Mattias Ohlsson
Professor
In search of the best method to predict acute coronary syndrome using only the electrocardiogram from the emergency department.
Författare
Summary, in English
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.
Avdelning/ar
- Medicin/akutsjukvård, Lund
- Beräkningsbiologi och biologisk fysik - Genomgår omorganisation
- Avdelningen för arbets- och miljömedicin
- Nuklearmedicin, Malmö
- Kardiologi
Publiceringsår
2009
Språk
Engelska
Sidor
58-63
Publikation/Tidskrift/Serie
Journal of Electrocardiology
Volym
42
Issue
1
Länkar
Dokumenttyp
Artikel i tidskrift
Förlag
Elsevier
Ämne
- Cardiac and Cardiovascular Systems
Nyckelord
- Neural network ensembles
- Myocardial infarction
- Acute coronary syndrome
- Diagnosis
- Unstable angina pectoris
- Electrocardiography
Status
Published
Projekt
- AIR Lund Chest pain - More efficient and equal emergency care with advanced medical decision support tools
Forskningsgrupp
- Nuclear medicine, Malmö
ISBN/ISSN/Övrigt
- ISSN: 1532-8430