Quantitative Ventricular Fibrillation Metrics in a Biosignal Guided Cardiopulmonary Resuscitation Device for Cardiac Arrest and Their Translation to Clinical Data

Out of hospital cardiac arrest is a major cause of mortality with an estimated yearly incidence of 350,000 in the United States alone. Cardiopulmonary resuscitation (CPR) is a treatment for cardiac arrest involving chest compressions and rescues breaths that can save lives but is limited by the fact that it currently treats all patients in a 'one size fits all' approach. This work describes an adaptive approach to chest compressions controlled by a mechanical device that receives biosignals from the patient it treats. The device is capable of adjusting its chest compression parameters such as rate and depth in response to the biosignals it receives. We focused on integrating the quantitative electrocardiogram (QECG) of the ventricular fibrillation signal, a biosignal shown to respond to increased perfusion of the myocardium during CPR, into a chest compression algorithm controlled by the adaptive chest compression device. QECG is readily available for cardiac arrest patients since ECG analysis is standard of care in cardiac arrest. In our first aim we developed the adaptive chest compression device and tested it in animal feasibility studies which demonstrated that it responded appropriately to the biosignals it received. Next, in a computational model of adaptive chest compressions, adjustments in chest compression depth yielded the largest increase in cardiac output in patients with simulated variable physiology. In follow-up animal studies, select QECG measures responded to changes in chest compression parameters which demonstrated the initial feasibility of QECG measures as a potential biosignal in this model. We found that the QECG measures of median slope, centroid frequency, and log of the absolute correlation responded to changes in chest compression rate in the early phase of chest compressions. We found that in late phases of chest compressions the QECG measure median slope responded to chest compression rate changes and the QECG measure AMSA responded to chest compression duty cycle changes. Our second aim sought to retrospectively translate the findings in the first aim animal studies to human clinical data in the continuous chest compression trial of the Resuscitation Outcomes Consortium (ROC). The clinical trial provided us with ECG and compression data in covering thousands of cardiac arrest events. All QECG metrics in the clinical data set was predictive of shock outcome and chest compression rate along with chest compression bout duration were predictive of survival. However, when controlled for the presenting first rhythm status and demographic variables, only chest compression bout duration was predictive of survival. In addition to the predictive value of chest compression parameters and QECG measures, associations were found between varying chest compression parameters averaged across bouts of compressions with change in QECG values (dQECG) in the clinical data. Chest compression rate was found to be predictive of the dQECG metric median slope (dMS) and the dQECG metric (dAMSA). Dosed compression rate was found to be predictive of the dQECG metric dMS as well. dCF responded to changes in chest compression duty cycle. These findings provide a foundation for delivering adaptive chest compressions with the potential of improving survival outcomes to cardiac arrest.