 segments to find highly localised difference in function, however the results can be difficult to interpret swiftly in the clinical setting and have higher associated errors, and as such strain has not been widely adopted into routine practice. Global strain measures also exist and are more robust, but can obscure localised changes in function. The simplified method proposed here will provide rapid diagnostic information for cardiologists and will help them determine the most appropriate and effective treatment plan for each individual patient.
The proposed method quantifies the degree and rate of mechanical strain in the base, mid-ventricle, and apex regions of the heart. We have shown that it characterises the changes in heart function that occur following a heart attack in an animal experiment, and have found it to be robust and repeatable across different users and medical imaging analysis software. Though other strain methods do already exist, our simple regional approach is more robust and repeatable than these voxel-based methods. Crucially, it is more straightforward to interpret and could be easily tracked over time to monitor the condition of the patient.
With rigorous testing and automation, this metric has the potential to be translated into standard clinical practice, supplementing current methods of determining MI outcome. Using the large datasets available in the UK Biobank we aim to establish normal ranges for these strain and strain rate measures, thus enabling simple guidelines to be produced, which can ultimately be translated to clinical practice.
The project is projected to last between 4 months to 8 months and will form a significant part of my doctoral thesis.
Last updated Apr 1, 2020