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Predicting defibrillation success by 'genetic' programming in patients with out-of-hospital cardiac arrest
M Podbregar1, M Kovacic, A Podbregar-Mars
1Department for Intensive Internal Medicine, General Hospital Celje, Oblakova 5, Celje 3000, Slovenia. matej.podbregar@guest.arnes.si
Background:
In some patients with ventricular fibrillation (VF) there may be a better chance of successful defibrillation after a period of chest compression and ventilation before the defibrillation attempt. It is therefore important to know whether a defibrillation attempt will be successful. The predictive power of a model developed by 'genetic' programming (GP) to predict defibrillation success was studied.
Methods And Results:
203 defibrillations were administered in 47 patients with out-of-hospital cardiac arrest due to a cardiac cause. Maximal amplitude, a total energy of power spectral density, and the Hurst exponent of the VF electrocardiogram (ECG) signal were included in the model developed by GP. Positive and negative likelihood ratios of the model for testing data were 35.5 and 0.00, respectively. Using a model developed by GP on the complete database, 120 of the 124 unsuccessful defibrillations would have been avoided, whereas all of the 79 successful defibrillations would have been administered.
Conclusion:
The VF ECG contains information predictive of defibrillation success. The model developed by GP, including data from the time-domain, frequency-domain and nonlinear dynamics, could reduce the incidence of unsuccessful defibrillations.