Chomiuk Tomasz, Kasiak Przemysław, Mamcarz Artur, Śliż Daniel
3rd Department of Internal Medicine and Cardiology, Medical University of Warsaw, 02-091 Warsaw, Poland.
Rev Cardiovasc Med. 2025 May 16;26(5):37493. doi: 10.31083/RCM37493. eCollection 2025 May.
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.
心血管疾病是全球主要的死亡原因。体育活动与心血管疾病患病率的降低有关。然而，过度的训练量可能会负面增加心血管疾病的风险。预测模型通常用于辅助决策，可用于精确调整体育活动强度并对个体运动能力进行分层。结合预测模型和心血管疾病风险因素的知识，能够准确确定运动员中的风险群体。由于业余体育活动日益普及，以及对职业运动员的高要求，关注他们的健康并提供精确的赛前建议、重返赛场指南或训练强度，对医生和健身从业者来说是一项重大挑战。确诊或疑似患有心血管疾病的运动员应被引导在精心调整的安全区域内进行训练。间接预测算法是个体心血管疾病风险评估可行且易于应用的方法。目前关于运动人群中临床预测评分使用的知识有限，且出现了许多争议。本综述的目的是总结最常见的预测模型在运动员最大摄氧量、心律失常、高血压、动脉粥样硬化和心肌病方面的实际应用。我们主要关注耐力项目，并对力量训练进行了额外的探讨。次要目的是在运动员临床管理的背景下讨论它们之间的关系，并突出未来研究中关键的未充分研究领域。
