In children, sudden cardiac arrest (SCA) is associated with abnormalities of cardiac structure and function as well as abnormalities of the electrical system. These include hypertrophic cardiomyopathy (HCM) and other cardiomyopathies, long QT syndrome (LQTS), Wolff-Parkinson-White (WPW) syndrome, coronary artery anomalies, congenital heart defects, and others.  These conditions result in 10,000-20,000 childhood deaths yearly in the United States (US).  Current screening of the young for cardiac conditions associated with sudden cardiac arrest (SCA) has not been effective in identifying children and adolescents who will experience SCA. Parents and children are often unaware of the signs and symptoms of an undiagnosed heart conditions associated with sudden cardiac arrest and are unaware of their family cardiac history. While many of these cardiac conditions are treatable, they can be fatal if undiagnosed. It is important to know which symptoms, family history, and ECG findings are most highly correlated with these heart conditions to ensure the necessary preventative measures.

This is an observational cross-sectional study. The data for this study are in the existing REDCap Heart Health Screening Study (HHSS). This study includes 4535 enrolled subjects aged 5-19 years from our IRB approved HHSS. Subjects completed a heart health screening questionnaire, reporting symptoms, family history, exercise patterns, and other medically pertinent information. On the screening day, subjects’ vitals (height, weight, blood pressure) were taken, and an ECG was performed. If the ECG indicated a potential structural or functional abnormality, or if the subject’s family history was concerning, the subject received an echocardiogram and physical exam. This study aims to identify if any symptoms alone or when paired with family history are more likely to be associated with an abnormal ECG finding or a significant cardiac condition. We also will determine the ECG findings associated with significant cardiac conditions. We plan to provide descriptive data, determine correlations, and look at a model using logistic regressions to determine the most important symptoms associated with abnormal ECGs and cardiac conditions.