Advanced cardiovascular risk prediction in the acute care setting
Research type
Research Study
Full title
Optimising diagnostic efficiency in the Emergency Department by using advanced machine learning methods to update and personalise a contemporary clinical prediction model for early identification and exclusion of acute coronary syndromes and long term cardiovascular outcomes
IRAS ID
244799
Contact name
Richard Body
Contact email
Sponsor organisation
University of Manchester
Clinicaltrials.gov Identifier
NIHR300246, NIHR Doctoral Research Fellowship; G70292, Manchester NHS Foundation Trust - Data Driven Healthcare Award
Duration of Study in the UK
2 years, 0 months, 1 days
Research summary
This study will examine retrospective databases and seek to improve the short term and long term treatment of cardiovascular disease.
------
The Troponin-only Manchester Acute Coronary Syndromes (T-MACS) decision aid allows clinicians to rapidly “rule out” the diagnosis of a heart attack when patients present to the Emergency Department with chest pain or similar symptoms. Research from over 4,000 patients has shown the algorithm is accurate, allowing over 40% of patients to be safely reassured following a single blood test that they are not having a heart attack and discharged from A&E. This could save the whole NHS over £100 million per year.
Research suggests that locality and different patient populations may have an important impact on the accuracy of the T-MACS algorithm. In addition, as patient demographics and standards of care evolve with the passage of time, the accuracy of the algorithm is likely to decline (a phenomenon known as 'calibration shift'). T-MACS has taken 14 years and seven clinical studies to be clinically ready. Repeating this would be overwhelmingly expensive, resource intensive and unfeasible.
------
Long term heart disease risk factors are routinely measured in the emergency department, and we hypothesise could be used to predict long term risk. The potential is huge, with 23.4 million patients attending ED each year, ED attenders are less engaged with primary care and Greater Manchester has double the national average of preventable heart disease deaths.
In this work, we will use routinely collected clinical data to refine the T-MACS algorithm and examine long term risk factors. We will determine the outcome of patients by linking the data with Hospital Episode Statistics, a national NHS database. We will use machine learning techniques to identify the best way to continually update T-MACS, and cox and logistic regression to analyse the long term outcomes.
REC name
Wales REC 7
REC reference
19/WA/0311
Date of REC Opinion
9 Jan 2020
REC opinion
Further Information Favourable Opinion