Decision support for the initial triage of patients with acute coronary syndromes
| Autor: | Lars Edenbrandt, Marie-Louise Nilsson, Samir Dzaferagic, Sven-Erik Olsson, Mattias Ohlsson, Per Sandkull, Hans Öhlin |
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| Rok vydání: | 2006 |
| Předmět: |
medicine.medical_specialty
Decision support system Heart disease Physiology medicine.medical_treatment education Myocardial Ischemia Revascularization Sensitivity and Specificity Automation Electrocardiography Physiology (medical) medicine Humans cardiovascular diseases Myocardial infarction medicine.diagnostic_test business.industry Internship and Residency Signal Processing Computer-Assisted General Medicine Gold standard (test) Emergency department medicine.disease Triage Surgery ROC Curve Emergency medicine Clinical Competence Neural Networks Computer business |
| Zdroj: | Clinical physiology and functional imaging. 26(3) |
| ISSN: | 1475-0961 |
| Popis: | 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 |
| Databáze: | OpenAIRE |
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