Background: Pre-hospital electrocardiogram (ECG) transmission to an expert for interpretation and triage reduces\r\ntime to acute percutaneous coronary intervention (PCI) in patients with ST elevation Myocardial Infarction (STEMI).\r\nIn order to detect all STEMI patients, the ECG should be transmitted in all cases of suspected acute cardiac\r\nischemia. The aim of this study was to examine the ability of an artificial neural network (ANN) to safely reduce the\r\nnumber of ECGs transmitted by identifying patients without STEMI and patients not needing acute PCI.\r\nMethods: Five hundred and sixty ambulance ECGs transmitted to the coronary care unit (CCU) in routine care\r\nwere prospectively collected. The ECG interpretation by the ANN was compared with the diagnosis (STEMI or not)\r\nand the need for an acute PCI (or not) as determined from the Swedish coronary angiography and angioplasty\r\nregister. The CCU physician�s real time ECG interpretation (STEMI or not) and triage decision (acute PCI or not)\r\nwere registered for comparison.\r\nResults: The ANN sensitivity, specificity, positive and negative predictive values for STEMI was 95%, 68%, 18% and\r\n99%, respectively, and for a need of acute PCI it was 97%, 68%, 17% and 100%. The area under the ANN�s receiver\r\noperating characteristics curve for STEMI detection was 0.93 (95% CI 0.89-0.96) and for predicting the need of acute\r\nPCI 0.94 (95% CI 0.90-0.97). If ECGs where the ANN did not identify a STEMI or a need of acute PCI were\r\ntheoretically to be withheld from transmission, the number of ECGs sent to the CCU could have been reduced by\r\n64% without missing any case with STEMI or a need of immediate PCI.\r\nConclusions: Our ANN had an excellent ability to predict STEMI and the need of acute PCI in ambulance ECGs,\r\nand has a potential to safely reduce the number of ECG transmitted to the CCU by almost two thirds.
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