An investigation of thrust, depth and the impedance cardiogram as measures of cardiopulmonary resuscitation efficacy in a porcine model of cardiac arrest.

OBJECTIVE: Optimising the depth and rate of applied chest compressions following out of hospital cardiac arrest is crucial in maintaining end organ perfusion and improving survival. The impedance cardiogram (ICG) measured via defibrillator pads produces a characteristic waveform during chest compressions with the potential to provide feedback on cardiopulmonary resuscitation (CPR) and enhance performance. The objective of this pre-clinical study was to investigate the relationship between mechanical and physiological markers of CPR efficacy in a porcine model and examine the strength of correlation between the ICG amplitude, compression depth and end-tidal CO2 (ETCO2). METHODS: Two experiments were performed using 24 swine (12 per experiment). For experiment 1, ventricular fibrillation (VF) was induced and mechanical CPR commenced at varying thrusts (0-60 kg) for 2 min intervals. Chest compression depth was recorded using a Philips QCPR device with additional recording of invasive physiological parameters: systolic blood pressure, ETCO2, cardiac output and carotid flow. For experiment 2, VF was induced and mechanical CPR commenced at varying depths (0-5 cm) for 2 min intervals. The ICG was recorded via defibrillator pads attached to the animal's sternum and connected to a Heartsine 500 P defibrillator. ICG amplitude, chest compression depth, systolic blood pressure and ETCO2 were recorded during each cycle. In both experiments the within-animal correlation between the measured parameters was assessed using a mixed effect model. RESULTS: In experiment 1 moderate within-animal correlations were observed between physiological parameters and compression depth (r=0.69-0.77) and thrust (r=0.66-0.82). A moderate correlation was observed between compression depth and thrust (r=0.75). In experiment 2 a strong within-animal correlation and moderate overall correlations were observed between ICG amplitude and compression depth (r=0.89, r=0.79) and ETCO2 (r=0.85, r=0.64). CONCLUSION: In this porcine model of induced cardiac arrest moderate within animal correlations were observed between mechanical and physiological markers of chest compression efficacy demonstrating the challenge in utilising a single mechanical metric to quantify chest compression efficacy. ICG amplitude demonstrated strong within animal correlations with compression depth and ETCO2 suggesting its potential utility to provide CPR feedback in the out of hospital setting to improve performance.

A support vector machine for predicting defibrillation outcomes from waveform metrics.

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 4.1Y window prior to defibrillation. Conventional prediction test validity of each waveform parameter was conducted and used AUC>0.6 as the criterion for inclusion as a corroborative attribute processed by the SVM classification model. The latter used a Gaussian radial-basis-function (RBF) kernel and the error penalty factor C was fixed to 1. A two-fold cross-validation resampling technique was employed. RESULTS: A total of 41 patients had 115 defibrillation instances. AMSA, slope and RMS waveform metrics performed test validation with AUC>0.6 for predicting termination of VF and return-to-organised rhythm. Predictive accuracy of the optimised SVM design for termination of VF was 81.9% (± 1.24 SD); positive and negative predictivity were respectively 84.3% (± 1.98 SD) and 77.4% (± 1.24 SD); sensitivity and specificity were 87.6% (± 2.69 SD) and 71.6% (± 9.38 SD) respectively. CONCLUSIONS: AMSA, slope and RMS were the best VF waveform frequency-time parameters predictors of termination of VF according to test validity assessment. This a priori can be used for a simplified SVM optimised design that combines the predictive attributes of these VF waveform metrics for improved prediction accuracy and generalisation performance without requiring the definition of any threshold value on waveform metrics.

Changes in the frequency spectrum, the P-P interval, and the bispectral index during ventricular fibrillation are physiologic indicators of ventricular fibrillation duration.

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. A number of frequency markers (fast Fourier transform and density and amplitude of peaks [DA]) were derived. There was a direct relationship between VF duration and bispectral index with a Pearson correlation coefficient (mean) of r = -0.91. The P-P interval recorded in the atria during VF, demonstrated similar findings (r = 0.97) when measured against VF duration. It was interesting to note that P waves were still apparent during VF despite the on-going chaotic activity in the ventricles. The DA was calculated for each episode of prolonged VF and an exponential relationship with VF duration was observed. The dominant frequency during VF, DA, the P-P interval, and the BIS index are all potential physiologic indicators of VF duration.