Document Detail


Decision support for the initial triage of patients with acute coronary syndromes.
MedLine Citation:
PMID:  16640509     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
Early revascularization of acute coronary syndromes improves the prognosis. It is of vital importance that the decision to treat the patient is taken as early as possible. The aim of this study was (i) to develop an automated tool for the analysis of electrocardiograms (ECGs) with regard to changes that indicate possible transmural ischaemia and (ii) to assess the influence of the tool on the ECG classifications of three interns with less than 12 months of experience in ECG reading. An artificial neural network was trained to automatically interpret ECGs using 3000 ECGs recorded at an emergency department. Thereafter, the performance of the network was evaluated using 1000 test ECGs. In the second step, three interns classified these test ECGs twice on different occasions, with and without the advice of the neural network. The gold standard was the classification made by two experienced cardiologists. On average, the three interns showed a sensitivity of 68% at a specificity of 92% without the advice of the neural network and a sensitivity of 93% at a specificity of 87% with the advice. The neural network itself showed a sensitivity of 95% at a specificity of 88%. The increase in sensitivity of 23-26% was significant (P<0.001) for all three interns. In conclusion, an artificial neural network can be trained to the improve performance in the interpretation of ST-segment changes in accordance with that of the experienced cardiologists.
Authors:
Sven-Erik Olsson; Mattias Ohlsson; Hans Ohlin; Samir Dzaferagic; Marie-Louise Nilsson; Per Sandkull; Lars Edenbrandt
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Publication Detail:
Type:  Journal Article; Research Support, Non-U.S. Gov't    
Journal Detail:
Title:  Clinical physiology and functional imaging     Volume:  26     ISSN:  1475-0961     ISO Abbreviation:  Clin Physiol Funct Imaging     Publication Date:  2006 May 
Date Detail:
Created Date:  2006-04-27     Completed Date:  2006-07-13     Revised Date:  2006-11-15    
Medline Journal Info:
Nlm Unique ID:  101137604     Medline TA:  Clin Physiol Funct Imaging     Country:  England    
Other Details:
Languages:  eng     Pagination:  151-6     Citation Subset:  IM    
Affiliation:
Department of Cardiology, Lund University, Lund, Sweden.
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MeSH Terms
Descriptor/Qualifier:
Automation
Clinical Competence
Electrocardiography / classification*
Humans
Internship and Residency
Myocardial Ischemia / diagnosis*
Neural Networks (Computer)*
ROC Curve
Sensitivity and Specificity
Signal Processing, Computer-Assisted*
Triage

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


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