| Title: | In silico clinicogenomic risk prediction in atrial fibrillation and flutter: electrifying patient and clinician decision making |
| Lead Institution: | Wye Valley NHS Trust |
| Principal investigator: | Dr Jason Sangha |
Application 64253
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About
Atrial fibrillation (AFib) and atrial flutter (AFlutter) are common electrical disorders of the upper chambers of the heart (the 'atria') where, instead of beating in a coordinated and functional manner, these chambers simply quiver or beat too fast leading to an irregular, and sometimes fast heart rate. These conditions increase the risk of stroke because the blood sits still in the atria, forming clots which can then break off and block blood vessels in the brain causing strokes. It is known that things like your age, gender, if you have other medical problems such as diabetes and so on ('risk factors') all influence how likely it is that a stroke will occur. In people with risk factors, anticoagulation (blood thinning medication) is recommended to stop the clots forming however the risk with these medications is bleeding, which can require blood transfusion or even be fatal sometimes. People without risk factors don't need anticoagulation. Clinicians use 'risk calculators' to estimate the risk of a stroke if you have AFib/Flutter. They input age and any relevant medical problems into a computer algorithm and it gives them an estimate of how likely it is that the patient will have a stroke. Similar risk calculators assess bleeding risk. However current risk calculators are out-of-date due to advances in anticoagulation and they don't use any information from the genetic code (DNA), which is different in different people. This study aims to develop new and more accurate risk calculators utilising more information, including DNA information, by studying the participants with AFib/Flutter in the UK Biobank. The project will help doctors and patients to make more personalised decisions, reducing strokes and reducing major bleeding complications. In addition, the study will explore and develop risk models where possible for: 1. Development of AFib/Flutter, to help clinicians decide which subsets of people should be screened for these conditions (with a simple heart tracing). 2. Why some people with AFib/Flutter get heart failure - i.e. the pumping action of the heart is reduced leading to water build up (swelling) in the legs and lungs. 3. Why some people don't revert to a normal heart rhythm with invasive treatments, such as an electrical shock. 4. The project will assess how much, if any, genetic information helps in making decisions in patients with AFib/Flutter. The aim is to share findings with patients and colleagues within the next 2 years.1 Publication
| Pub ID | Title | Author(s) | Year | Journal |
|---|---|---|---|---|
| 17846 | The effect of baseline prediabetes on the relation between lipoprotein (a) and incident type 2 diabetes mellitus and cardiovascular outcomes: observations from the UK-Biobank. | Sudipta Chattopadhyay (+2) | 2026 | Diabetes & Metabolic Syndrome Clinical Research & Reviews |
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