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In search of the best method to predict acute coronary syndrome using only the electrocardiogram from the emergency department.
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- Forberg, Jakob L (author)
- Lund University,Lunds universitet,Medicin, Lund,Sektion II,Institutionen för kliniska vetenskaper, Lund,Medicinska fakulteten,Medicine, Lund,Section II,Department of Clinical Sciences, Lund,Faculty of Medicine
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- Green, Michael (author)
- Lund University,Lunds universitet,Beräkningsbiologi och biologisk fysik - Genomgår omorganisation,Institutionen för astronomi och teoretisk fysik - Genomgår omorganisation,Naturvetenskapliga fakulteten,Computational Biology and Biological Physics - Undergoing reorganization,Department of Astronomy and Theoretical Physics - Undergoing reorganization,Faculty of Science
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- Björk, Jonas (author)
- Lund University,Lunds universitet,Avdelningen för arbets- och miljömedicin,Institutionen för laboratoriemedicin,Medicinska fakulteten,Division of Occupational and Environmental Medicine, Lund University,Department of Laboratory Medicine,Faculty of Medicine
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- (creator_code:org_t)
- Elsevier BV, 2009
- 2009
- English 6 s.
- In: Journal of Electrocardiology. - : Elsevier BV. - 1532-8430 .- 0022-0736. ; 42:1, s. 58-63
- Journal article (peer-reviewed)
Abstract
Subject headings
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- INTRODUCTION: The aim of this study was to compare different methods to predict acute coronary syndrome (ACS) using only data from a single electrocardiogram (ECG) in the emergency department (ED). METHOD: We compared the ACS prediction abilities of classical ECG criteria, human expert ECG interpretation, a logistic regression model and an artificial neural network ensemble (ANN). The ED ECG and discharge diagnoses were retrieved for 861 patient visits to the ED for chest pain. Cross-validation was used to estimate the generalization performance of the logistic regression and the ANN model. RESULTS: The logistic regression model had the overall best performance in predicting ACS with an area under the receiver operating characteristic curve of 0.88. The sensitivities of logistic regression, ANN, expert physicians, and classical ECG criteria were 95%, 95%, 82%, and 75%, respectively, and the specificities were 54%, 44%, 63%, and 69%. CONCLUSION: Our logistic regression model was the best overall method to predict ACS, followed by our ANN. Decision support models have the potential to improve even experienced ECG readers' ability to predict ACS in the ED.
Subject headings
- MEDICIN OCH HÄLSOVETENSKAP -- Klinisk medicin -- Kardiologi (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Clinical Medicine -- Cardiac and Cardiovascular Systems (hsv//eng)
Keyword
- Neural network ensembles
- Myocardial infarction
- Acute coronary syndrome
- Diagnosis
- Unstable angina pectoris
- Electrocardiography
Publication and Content Type
- art (subject category)
- ref (subject category)
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- Forberg, Jakob L
- Green, Michael
- Björk, Jonas
- Ohlsson, Mattias
- Edenbrandt, Lars
- Öhlin, Hans
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- Ekelund, Ulf
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