The risk for sudden cardiac death (SCD) in adults with no history of cardiovascular disease (CVD) can now be assessed via a prediction model developed and validated by researchers at the University of Pennsylvania, and published in the journal Circulation.
Annually, over 300,000 Americans die from out-of-hospital SCD, making it one of the leading causes of death in this country. Its onset is unexpected, gives few to no warning signs or symptoms, and occurs most frequently in the general population without a history of CVD. Clinicians have been unable to develop a model that is predictive of SCD risk in the general population.
“Sudden cardiac death is a significant public health concern, and the rates of decline have not paralleled those observed for other cardiovascular conditions such as heart attack or stroke,” said lead author Rajat Deo, MD, MTR, an assistant professor of Cardiovascular Medicine in the Perelman School of Medicine at the University of Pennsylvania, Philadelphia. "The American Heart Association (AHA) and American College of Cardiology (ACC) developed a risk equation for determining generalized cardiovascular risk in 2013, but this is the first time an SCD-specific prediction model has been developed and validated.”
Dr. Deo and colleagues assessed 17,884 adults aged 45 years or older who had no history of cardiovascular disease and were already participating in two studies: Atherosclerosis Risk in Communities (ARIC) study, and the Cardiovascular Health Study (CHS). They examined demographics, clinical and laboratory data, electrocardiographic/echocardiographic measures, and biological markers, and adjudicated out-of-hospital deaths to determine which were most likely caused by fatal arrhythmias.
In all, 345 adjudicated SCD events occurred. Researchers identified 12 independent risk markers in their model that outperformed the 2013 ACC/AHA Pooled Cohort Risk Equation for the prediction of SCD:
- Male sex;
- African-American race;
- Current smoking;
- Systolic blood pressure;
- Use of antihypertensive medication;
- Serum potassium;
- Serum albumin;
- Estimated glomerular filtration rate (GFR); and
- QT interval.
Over 10 years of follow-up, their model had good to excellent discriminatory value for SCD risk (C statistic 0.820 in ARIC and 0.745 in CHS). This SCD prediction model was also slightly better in predicting SCD compared with the 2013 ACC/AHA Pooled Cohort Risk Equations (C statistic 0.808 in ARIC and 0.743 in CHS).
Furthermore, only this SCD prediction model had similar and accurate prediction for SCD using the original uncalibrated score and the recalibrated equation. In the echocardiographic subcohort, a left ventricular ejection fraction of less than 50%—traditionally the primary marker for identifying high-risk individuals—was demonstrated by only 1.1% of subjects, and did not enhance SCD prediction.
“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. 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,” concluded Dr. Deo.
This study was funded by the National Institutes of Health (K23DK089118), National Heart, Lung and Blood Institute (RC1-HL099452) (R01HL116747) (R01HL111089), and the American Heart Association (16EIA26410001), with additional contribution from the National Institute of Neurological Disorders and Stroke and the National Institute on Aging (AG023629).