In 1763, Bayes theorem ("An Essay Towards Solving a Problem in the Doctrine of Chances"), one of the most fundamental principles in probability theory, was published in Philosophical Transactions of the Royal Society.1 In a twist of fate, Thomas Bayes had died 2 years prior to the publication of his lasting legacy, which still resonates throughout modern clinical medicine. Bayes theorem is based on conditional probability, or the probability of an event occurring based on other conditions associated with the event in question.1 Today, bayesian philosophy and consideration of pretest probability remains embedded in the determination of best practices for clinical screening and risk stratification. In the clinical care of competitive athletes, the emergence of coronavirus disease 2019 (COVID-19) and the marked tropism of severe COVID-19 infection on the cardiovascular (CV) system have raised concerns regarding the potential deleterious effects of COVID-19 infection on the heart of the healthy athlete. Moreover, recent single-center observational data from the collegiate athletic setting suggest subclinical myocardial injury and alleged myocarditis, as determined by cardiac magnetic resonance (CMR) imaging, may occur after mild and even asymptomatic cases of COVID-19 infection in young athletes.2,3 In these prior analyses, the return-to-play screening algorithm2,3 included mandated CMR imaging in addition to 12-lead electrocardiography, transthoracic echocardiography, and testing for cardiac troponin levels.4
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