Best leads in the standard electrocardiogram for the emergency detection of acute coronary syndrome.

Summary, in English

Background and Purpose: The purpose of this study was to determine which leads in the standard 12-lead electrocardiogram (ECG) are the best for detecting acute coronary syndrome (ACS) among chest pain patients in the emergency department. Methods: Neural network classifiers were used to determine the predictive capability of individual leads and combinations of leads from 862 ECCs from chest pain patients in the emergency department at Lund University Hospital. Results: The best individual lead was aVL, with an area under the receiver operating characteristic curve of 75.5%. The best 3-lead combination was III, aVL, and V-2, with a receiver operating characteristic area of 82.0%, compared with the 12-lead ECG performance of 80.5%. Conclusions: Our results indicate that leads III, aVL, and V2 are sufficient for computerized prediction of ACS. The present results are likely important in situations where the 12-lead ECG is impractical and for the creation of clinical decision support systems for ECG prediction of ACS.

• Author: Michael Green; Mattias Ohlsson; Jakob Lundager Forberg; Jonas Björk; Lars Edenbrandt; Ulf Ekelund
• Department/s: Computational Biology and Biological Physics - Has been reorganised; Medicine, Lund; Division of Occupational and Environmental Medicine, Lund University; Clinical Physiology (Lund)
• Publishing year: 2007
• Language: English
• Pages: 251-256
• Publication/Series: Journal of Electrocardiology
• Volume: 40
• Issue: 3
• Links: http://dx.doi.org/10.1016/j.jelectrocard.2006.12.011
• Document type: Journal article
• Publisher: Elsevier
• Topic: Cardiac and Cardiovascular Systems
• Keywords: acute coronary syndrome; artificial neural networks; myocardial infarction; electrocardiography
• Status: Published
• Project: AIR Lund Chest pain - More efficient and equal emergency care with advanced medical decision support tools
• ISBN/ISSN/Other: ISSN: 1532-8430