Abstract
Cardiovascular diseases are a leading cause of mortality worldwide. Physical activity is linked with a reduced prevalence of cardiovascular diseases. However, excessive over-volume of training could negatively increase the risk of cardiovascular diseases. Prediction models are usually derived to facilitate decision-making and may be used to precisely adjust the intensity of physical activity and stratify individual exercise capacity. Incorporating prediction models and knowledge of risk factors of cardiovascular diseases allows for the accurate determination of risk groups among athletes. Due to the growing popularity of amateur physical activity, as well as the high demands for professional athletes, taking care of their health and providing precise pre-participation recommendations, return-to-play guidelines or training intensity is a significant challenge for physicians and fitness practitioners. Athletes with confirmed or suspected cardiovascular disease should be guided to perform training in carefully adjusted safe zones. Indirect prediction algorithms are feasible and easy-to-apply methods of individual cardiovascular disease risk estimation. Current knowledge about the usage of clinical forecasting scores among athletic cohorts is limited and numerous controversies emerged. The purpose of this review is to summarize the practical applications of the most common prediction models for maximal oxygen uptake, cardiac arrhythmias, hypertension, atherosclerosis, and cardiomyopathies among athletes. We primarily focused on endurance disciplines with additional insight into strength training. The secondary aim was to discuss their relationships in the context of the clinical management of athletes and highlights key understudied areas for future research.