This project will harness TTP’s medical understanding as well as our technical capability, enabling us to gain new physiological understanding of the mechanics of ECV as an AF treatment, and why it works or does not work in certain patients. We are delighted to have been chosen as a partner and look forward to working with the NHS and University groups.
TTP plc, NHS Highland and The University of the Highlands and Islands to partner on atrial fibrillation clinical data collection and analysis project
- Collaboration aims to develop scoring algorithms to enable better clinical management of patients with atrial fibrillation
- Data will help determine patient suitability for treatment with electrical cardioversion therapy
Cambridge, UK, 16 March 2020: TTP plc (TTP), a leading independent technology and product development company, today announced it has established a partnership with NHS Highland and The University of the Highlands and Islands, to use clinical data interpretation and machine learning algorithms to predict which patients with atrial fibrillation (AF) are able to be successfully treated using electrical cardioversion (ECV).
AF is a common heart condition, causing an abnormally fast heart rate and irregular rhythm, which can lead to significant morbidity (e.g. stroke, heart failure), and mortality. Treatment options include drugs and/or ECV, however although morbidity and mortality can be significantly reduced with ECV, it is successful at one year in only 30% of patients. The ECV procedure also carries risk and is expensive to carry out. Consequently, there is an urgent need to predict which patients with AF are most suitable for treatment using ECV. Successfully predicting treatment suitability using remotely collectable data becomes especially valuable in geographies where population density is sparse, and where patients may often travel long distances for treatment.
NHS Highland and The University of the Highlands and Islands will work together to gather clinical data including electrocardiograms (ECGs), age, gender, comorbidities, medications and outcomes, all of which will be anonymised at source. TTP will analyse and interpret the data, using this information to determine any clinical notable ECG-derived factors that may influence short – and long-term AF outcomes post ECV. TTP will also use the data to rapidly prototype and train machine-learning algorithms for clinical prediction and risk scoring. The output of the project has the potential to be taken forward to be deployed in clinical settings such as the NHS as a risk stratification/clinical decision support tool.
Currently, decisions on whether to proceed with ECV are based on varying factors and can be fairly subjective. There is a clear need for an evidence-based test to help guide physicians when treating AF, improve patient outcome and reduce unnecessary burden on the NHS.
The NHS Highland and University of the Highlands and Islands research group has received £15k funding for the project via a grant from the Collaborative Campus Challenge Fund, provided by Highlands and Islands Enterprise.