Although guidelines have aided in the recognition of normal physiologic ECG findings in athletes, we demonstrated that even among experienced physicians, agreement on what constitutes an abnormal ECG is poor. Unlike other screening tools with laboratory
Modest agreement in ECG interpretation limits the application of ECG screening in young athletes
Introduction
Preparticipation screening inclusive of a 12-lead ECG is recommended by the European Society of Cardiology (ESC) for all young persons engaging in competitive sport1 in an effort to reduce the incidence of sudden cardiac death, as reported in the Veneto region of Italy after mandated screening.2 Although the strength of this evidence is still debated, many sporting bodies are implementing cardiac screening inclusive of an ECG.
The 12-lead ECG is a relatively cheap screening tool; however, the accuracy of the test is entirely dependent on individual interpretation of the pathologic findings, and its cost efficacy is dictated by a cascade of subsequent investigations to elucidate underlying pathology. There has been recent focus on refinement of criteria for ECG interpretation in athletes in an effort to reduce false-positive results of screening.3, 4, 5 However, there is a paucity of data on the extent of variability in ECG interpretation among experienced physicians when faced with a real-world sample of athletes’ ECGs in which the incidence of underlying pathology is low. Therefore, we sought to assess the variability of ECG interpretation among experienced physicians and their ability to identify true abnormal results when faced with a large number of screening ECGs.
Section snippets
Methods
The first 440 consecutively collected ECGs in our prospective study, which commenced in June 2011, were used for analysis.6 ECGs were deliberately unfiltered and nonenriched so that the prevalence of disease approximated that which could be expected in an athletic cohort. The inclusion and exclusion criteria have been described in detail elsewhere.6, 7 In brief, all subjects were elite athletes (age 16–35 years) and were not known to have any preexisting cardiac conditions.
These 440
Results
The 440 elite athletic subjects were aged 21 ± 5 years (range 16–35 years), and 36 (8.2%) were female. The cohort was predominantly (81.6%) Caucasian, with 14.5% indigenous Australian, Torres Strait Islander, or Pacific Islander/Maori, 1.6% Asian, and 2.3% African/South American.
Discussion
This study conveys important new information on the variability of detection of pathologic ECG abnormalities in a large unselected cohort of athletes undergoing ECG screening. The main finding was that interphysician variability of en masse athlete ECG interpretation is high, with at best moderate agreement between physicians experienced in athlete ECG interpretation. Suggested resultant investigations were many and varied, and the small number of athletes with cardiac pathology was missed on
Conclusion
There is significant variability in ECG interpretation, even among physicians with prior experience in athlete ECG analysis. This limitation must be considered before implementing population-based ECG screening for young asymptomatic athletes.
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Cited by (46)
Detection of Hypertrophic Cardiomyopathy Using a Convolutional Neural Network-Enabled Electrocardiogram
2020, Journal of the American College of CardiologyCitation Excerpt :The large-scale model creation and testing reported herein represents the first application of a multilayer CNN that was truly agnostic to any specific ECG features during its development. Several sets of ECG criteria for HCM in young athletes have been proposed with an emphasis toward distinguishing athletic adaptation and normal ECG variants from HCM, including the European (16), Stanford (17), and Seattle criteria (18), yet follow-up studies and attempts for external validation of these criteria have revealed inconsistencies in their diagnostic performance (19–22). However, the use of an AI-based deep learning network has the potential to overcome these challenges as it is not confined to any “classic” ECG criteria and it is trained to detect even the most subtle ECG patterns associated with structural changes in HCM that may be undetectable by the human eye or by traditional automated algorithms.
Use of the electrocardiogram in pre‐participation screening of athletes: For or against?
2017, Revista Portuguesa de Cardiologia
Dr. Brosnan is the recipient of a National Health and Medical Research Council (NHMRC) PhD scholarship. Dr. LaGerche is the recipient of an NHMRC postdoctoral scholarship. Dr. Kumar is the recipient of a Neil Hamilton Fairley Overseas Research Scholarship co-funded by the National Heart Foundation of Australia and NHMRC. Dr. Kalman is the recipient of an NHMRC practitioner fellowship.