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Record W4393929171 · doi:10.1159/000538326

The Challenges of Screening Master Athletes

2024· article· en· W4393929171 on OpenAlexaboutno aff
Francesca Graziano, Sergei Bondarev, Domenico Corrado, Alessandro Zorzi

Bibliographic record

VenueCardiology · 2024
Typearticle
Languageen
FieldMedicine
TopicCardiovascular Effects of Exercise
Canadian institutionsnot available
Fundersnot available
KeywordsAthletesIncidence (geometry)MedicinePhysical therapyGerontologyDemographySociology

Abstract

fetched live from OpenAlex

In recent decades, there has been a significant increase in the number of men and women over the age of 40 participating in high-intensity endurance sports, often referred to as “master athletes.” The “State of the Running 2019” report by RunRepeat.com and the International Association of Athletics Federation covers 96% of US recreational race results, 91% of the race results from the EU, Canada, and Australia, and a big portion from Asian, Africa, and South America. According to this study, more than a million people finished a marathon in 2018 (compared to less than half million in 2001): of those, more than 40% were over 40 years old [1] (Fig. 1). Other endurance sports, such as cycling, also show a large number of adult and senior competitors. This trend towards a higher mean age of participants in strenuous sport disciplines obliges the sports cardiology community to confront new challenges.The majority of scientific studies and public health initiatives have historically focused on reducing the incidence of sudden death (SD) among young competitive athletes. In this age group, most SDs used to be secondary to inherited cardiomyopathies: diseases that can be suspected based on simple clinical tests such as the electrocardiogram (ECG) [2]. In Italy, the incidence of SD in young (<35-year-old) athletes was 4/100,000/year in 1982. At that time, a law mandating national preparticipation screening (PPS) including history, physical examination, and ECG was enacted, and in the following 20 years, the incidence of SD decreased by almost 90%. In parallel, the number of athletes who received a diagnosis of cardiomyopathies increased [3]. The low incidence of cardiac arrest/SD among young screened athletes was confirmed by two other recent Italian studies that, most importantly, recorded no adverse events caused by diseases potentially identifiable by PPS [4, 5]. Screening strategies have also improved, for example, by recognizing the role of stress testing for ventricular arrhythmias that may suggest the presence of a concealed arrhythmogenic substrate such as the non-ischemic left ventricular scar [6].The causes of cardiac arrest and SD in master athletes are different from that of their younger counterparts, being most often (but not exclusively) related to coronary artery diseases (CAD) [1]. Screening for asymptomatic CAD remains a significant clinical challenge for both athletes and the general population. Unfortunately, the diagnostic accuracy of standard clinical tests such as exercise testing is low and may be even lower in individuals who are used to exercise intensively and tolerate myocardial ischemia better [7]. Moreover, there is evidence that CAD may be more prevalent in endurance athletes than sedentary individuals and that SDs during exercise are often related to rupture of an unstable (but not necessarily obstructive) coronary plaque [8]. Currently, coronary computed tomography (CT) is the only non-invasive test capable of identifying vulnerable non-obstructive coronary plaques. However, its use as a mass screening test is limited due to factors such as availability, cost, radiation exposure, and the need for contrast agents. Furthermore, screening master athletes presents an additional challenge, as many are reluctant to undergo medical tests they perceive as unnecessary, and some exhibit pathological dependence on exercise [9]. As a result, the vast majority of sport-related SDs now occur in this age group [10].In this issue of the journal, Laily et al. [11] enrolled a cohort of 25 men first-time marathon runners aged 45–50 years and compared the results of PPS according to the protocols suggested by the American College of Sports Medicine (ACSM) and by the 2020 European Society of Cardiology (ESC) guidelines on sports cardiology to a comprehensive study protocol including advanced imaging modalities. The ACSM consisted only of a questionnaire and did not pick up any athlete requiring further tests. The ESC screening protocol is more complex and includes history, physical examination, resting ECG, and assessment of the CAD risk based on the on the ESC Systematic Coronary Risk Evaluation (SCORE) system. Those at high risk for CAD should also undergo exercise testing. This PPS method identified one case of hypertrophic cardiomyopathy and premature atherosclerosis. Among the other 24 men who were considered at low risk by both PPS methods, the in-depth clinical investigation protocol also including cardiopulmonary exercise testing, echocardiography, and cardiac magnetic resonance identified two other cases with diseases that were considered as contraindications to intense exercise (non-ischemic left ventricular fibrosis and aortic aneurism), as well as many others with minor cardiac abnormalities or risk factors. It is plausible that if a coronary CT had also been performed, cases of concealed CAD would have been identified. The study confirms that the hearts of senior marathon runners may not necessarily be healthy and highlights the limitations of traditional PPS methods in diagnosing cardiac abnormalities.While addressing the issue of screening master athletes poses considerable challenges, potential strategies can be devised, drawing inspiration from Dr. Rasmussen’s Swiss Cheese Model. This is a metaphor used to illustrate how an accident (in this case cardiac arrest during sport) can occur only when multiple layers of defence, each of them with limitations (represented by the holes in the slices of Swiss cheese), fail to prevent it (Fig. 2). However, adding additional layers of defence reduces the probability that hazards can pass through all of them. This model is used in the aviation industry where critical systems are redundant, so if the primary system fails, there is always another one (or more) for backup. In the setting of master athletes, we believe that PPS protocol should include: (1) history, physical examination, (2) blood tests (in particular glucose and lipid profile), (3) resting ECG, and (4) exercise testing. According to our perspective, which is in line with the recent Italian sports cardiology guidelines [12], exercise testing should not be limited to those with CAD risk factors but to all athletes because it can be useful to evaluate the blood pressure response to exercise and the occurrence of exercise-induced arrhythmias, and to assess symptoms or physical performance and its relation to exercise training. In those with very high risk of CAD, a coronary CT may be justified even if exercise testing is negative.However, we should recognize that PPS does not have the ability to identify all athletes with an underlying disease at risk of SD. This is particularly true for master athletes that may be prone to acute coronary syndrome due to the rupture of a non-obstructive plaque. Hence, the last layer of defence should be the widespread availability of automated external defibrillators (AEDs). These devices have proven to resuscitate up to 90% of individuals suffering cardiac arrest during sport, provided they are used within the first minutes [13].In conclusion, despite its limitations, we believe that PPS is an important health intervention also for master athletes. Besides diseases at risk of SD, a careful medical examination may reveal previously unrecognized medical conditions such as hypertension or dyslipidaemia that have a long-term prognostic relevance. Athletes should be encouraged to undergo PPS, even if asymptomatic and seemingly healthy, as at-risk cardiac conditions may not manifest symptoms before a cardiac arrest occurs. Finally, athletes should also be aware of the importance of readily available AEDs during competitions and verify that an emergency action plan has been implemented before enlisting in a race.The authors have no conflicts of interest to declare.This study was not funded.F.G., S.B., D.C., and A.Z. contributed to conceptualize the study. A.Z. and F.G. wrote the manuscript. S.B. and D.C. contributed to editorial changes in the manuscript. F.G., S.B., D.C., and A.Z. read and approved the final manuscript.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.023
metaresearch head score (Gemma)0.062
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.023
Threshold uncertainty score0.119

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0230.062
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0030.001
Science and technology studies0.0020.003
Scholarly communication0.0040.006
Open science0.0050.005
Research integrity0.0070.008
Insufficient payload (model declined to judge)0.0150.009

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.030
GPT teacher head0.273
Teacher spread0.243 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations8
Published2024
Admission routes1
Has abstractyes

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