Modelling ventricular fibrillation coarseness during cardiopulmonary resuscitation by mixed effects stochastic differential equations.

For patients undergoing cardiopulmonary resuscitation (CPR) and being in a shockable rhythm, the coarseness of the electrocardiogram (ECG) signal is an indicator of the state of the patient. In the current work, we show how mixed effects stochastic differential equations (SDE) models, commonly used in pharmacokinetic and pharmacodynamic modelling, can be used to model the relationship between CPR quality measurements and ECG coarseness. This is a novel application of mixed effects SDE models to a setting quite different from previous applications of such models and where using such models nicely solves many of the challenges involved in analysing the available data.

Which factors influence spontaneous state transitions during resuscitation?

Background: The clinical state (i.e. ventricular fibrillation/tachycardia: VF/VT, asystole: ASY, pulseless electrical activity: PEA, or return of spontaneous circulation, ROSC) during cardiopulmonary resuscitation determines patient management.

Acute ischemic heart disease alters ventricular fibrillation waveform characteristics in out-of hospital cardiac arrest.

Background: Although ventricular fibrillation waveform characteristics (VFWC) correlate with coronary perfusion pressure and may predict defibrillation outcome, recent animal data indicate that these waveform characteristics are altered in both acute myocardial infarction (AMI) and chronic coronary heart disease (CHD). We wanted to confirm these recent animal data in humans and explore the possibility for such characteristics to identify acute ischemia during cardiac arrest.

Methods: Data from all adult patients admitted to hospital after out-of-hospital VF cardiac arrest in Oslo between May 2003 and July 2007 were prospectively collected.

Development of the probability of return of spontaneous circulation in intervals without chest compressions during out-of-hospital cardiac arrest: an observational study.

Background: One of the factors that limits survival from out-of-hospital cardiac arrest is the interruption of chest compressions. During ventricular fibrillation and tachycardia the electrocardiogram reflects the probability of return of spontaneous circulation associated with defibrillation. We have used this in the current study to quantify in detail the effects of interrupting chest compressions.

Chest compression quality variables influencing the temporal development of ROSC-predictors calculated from the ECG during VF.

Background: Predictive measures that reflect the probability of return of spontaneous circulation (ROSC) if the patient is defibrillated can be calculated from the electrocardiogram during ventricular fibrillation (VF) and ventricular tachycardia (VT). It has not been studied how the quality of chest compressions affect the development of such ROSC predictors.

Materials And Methods: We have formulated a model for the effect of chest compressions on the ROSC predictor median-slope (MS).

Using within-patient correlation to improve the accuracy of shock outcome prediction for cardiac arrest.

Background: Analysis of the electrocardiogram (ECG) can to a certain extent predict if a cardiac arrest patient in ventricular fibrillation will get return of spontaneous circulation (ROSC) if defibrillated. The accuracy of such methods determines how useful it is clinically and for retrospective analysis.

Methods And Results: We have tested the accuracy of a new shock outcome prediction algorithm that is the first to use an updating algorithm capable of learning from previous shocks within a resuscitation effort.

Dynamics and state transitions during resuscitation in out-of-hospital cardiac arrest.

Background: The state or rhythm during resuscitation, i.e. ventricular fibrillation/tachycardia (VF/VT), asystole (ASY), pulseless electrical activity (PEA), or return of spontaneous circulation (ROSC) determines management.

Identifying approaches to improve the accuracy of shock outcome prediction for out-of-hospital cardiac arrest.

Background: Analysis of the electrocardiogram (ECG) can predict if a cardiac arrest patient in ventricular fibrillation is likely to have a return of spontaneous circulation if defibrillated. The accuracy of such methods determines how useful it is clinically and for retrospective analysis.

Methods And Results: We wanted to identify if there is a potential of improving prediction accuracy by adding peri-arrest factors to an ECG-based prediction system, or constructing a prediction system that adapts to each patient.