Predicting AF after Cardiac Surgery - the PARADISE Score. A Clinical Prediction Rule for Post-operative Atrial Fibrillation in Patients Undergoing Cardiac Surgery (PARADISE)
The PARADISE study will develop two reliable prediction models to identify which patients are at greatest risk of developing Atrial Fibrillation (AF) following heart surgery.
The PARADISE Project started 1 February 2021.
Kadoorie Centre for Critical Care Research and Education, Nuffield Department of Clinical Neurosciences , University of Oxford, Level 3, John Radcliffe Hospital, Oxford OX3 9DU
Funder: NIHR National Institute of Health Research, Health Technology Assessment Programme HTA Project: NIHR131227
Chief Investigator: Professor Peter Watkinson, University of Oxford and Professor Benjamin O'Brien, Perioperative Medicine Barts Heart Centre
IRAS Project ID: 296508
Sponsor: University of Oxford
Clinical Trials Unit: Oxford Clinical Trials Research Unit
Atrial Fibrillation (AF) is a common abnormal heart rhythm. AF causes the heart to beat irregularly and sometimes very rapidly. About 30-50% of patients develop AF after heart surgery. These patients stay longer on the Intensive Care Unit (ICU) after surgery, are more likely to develop complications and have a higher risk of dying. Avoiding AF is important.
Some drugs, including beta blockers and amiodarone may help prevent AF if given after surgery. However, these may also lead to complications (such as lung damage). It is therefore important to identify which patients are most likely to benefit from these treatments (i.e., where the benefits outweigh the risks). There are existing tools designed to predict the risk of suffering AF after heart surgery. However, they are unreliable and therefore not used in clinical practice. A modern, reliable risk prediction tool is needed.
The PARADISE study will develop and test new prediction tools to identify which patients are most at risk of developing AF after heart surgery. We will focus our tools on those patients who most commonly develop AF, such as those who have had surgery to repair a valve or blood vessel in their heart.
To do this we will:
• Review the medical literature and assemble a panel of medical experts to create a list of known factors that affect patients’ risk of AF after heart surgery
• Use a large UK general practice database (CALIBER) to see whether we can find new risk factors.
• Ask the expert panel to agree a list of known and new risks factors to be included in the prediction tool.
• Develop two new prediction tools using an existing American cardiac surgery database (the Partners research Database). The first will be used before surgery, the second immediately following surgery. Two models are needed as events during surgery may alter the risk of AF.
• Test how reliably our new tools predict which patients suffer AF after surgery, with data from two large UK NHS heart centres (Liverpool and Barts), one US Hospital (Brigham) and a UK clinical trial (Tight-K).
• We will work with two charities (AF Alliance and StopAfib) to share our results with patients and the wider public.
