Parameters of cardiac symptoms in young athletes using the Heartbytes database

Introduction: To determine clinical parameters that are related to abnormal cardiac symptoms in physically active youth. Methods: We used Simon's Heart Heartbytes National Youth Cardiac Registry to collect data from adolescent athletes in southeastern Pennsylvania. We collected age, race/ethnicity, abnormal cardiac symptoms, medical history, medication use, caffeine intake, and family history. We obtained height, weight, blood pressure, cardiac murmur findings, and ECGs. Echocardiogram was obtained if necessary. Binary logistic regression analysis was performed to identify independent associations between abnormal cardiac symptoms and collected variables. The odds ratio (OR), 95% confidence interval (95% CI), and p-values were used as statistical values. Results: Of the 887 athletes (543 males and 344 females, age = 16.9 +/- 2.1 years, height = 166.9 +/- 11.4 cm, weight = 62.0 +/- 16.0 kg), 186 (21%) had abnormal cardiac symptoms including chest pain, passing out, difficulty breathing, extreme fatigue, and heart race. There was an independent association between abnormal symptoms and a past medical history (OR: 4.77, 95%CI: 3.18, 7.17, p = 0.001) and medication use (OR: 1.74, 95%CI: 1.08, 2.79, p = 0.022). In medical history, young athletes with asthma showed a greater propensity of abnormal cardiac symptoms (48.9%) compared to young athletes without (14.0%, p = 0.001). Additionally, young athletes with anxiety or depression demonstrated a higher proportion of abnormal cardiac symptoms (48.9%) than those without (19.5%, p = 0.001). Although the association between the presence of abnormal symptoms and African-American race (OR: 2.04, 95%CI: 0.96, 4.35, p = 0.065) and average daily consumption of at least 2 caffeine drinks (OR: 2.08, 95%CI: 0.86, 5.02, p = 0.103) were not significant, there was a trend to reach the a priori significance level. Conclusions: This study identified several clinical parameters that are associated with symptoms suggestive of abnormal cardiac conditions. Larger studies need to be done to better sort out the clinical history that may contribute to false positives to further reduce false positives at heart screenings.