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Record W4379094728 · doi:10.1093/eurjpc/zwad188

Atrial fibrillation is the most prevalent cardiac condition in master athletes

2023· article· en· W4379094728 on OpenAlexaboutno aff
Eivind Sørensen, Trygve Berge, Marius Myrstad

Bibliographic record

VenueEuropean Journal of Preventive Cardiology · 2023
Typearticle
Languageen
FieldMedicine
TopicCardiovascular Effects of Exercise
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineAthletesAtrial fibrillationInternal medicineLibrary scienceFamily medicinePhysical therapyComputer science

Abstract

fetched live from OpenAlex

The letter is a comment to the article: Masters Athlete Screening Study (MASS): incidence of cardiovascular disease and major adverse cardiac events and efficacy of screening over five years. Published online ahead-of-print 22 March 2023 in Eur J Prev Cardiol. A growing number of individuals ≥35 years, referred to as ‘master athletes’ (MAs), engage in vigorous physical activity and endurance sports, warranting increased awareness regarding the balance between the benefits of exercise and the risk of exercise-related adverse cardiovascular events. In the Master Athlete Screening Study (MASS), Morrison and colleagues evaluated the effectiveness of cardiovascular screening in Canadian MAs (mean age 55 years) over 5 years.1 Coronary artery disease (CAD) was the dominating diagnosis detected by screening, but arrhythmias were the most common cardiac events during follow-up. However, although the screening algorithm included yearly evaluation with a standard electrocardiogram (ECG), only 19 cases of atrial fibrillation (AF)/atrial flutter were diagnosed. Another eight were detected outside the screening programme. The low AF incidence compared to previous studies of MAs suggest that the screening method in MASS was ineffective at detecting AF or reflects a lower incidence of AF in this relatively young population of MAs. Previous studies among middle-aged and older male endurance athletes have revealed a high prevalence of AF of between 12% and 29%, but despite AF being the most prevalent arrhythmia among athletes, current recommendations regarding the screening of MAs are less focused on AF compared to CAD, cardiomyopathies, and ventricular arrhythmias.2,3 A plausible reason for this is that AF carries a lower risk for major adverse cardiac events. But with increasing age and concomitant cardiovascular risk factors, AF is associated with severe adverse outcomes such as stroke, heart failure, and death. Unfortunately, studies assessing stroke risk in MAs with AF are scarce, but MAs ≥65 years seem to resemble the broader population regarding an increased stroke risk related to AF.3 This observation highlights the relevance of AF and warrants a discussion about the optimal algorithms for AF detection in MAs. AF typically presents with short and rare episodes in MAs, suggesting a low sensitivity of standard and intermittent ECGs to detect paroxysmal AF. While Morrison and colleagues suggested 24-h-ECG as an additional diagnostic method in symptomatic individuals, screening algorithms aiming to detect paroxysmal arrhythmias in populations with a low arrhythmia burden should probably include prolonged ECG monitoring or intermittent recording with a consumer ECG device to improve detection rate. Devices capable of providing high-quality ECGs during exercise, such as smartwatches and patch ECGs, are now broadly available. Albeit validation in studies is needed, these methods may be more suitable among MAs. In general, screening should be reserved for situations where detecting the condition of interest would result in a meaningful response. We believe that different screening algorithms for AF are needed for different subpopulations of MAs, targeted towards those with symptoms suspicious of AF and those at increased risk of stroke. Given the high prevalence of AF, a more comprehensive strategy may be considered for MAs ≥65 years attending cardiovascular screening programmes, where both the risk of developing AF and the risk of stroke related to AF is highest. Despite emerging data on the features of the ‘athlete’s heart,’ it remains unknown to which extent exercise-induced cardiac remodelling in athletes translates into an increased risk of cardiovascular events. The algorithm suggested by Morrison and colleagues includes examination with echocardiography in MAs with concerning symptoms or ECG abnormalities. Concerning the detection of AF in these individuals, recently published data indicate that AF may be suspected in MAs with abnormal atrial function (reduced left atrial strain values).4 Other biomarkers suggestive of increased yield from AF screening, such as elevated NT-proBNP, are yet to be explored in MAs, particularly at the age ≥65. The yield of screening depends on both screening methods and the population evaluated. A common limitation of sports cardiology studies is that the term ‘athlete’ is poorly defined. A broad definition of MAs includes recreational and professional athletes of different age groups, some of whom have lifelong exposure to exercise and others who are late-onset athletes. If we are to screen MAs, then let`s do it right. Data from MASS and other prospective studies may identify subpopulations of athletes at risk of cardiovascular events and aid the development of more efficient screening strategies targeting high-risk populations.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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: Empirical
Teacher disagreement score0.639
Threshold uncertainty score0.448

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.020
GPT teacher head0.276
Teacher spread0.256 · 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 teacher head, 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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Citations5
Published2023
Admission routes1
Has abstractyes

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