How To Better Predict Stroke In Patients With Atrial Fibrillation?

Atrial fibrillation (or simply AF) is the most common type of arrhythmia, which is a problem with the rate or rhythm of the heartbeat. During an arrhythmia, the heart can beat too fast, too slow, or with an irregular rhythm.

Though people with AF might not have any symptoms, they are actually at a higher risk of getting a stroke. As AF could lead to the formation of blood clots that can travel to the brain and block an artery, AF independently raises the risk of ischemic stroke 4-to-5-fold. AF could also lead to heart failure and other kinds of heart disease.

Being highly age-dependent, AF affects 10 percent of those aged above 80. About 2.7 million Americans are living with AF.

There is no doubt that predicting ischemic stroke and major bleeding are both relevant to the anticoagulation decision, but previous decision analyses have indicated that risk of ischemic stroke is the more important one for most patients with AF.

In order to help physicians decide whether to begin blood-thinning treatment for patients with AF, a more accurate and reliable stroke prediction model has been jointly developed by researchers from Kaiser Permanente, Massachusetts General Hospital, University of California San Francisco and Stanford University School of Medicine. The findings were published online June 19, 2013 in the ‘Journal of the American Heart Association (JAHA)’. 

To predict AF stroke risk factors, the new model incorporates common clinical features and uses a broader range of age categories to calculate a risk score. The risk score helps doctors and their patients weigh the benefits and risks of starting a blood-thinning treatment.

Clinical features used in the new model include coronary artery disease, diabetes, excess urinary protein excretion, female gender, heart failure, hypertension, kidney dysfunction, peripheral arterial disease, prior ischemic stroke, and older age,

Strong increased stroke risk across the entire age range was found. Individuals aged above 85 were at nearly double the risk of those aged between 75 and 84. But individuals who had experienced a prior stroke were at higher risk regardless of age. Age, prior stroke and their interaction proved to be the dominant risk factors. 

While warfarin (a blood-thinner and anticoagulant) has been known to be highly effective in preventing ischemic stroke, treatment could be difficult to control and often leads to hemorrhage. Based on the prevailing risk assessment formulas recommended by leading clinical practice guidelines, doctors could only have moderate ability to predict which patients will have a stroke.

The new model was effective in calculating risk in primary prevention patients whose stroke risk is most uncertain. These patients pressingly require personalization in the anticoagulation decisions and better prediction in severe stroke.