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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.

Atrial Fibrillation after cardiac surgery (AFACS) is the most common complication following cardiac surgery, with an incidence between 30% and 50%. Even though AFACS can be transient and patients are often discharged from hospital in normal sinus rhythm, patients with new-onset AFACS have a 5-fold increased risk of developing long-term AF. Currently, there is no widely accepted prediction model that reliably allows clinicians to determine the risk of a patient developing AFACS. This study will develop and validate two different scores as they apply to different situations, in the pre-operative assessment clinic (PARADISE 1) and on arrival in the post-operative care unit (PARADISE 2).

There are three parts to our study:

First, we will produce a list of possible factors that alter the risk of getting AF after heart surgery. We will do this by:

  • doing a detailed review of what has been published including medical papers, and any clinical trials,
  • seeing if we can identify specific risks (known as risk factors) in a UK general practice database (CALIBER) which includes 90,000 people who had heart surgery,
  • asking mathematical and clinical experts, and
  • using a modern computer technique (called machine learning) to look for previously unrecognised AF risk factors in a large United States (US) database (the Partners Research Database (PRD), which includes over 30,000 patients) and in the CALIBER database.

Using this list, we will use mathematics (standard statistical approaches) and ‘machine learning’ within the PRD to develop prediction models to identify patients at increased risk of AF.

Meanwhile we will work with two large UK NHS heart centres that together do 6000 heart operations a year, and an ongoing UK prospective clinical trial to ensure the risk factor list and when a patient develops AF are documented precisely.  

Finally, we will see if our new models work by using it in a new study using information from the two UK NHS heart centres to see if our models predict who will get AF reliably.