Literature DB >> 24563390

Visual or computer-based measurements: important for interpretation of athletes' ECG.

H M Berge1, K Steine, T E Andersen, E E Solberg, K Gjesdal.   

Abstract

BACKGROUND: ECG is recommended for preparticipation health examination in athletes. Owing to a lack of consensus on how to read and interpret athletes' ECGs, different criteria for abnormality are used. AIMS: To compare the prevalence of abnormal ECGs and test the correlation between visual and computer-based measurements.
METHODS: In a preparticipation cardiac screening examination of 595 male professional soccer players aged 18-38 years, ECGs were categorised according to the European Society of Cardiology's (ESC) recommendations and the Seattle criteria, respectively. Visual measurements were conducted with callipers on-screen on the averaged PQRST complex in each lead, calculated by the trimmed mean. Computer-based measurements were derived from the medium beat. Heart rhythm and conduction were scored visually by a cardiologist. Categorical variables were compared by κ statistics (K) and continuous variables by intraclass correlation.
RESULTS: ECGs of good quality were available from 579 players. According to the ESC's recommendations and Seattle criteria, respectively, ECGs were categorised as abnormal in 171 (29.5%) vs 64 (11.1%) players after visual assessment, and in 293 (50.6%; K=0.395) vs 127 (21.9%; K=0.564) after computer-based measurements. Intraclass correlation was very good for measurements of R and S wave amplitudes and moderate to very good for intervals. K was very good for pathological Q wave amplitudes and moderate for T wave inversions.
CONCLUSIONS: Abnormal ECGs were more than twice as common after computer-based than after visual measurements. Such a difference will markedly influence the number of athletes who need further examinations. Reference values may need adjustments dependent on measurement methods.

Entities:  

Keywords:  Athletics; Cardiology prevention; Soccer

Mesh:

Year:  2014        PMID: 24563390     DOI: 10.1136/bjsports-2014-093412

Source DB:  PubMed          Journal:  Br J Sports Med        ISSN: 0306-3674            Impact factor:   13.800


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3.  Automatic Triage of 12-Lead ECGs Using Deep Convolutional Neural Networks.

Authors:  Rutger R van de Leur; Lennart J Blom; Efstratios Gavves; Irene E Hof; Jeroen F van der Heijden; Nick C Clappers; Pieter A Doevendans; Rutger J Hassink; René van Es
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  3 in total

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