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Mean Coronary Perfusion Pressure for animals receiving CPR based on AHA Recommendations vs Human-Controlled CPR vs Machine-Controlled CPR CPP for each minute of CPR is plotted along with the linear curves used to quantify the rate of change of CPP per minute. AHA, American Heart Association; CPP, coronary perfusion pressure; CPR, cardiopulmonary resuscitation; HC-CPR, human-controlled CPR; MC-CPR, machine-controlled CPR.
Source publication
Objectives
We evaluated the feasibility of optimising coronary perfusion pressure (CPP) during cardiopulmonary resuscitation (CPR) with a closed-loop, machine-controlled CPR system (MC-CPR) that sends real-time haemodynamic feedback to a set of machine learning and control algorithms which determine compression/decompression characteristics over ti...
Contexts in source publication
Context 1
... of all haemodynamics calculated for each 5-min interval over the 30-min CPR period. Diastolic and systolic blood pressures were significant between groups (p < 0.05). CPP over the 30 min of CPR was also significantly different between groups (p < 0.001), and the disparity between MC-CPR and the other two groups increased as time approached 30 min (Fig. 3). After the initial drop from baseline, CPP consistently increased for MC-CPR and decreased for AHA-CPR, whereas for HC-CPR the CPP increased during the first 15 min and then decreased. The areas under the CPP curve (AUC) were calculated for every animal. The mean AUC of HC-CPR (520 AE 27 mmHg*sec) and MC-CPR (570 AE 68 mmHg*sec) were ...
Context 2
... has the potential to account for inter-and intra-patient variability. Inter-patient variability was indicated in Fig. 2, as the automated algorithms were able to vary CPR depths in an effort to find the optimal depths in the long term. Intrapatient variability was exhibited in the time-variance of perfusion during prolonged CPR, displayed in Figs. 3 and ...
Citations
... Sebastian et al. evaluated the feasibility of using a machinecontrolled closed-loop CPR (MC-CPR) system to optimize coronary perfusion pressure (CPP) during CPR. Their real-time hemodynamic simulations demonstrated that MC-CPR, controlled by a closed-loop machine, significantly outperformed the AHA CPR guidelines in improving coronary perfusion pressure [30]. Jaureguibeitia et al. proposed a machine learning algorithm for impedance-based ventilation detection in mechanical CPR. ...
The application of feedback devices for cardiopulmonary resuscitation (CPR) can effectively enhance the quality of life-saving treatment during CPR. This article is a narrative review that selects literature on feedback devices used in chest compression and provides a comprehensive review and analysis of the development and current research status of feedback devices used in CPR. The development status of visual–auditory feedback devices used in chest compression at home and abroad and the advantages and disadvantages of existing feedback devices are the main points of discussion. Based on existing devices and technologies, this article explores the difficulties and deficiencies of current feedback device development. Finally, we propose the future development direction of CPR feedback equipment combined with physiological information data-monitoring devices.
... It has been over 60 years since CPR was proposed to rescue patients with cardiac arrest [1]. The method of CPR is constantly improved with clinical experiments and scientific analysis, and improving the quality of CPR has become a major concern [2][3][4] in recent years. CPR quality is strongly related to the compression waveform during compressions. ...
... After obtaining the compression waveforms under various conditions, we chose to use the cardiac output (CO) [34], coronary perfusion pressure (CPP) [3,4,35], and cerebral flow (CF) [36,37] as the criteria for evaluating the blood circulation effect. CO calculates the blood flow pumped out of the heart per minute, CPP calculates the pressure gradient between the aorta and the right atrium during the diastolic period, and CF calculates the blood flow of the brain per minute. ...
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.
