Evaluating current guidelines for cardiopulmonary resuscitation using an integrated computational model of the cardiopulmonary system.

Objective: We aimed to use a high-fidelity computational model that captures key interactions between the cardiovascular and pulmonary systems to investigate whether current CPR protocols could potentially be improved.

Methods: We developed and validated the computational model against available human data. We used a global optimisation algorithm to find CPR protocol parameters that optimise the outputs associated with return of spontaneous circulation in a cohort of 10 virtual subjects.

Identification of an optimal CPR chest compression protocol.

In this study, we used a high-fidelity integrated computational model of the respiratory and cardiovascular systems to investigate cardiopulmonary resuscitation (CPR) after cardiac arrest in a virtual healthy subject. For the purpose of this work, a newly developed thoracic model has been integrated to the current model, to study the influence of external chest compressions upon the arrested circulation during CPR. We evaluated the chest compression (CC) parameters, namely, end compression force, compression rate, and duty cycle to optimize the coronary perfusion pressure and the systolic blood pressure, using a genetic algorithm.