Cardiac imaging in evaluating patients prone to sudden death

Multiparametric Mapping via Cardiovascular Magnetic Resonance in the Risk Stratification of Ventricular Arrhythmias and Sudden Cardiac Death

Risk stratification for malignant ventricular arrhythmias and sudden cardiac death is a daunting task for physicians in daily practice. Multiparametric mapping sequences obtained via cardiovascular magnetic resonance imaging can improve the risk stratification for malignant ventricular arrhythmias by unveiling the presence of pathophysiological pro-arrhythmogenic processes. However, their employment in clinical practice is still restricted. The present review explores the current evidence supporting the association between mapping abnormalities and the risk of ventricular arrhythmias in several cardiovascular diseases. The key message is that further clinical studies are needed to test the additional value of mapping techniques beyond conventional cardiovascular magnetic resonance imaging for selecting patients eligible for an implantable cardioverter defibrillator.

Exploring the Risk Factors of Sudden Cardiac Death Using an Electrocardiography and Medical Ultrasonography for the General Population Without a History of Coronary Artery Disease or Left Ventricular Ejection Fraction <35% and Aged >35 Years - A Novel Point-Based Prediction Model Based on the Chin-Shan Community Cardiovascular Cohort

BACKGROUND

Most of the factors and prediction models of sudden cardiac death (SCD) have been developed without considering the Asia population. The purpose of this study is to construct a point-based prediction model for the general population in Asia.

METHODS AND RESULTS

Chin-Shan Community Cardiovascular Cohort (CCCC) is a community-based longitudinal cohort initiated between 1990 and 1991, enrolling participants aged ≥35 years and following them up until 2005. Participants with coronary artery disease (CAD) or a left ventricular ejection fraction (LVEF) of 35% were excluded from this study. The Framingham risk score function was used to derive a simple point-based prediction model. Based on bootstrapping, a novel model (CCCC-SCD-Score) was validated. A total of 2,105 participants were analyzed. The incidence rate of SCD was 0.406 per 1,000 person-years. The CCCC-SCD-Score score was calculated using age groups (maximal points=4), left ventricular hypertrophy, hypertension, left ventricular ejection fraction <40%, aortic flow rate >190 cm/s, and carotid plaque scores ≥5 (point=1 for each risk factor). The C-index of the CCCC-SCD-Score in predicting SCD risks was 0.888 (95% confidence interval: 0.807-0.969).

CONCLUSIONS

For the general Asian population without a history of CAD or a LVEF <35% and who are aged >35 years, the novel model-based scoring system effectively identifies the risk for SCD using the clinical factors, electrocardiographic and echocardiographic data.