Background

Sudden cardiac death is the leading cause of mortality in athletes during sport. Intense endurance exercise may be associated with cardiac remodeling processes and abnormal ECG findings. Thus, the interpretation of 12-lead ECGs in athletes requires careful analysis to distinguish physiological changes related to athletic training from findings suggestive of an underlying pathological condition.

Objective

The aim of this observational clinical trial was to analyze a possible correlation between training intensity and altered ECG pattern. Beyond that, possible further predictive parameters for ECG alterations were evaluated.

Methods

Data from consecutive athletes with complete data sets, that presented at our outpatient clinic between 2007 and 2022, were analyzed. High-performance athletes (group 1) were compared to competitive athletes with a training intensity of > 6 hours per week (group 2) and recreational athletes that trained for less than 6 hours per week (group 3). Normal 12-lead ECGs were distinguished from ECGs with physiological changes related to athletic training, borderline and abnormal ECG pattern according to the current international consensus standards for ECG interpretation in athletes.

Results

A total of 465 consecutive athletes (mean age 27.5 ± 7.7 years, 94% male) were included. High-performance athletes (n=155, 94% male,  26.1 ± 6.8 years old) were compared to competitive (n= 155, 26.1 ± 6.5 years old, 94% male) and recreational athletes (n=155, 30.2 ± 8.9 years old, 94% male). 4% of all athletes (n=17, 34.3 ± 27.2 years old, 100% male) presented with normal ECGs. The majority (85%) (n=395, 37,4 ± 36.6 years old, 93% male) showed physiological changes related to athletic training. In 10 athletes (2%) borderline ECG-findings and in 43 athletes (9%) abnormal ECG-pattern were documented, which required further evaluation (Figure 1). Regression analyses identified a significant correlation between physiological ECG adaptations and the athletes’ training intensity (regression coefficient β=0.218, p<0.001) whereas gender (β=-0.047, p=0.318) and age (β=0.084, p=0.070) were not associated with adaptive ECG alterations. Between the athletes’ training intensity and abnormal ECG pattern no significant correlation was observed (β=-0.018, p=0.701).

Conclusion

A significant correlation between the athlete’s exercise intensity and physiological ECG adaptations was revealed. In one out of 10 athletes, abnormal findings suggestive of an underlying pathology were identified. Further studies are warranted to confirm our initial observations.