Researchers develop model to predict sudden cardiac death risk

Predicting a cardiac event with no warning signs or symptoms in patients without a history of cardiovascular disease (CVD) sounds like something from science fiction. With that being said, a team of researchers at the Perelman School of Medicine at the University of Pennsylvania have taken a step toward achieving the impossible.

Rajat Deo, MD, MTR, an assistant professor of cardiovascular medicine at the University of Pennsylvania, has developed and validated a prediction model to determine sudden cardiac death (SCD) risk in adults without a history of cardiovascular disease.

Unlike other cardiovascular conditions, SCD, which is responsible for half of all heart disease deaths, has not seen a decrease in incidence or mortality rates in recent years.

Researchers evaluated 17,884 adults, 45 years of age and older with no history of CVD, who were participants in two large, National Institute of Health (NIH)-funded cohorts: the Atherosclerosis Risk in Communities Study and the Cardiovascular Health Study.

Based on that data, Deo and his team identified 12 independent risk markers that outperformed the American Health Association and American College of Cardiology’s risk equation for determining generalized cardiovascular risk that was developed in 2013. These risk factors included age, male sex, African American race, current smoking, systolic blood pressure, use of antihypertensive medication, diabetes, serum potassium, serum albumin, HDL, estimated GFR and QT interval.

Interestingly, the strongest marker for high risk individuals was not a predictor of SCD risk. Low left ventricular ejection fraction was present in only 1 percent of participants.

"Our findings provide a strong step toward distinguishing SCD risk across the general population and can help target future strategies at SCD prevention for the highest risk subgroups of the general population,” said Deo in a statement. “What's more, use of this risk model could lead to pinpointing specific communities with higher risk populations, ideally leading to increased training and awareness for emergency medical staff, volunteers and the general public in those regions."