Researchers in the USA have identified the first generalisable risk score for sudden cardiac death. This prediction model, the researchers argue, provides well-calibrated, absolute risk estimates across different risk strata in an adult population without a history of cardiovascular disease. It may also help target future non-ICD strategies aimed at sudden cardiac death prevention for the highest risk subgroups of the general population.
The majority of sudden cardiac death cases occur in the general population without previous clinically recognised heart disease. There are multiple population-based studies which have demonstrated independent associations between specific risk factors and biomarkers of inflammation, myocyte injury, and neuro-hormonal activation with risk for sudden cardiac death. However, “there is no widely accepted model of individual risk estimation for sudden cardiac death,” writes study author Rajat Deo (Section of Electrophysiology, Division of Cardiovascular Medicine, University of Pennsylvania, Pennsylvania, USA) and colleagues in Circulation ahead-of-print.
Deo et al sought to develop and evaluate a sudden cardiac death prediction model among US adults without a history of cardiovascular disease. They included 13,677 participants from the Atherosclerosis Risk in Communities (ARIC) study and 4,207 participants from the Cardiovascular Health Study (CHS) who were 45 years of age and older. The average age in ARIC was 54±6 years and 72±5 years in CHS. Fifty six per cent were women in ARIC and 61% were women in CHS. Twenty six per cent and 15% were African Americans in ARIC and CHS, respectively. The authors evaluated a series of demographic, clinical, laboratory, electrocardiographic and echocardiographic measures.
Twelve independent risk factors were identified in the ARIC study including age, male sex, African American race, current smoking, systolic blood pressure, use of antihypertensive medication, diabetes, serum potassium, serum albumin, high density lipoprotein (HDL) cholesterol, estimated glomerular filtration rate (GFR) and QTc interval.
There were a total of 345 adjudicated sudden cardiac death events in this combined analysis (ARIC=171 sudden cardiac death events, CHS=174 sudden cardiac death events). Deo et al reported that their sudden cardiac death predictive model, which combined the risk factors identified above, showed good to excellent discrimination for sudden cardiac death (C statistic 0.820 in ARIC and 0.745 in CHS) over a 10-year follow-up. In ARIC, approximately three-fourths of the participants had a 10-year sudden cardiac death risk of less than 1%. The highest decile of risk, however, approached 5% over 10 years, “suggesting that this panel of risk factors can distinguish a large gradient in sudden cardiac risk among middle-aged adults,” the authors note. In the CHS population, the lowest and highest deciles of sudden cardiac death risks approximated 1.5 and 11% respectively over a 10-year follow-up.
Compared to the current 2013 American College of Cardiology/American Heart Association (ACC/AHA) CVD Pooled Cohort risk equations (model developed to calculate the 10-year risk of a first cardiovascular event including nonfatal myocardial infarction, coronary heart disease death, or fatal or nonfatal stroke), Deo et al found that their sudden cardiac death prediction model was slightly better in predicting sudden cardiac death (C statistic 0.808 in ARIC and 0.743 in CHS).
The researchers highlight that this study is one of the first to identify low albumin concentration as an independent sudden cardiac death risk factor in both cohorts; therefore, it may be an important marker for arrhythmic mortality. Additionally, they identified that only 1.1% of participants had a left ventricular ejection fraction <50 and did not enhance sudden cardiac death prediction.
