An algorithm for predicting risk of major cardiovascular events
Diagnostic
Whilst a number of tools have previously been developed that measure the risk of a cardiovascular event such as a heart attack or stroke in people without cardiovascular disease, there exists a need for a similar tool for people already diagnosed with cardiovascular disease. This project aimed to develop such a tool, the Heart Research UK risk algorithm, using large database sets.
A large data cohort was utilised of patients in primary care settings in the years between 2006 -2014 and a number of computational, statistical models were used to generate a risk score that could be used to predict future cardiovascular events. The resulting model has potential for use in clinical environments and is intended to be made public access on open platforms.
There are additional plans to use machine learning to complete further analyses of these results and future funding will be sought regarding implementation of the risk score.
The development of a new AI-supported model for imaging the heart based on data from over 700 people with atrial fibrillation, allow ablation to be more personalised to individual patients.
Bringing together patients and medical professionals to establish a partnership aimed at identifying areas of priority in adult cardiac surgery, to better inform clinicians and make heart surgery safer for patients.
Developing an AI-assisted computer programme to identify whether a conduction system pacing (CSP) procedure has been successful, which will allow more patients to benefit from this innovative pacemaker technology.
Developing a new test to measure the levels of desmosine in the body, which are higher when there is damage to the aorta, to diagnose aortic dissection more quickly.
Your gifts fund our life saving project