Cardio Round-Up: COVID Linked to Heart Condition in Young Athletes; and More

COVID May Trigger Heart Condition in Young Athletes

A heart condition, myocarditis, has been found in a number of U.S. college athletes who have had COVID-19, a new study finds. Myocarditis has also been linked in some young people to the COVID vaccine. But the odds are far greater that this inflammation of the heart muscle will occur in those who get COVID infection itself, experts said. “We’re still learning about how the virus attacks the heart,” said lead researcher Dr. Jean Jeudy, a professor of radiology at the University of Maryland School of Medicine. “Myocarditis is part of the body’s reaction to fighting the infection, but it’s also in response to the virus trying to attack the heart.”

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Aspirin Use Tied to Incident Heart Failure in At-Risk Adults

For individuals at risk, aspirin use is associated with increased risk for incident heart failure, according to a study published online Nov. 22 in ESC Heart Failure. Blerim Mujaj, M.D., Ph.D., from the University of Leuven in Belgium, and colleagues conducted a pooled analysis of data from a total of 30,827 individuals (mean age, 66.8 ± 9.2 years) at risk for heart failure who were enrolled in six observational studies. Patients were followed for the first incident of fatal or nonfatal heart failure. The association of incident heart failure with aspirin use was examined.

The researchers found that 1,330 patients experienced heart failure during a median of 5.3 years. The hazard ratio for heart failure in association with aspirin use was 1.26 in fully adjusted analyses and 1.26 in a propensity-score-matched analysis. For the 22,690 patients without cardiovascular disease history, the hazard ratio associated with aspirin use was 1.27.

AI-Based Analysis of ECG May Help Assess A-Fib Risk

Artificial intelligence (AI)-based analysis of 12-lead electrocardiograms (ECGs) has similar predictive ability for incident atrial fibrillation (AF) as a clinical risk score, according to a study published online Nov. 8 in Circulation.

Shaan Khurshid, M.D., M.P.H., from Massachusetts General Hospital (MGH) in Boston, and colleagues trained a convolutional neural network (ECG-AI) to infer five-year incident AF risk using 12-lead ECGs in patients receiving care at MGH. Three hazard models were fit and included: ECG-AI five-year AF probability; the Cohorts for Heart and Aging in Genomic Epidemiology AF (CHARGE-AF) clinical risk score; and terms for both ECG-AI and CHARGE-AF (CH-AI). Model performance was assessed in an internal test set and two external test sets (Brigham and Women’s Hospital [BWH] and U.K. Biobank). The training set and test sets included 45,770 and 83,162 individuals, respectively.