Computer-Based CPR Simulation Towards Validation of AHA/ERC Guidelines

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.

RESULTS

Compared with current protocols, myocardial tissue oxygen volume was more than 5 times higher, and cerebral tissue oxygen volume was nearly doubled, during optimised CPR. While the optimal maximal sternal displacement (5.5 cm) and compression ratio (51%) found using our model agreed with the current American Heart Association guidelines, the optimal chest compression rate was lower (67 compressions min^-1). Similarly, the optimal ventilation strategy was more conservative than current guidelines, with an optimal minute ventilation of 1500 ml min^-1 and inspired fraction of oxygen of 80%. The end compression force was the parameter with the largest impact on CO, followed by PEEP, the compression ratio and the CC rate.

CONCLUSIONS

Our results indicate that current CPR protocols could potentially be improved. Excessive ventilation could be detrimental to organ oxygenation during CPR, due to the negative haemodynamic effect of increased pulmonary vascular resistance. Particular attention should be given to the chest compression force to achieve satisfactory CO. Future clinical trials aimed at developing improved CPR protocols should explicitly consider interactions between chest compression and ventilation parameters.

Mathematical Model of Blood Circulation with Compression of the Prototype's Mechanical CPR Waveform

The waveform of chest compressions directly affects the blood circulation of patients with cardiac arrest. Currently, few pieces of research have focused on the influence of the cardiopulmonary resuscitation (CPR) device's mechanical waveform on blood circulation. This study investigates the effect of the mechanical waveform from a novel CPR prototype on blood circulation and explores the optimal compression parameters of the mechanical waveform to optimize blood circulation. A novel CPR prototype was designed and built to establish a kinetic model during compressions. The prototype's mechanical waveforms at various operating conditions were obtained for comparison with manual waveforms and the investigation of the optimal compression parameters. The novel CPR prototype can complete chest compressions quickly and stably. The cardiac output (CO), coronary perfusion pressure (CPP), and cerebral flow (CF) obtained by mechanical waveform compressions (1.22367 ± 0.00942 L/min, 30.95083 ± 0.24039 mmHg, 0.31992 ± 0.00343 L/min, respectively) were significantly better than those obtained by manual waveform compressions (1.10783 ± 0.03601 L/min, 21.39210 ± 1.42771 mmHg, 0.29598 ± 0.01344 L/min, respectively). With the compression of the prototype, the blood circulation can be optimized at the compression depth of 50 mm, approximately 0.6 duty cycle, and approximately 110 press/min, which is of guiding significance for the practical use of CPR devices to rescue patients with cardiac arrest.

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. While the sternal displacement associated with the CC force agreed with the ERC guidelines, the CC rate and duty cycle were respectively higher and lower than the ones recommended by the ERC guidelines. The effect of these CC parameters on cardiac output (CO) were also assessed. The end compression force was the parameter with the largest impact on CO, while the compression rate and duty cycle scarcely influence it.Relevance- Our results may aid in understanding the underlying pathophysiology of cardiac arrest and help guide research into the refinement of CPR strategies, without sacrificing animals or conducting clinical trials, which are difficult to undertake in crisis scenarios.

The assessment of the kinematics of the rescuer in continuous chest compression during a 10-min simulation of cardiopulmonary resuscitation

BACKGROUND

In pursuit of improvement in cardiopulmonary resuscitation (CPR), new technologies for the measurement and assessment of CPR quality are implemented. In our study, we assessed the kinematics of the rescuer during continuous chest compression (CCC-CPR). The proper performance of the procedure is a survival predictor for patients with cardiac arrest (CA). The purpose of the study was a prospective assessment of the kinematics of the rescuer's body with consideration given to the depth and rate of chest compression (CC) as the indicator of properly performed CC maneuver by professional and non-professional rescuers during a simulation of a 10-min CCC using a manikin.

METHODS

Forty participants were enrolled in the study. CCC-CPR was performed in accordance with the 2015 AHA guidelines on a manikin positioned on the floor. Kinematic data on the movement were obtained from the measuring system (X-sens MVN Biomech) transmitting information from 17 inertial sensors. Measurement data were imported to the author's program RKO-Kinemat written in the Matlab and C # environments. Two groups of results were distinguished: Group I-results of CC with the depth of ≥ 40 mm and Group 2-CC results with the depth of < 40 mm.

RESULTS

The multiple regression model demonstrated that the path length, left knee flexion angle, and left elbow flexion angle were the essential elements of the rescuer's kinematics that facilitated achieving and maintaining the normal depth of CC.

CONCLUSIONS

We believe that raising the rescuer's hips by moving the center of the rescuer's body over the point of sternal compression increases the value of the CC force vector, thereby increasing the depth of CC. In addition, we observed that, during an effective CC, the rescuer was unable to maintain arms straight and, in consequence, a slight elbow flexion was observed. It, however, did not influence the quality of the maneuver.