Publications by authors named "Rebecca Di Maio"
Article Synopsis
- The study aimed to optimize chest compressions during out-of-hospital cardiac arrest to enhance organ perfusion and survival rates.
- The research was conducted using a porcine model, examining relationships between mechanical measures (like compression depth and force) and physiological markers (like end-tidal CO2 and systolic blood pressure) during cardiopulmonary resuscitation (CPR).
- Results showed moderate to strong correlations between the amplitude of the impedance cardiogram and both compression depth and other physiological parameters, suggesting that the ICG could be a valuable feedback tool for improving CPR effectiveness.
Background: Algorithms to predict shock success based on VF waveform metrics could significantly enhance resuscitation by optimising the timing of defibrillation.
Objective: To investigate robust methods of predicting defibrillation success in VF cardiac arrest patients, by using a support vector machine (SVM) optimisation approach.
Methods: Frequency-domain (AMSA, dominant frequency and median frequency) and time-domain (slope and RMS amplitude) VF waveform metrics were calculated in a 4.
The 3-phase time-sensitive model by Weisfeldt and Becker in 2002 has resulted in a redirection of efforts toward developing treatment algorithms specific to each phase of cardiac arrest. In this study, a number of physiologic indicators of ventricular fibrillation (VF) duration were investigated. The bispectral index was recorded at 15-second intervals over 12 minutes and recordings of the atrial electrocardiogram and lead II electrocardiogram were acquired simultaneously using Notocord data acquisition software during sinus rhythm, ventricular tachycardia, and VF, and analyzed using a total of 30 porcine models.
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