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Enregistrement W4379094728 · doi:10.1093/eurjpc/zwad188

Atrial fibrillation is the most prevalent cardiac condition in master athletes

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

Notice bibliographique

RevueEuropean Journal of Preventive Cardiology · 2023
Typearticle
Langueen
DomaineMedicine
ThématiqueCardiovascular Effects of Exercise
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésMedicineAthletesAtrial fibrillationInternal medicineLibrary scienceFamily medicinePhysical therapyComputer science

Résumé

récupéré en direct d'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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,004
score de la tête « metaresearch » (Gemma)0,001
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,639
Score d'incertitude au seuil0,448

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0040,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,020
Tête enseignante GPT0,276
Écart entre enseignants0,256 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations5
Publié2023
Routes d'admission1
Résumé présentoui

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