Technical factors weaken the clinical relevance of manikin measurements of mechanical chest compression depth

Introduction: Current guidelines require a 50 mm compression depth for manual chest compression. During mechanical chest compression, however, because of the design of each device, whether this depth yields the most optimal hemodynamic efficacy remains to be tested. In this study, we investigated the effects of compression depth on hemodynamics efficacy during mechanical CPR with the Weil Mini Chest Compressor in a porcine model. Hypothesis: There is no significant difference in hemodynamic efficacy between compression depth of 30 mm and 50 mm during mechanical CPR with the Weil Mini Chest Compressor. Methods: Ten male domestic pigs weighing 34±2 kg were utilized. Ventricular fibrillation was electrically induced and untreated for 7 min. The animals were then randomized to receive compression depth of 30 mm or 50 mm. Coincident with the start of precordial compression, the animals were mechanically ventilated at a rate of 10 breaths per minute. Defibrillation was attempted by a single 150 J shock. If resuscitation was not successful, CPR was resumed for 2 mins prior to the next defibrillation until either successful resuscitation or for a total of 15 mins. Results: All animals were successfully resuscitated. There were no differences in coronary perfusion pressure (CPP), end-tidal carbon dioxide (ETCO2) and carotid blood flow (CBF) between the two groups (Table). A significantly less rib fracture was observed in the 30 mm group [0 (0-0) vs 1.2 (0-2), p<0.05]. Conclusion: Similar hemodynamic efficacy was observed between 30 and 50 mm compression depth during mechanical CPR with the Weil Mini Chest Compressor.

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Manual CPR Vs Mechanical CPR: Which One Is More Effective During Ambulance Transport?

10.21203/rs.3.rs-29271/v1 ◽

2020 ◽

Author(s):

İshak Şan ◽

Burak Bekgöz ◽

Mehmet Ergin ◽

Eren Usul

Keyword(s):

Chest Compression ◽

Training Model ◽

Chest Compressions ◽

Compression Depth ◽

Compression Device ◽

Mechanical Chest Compression ◽

Ambulance Transport ◽

Mechanical Cpr ◽

Off Time ◽

Trial Setting

Abstract Objectives We aimed to evaluate and compare the qualities of chest compressions performed manually by healthcare professionals and by a mechanical chest compression device on a training model during an ambulance transfer. DesignThis is an experimental trial. Setting This study was performed by the EMS of Ankara City (Capital of Turkey). 20 (10 male and 10 female) paramedic participated the study. We used LUCAS 2 as mechanical chest compression device in the study. A total of 40 rounds were driven on the track; in that moving ambulance, the model was applied chest compression in 20 rounds by paramedics, while in 20 rounds were applied by mechanical chest compression device. The depth, rate and hands-off time of chest compression were measured by means of the model's recording system. Results The median chest compression rate was 120.1 compressions per minute (IQR 25–75%=117.9–133.5) for the paramedics, whereas it was 102.3 compressions per minute for the mechanical chest compression device (IQR 25–75%=102.1–102.7) (p<0.001). The median chest compression depth was 38.9 millimeters (IQR 25–75%=32.9–45.5) for the paramedics, whereas it was 52.7 millimeters for the mechanical chest compression device (IQR 25–75%=51.8–55.0) (p<0.001). The median hands-off time during cardiopulmonary resuscitation was 6.9% (IQR 25–75=5.0–10.1%) for the paramedics and 9% (IQR 25–75%=8.2–12.5%) (p=0.09). Conclusion Chest compressions performed by the mechanical chest compression device were found to be within the range recommended by the guidelines in terms of both speed and duration.

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Compression depth of 30 mm has similar efficacy and fewer complications versus 50 mm during mechanical chest compression with miniaturized chest compressor in a porcine model of cardiac arrest

Journal of Thoracic Disease ◽

10.21037/jtd-21-812 ◽

2021 ◽

Vol 0 (0) ◽

pp. 0-0

Author(s):

Lian Liang ◽

Zuyong Li ◽

Ran Chen ◽

Siqi Liu ◽

Tianen Zhou ◽

...

Keyword(s):

Cardiac Arrest ◽

Chest Compression ◽

Porcine Model ◽

Similar Efficacy ◽

Compression Depth ◽

Mechanical Chest Compression

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Testing mechanical chest compression devices of different design for their suitability for prehospital patient transport - a simulator-based study

BMC Emergency Medicine ◽

10.1186/s12873-021-00409-3 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Maximilian Jörgens ◽

Jürgen Königer ◽

Karl-Georg Kanz ◽

Torsten Birkholz ◽

Heiko Hübner ◽

...

Keyword(s):

Chest Compression ◽

User Satisfaction ◽

Scale Up ◽

Physical Effort ◽

Patient Transport ◽

Construction Design ◽

Device Stability ◽

Mechanical Chest Compression ◽

Electrical Devices ◽

Hospital Transport

Abstract Background Mechanical chest compression (mCPR) offers advantages during transport under cardiopulmonary resuscitation. Little is known how devices of different design perform en-route. Aim of the study was to measure performance of mCPR devices of different construction-design during ground-based pre-hospital transport. Methods We tested animax mono (AM), autopulse (AP), corpuls cpr (CC) and LUCAS2 (L2). The route had 6 stages (transport on soft stretcher or gurney involving a stairwell, trips with turntable ladder, rescue basket and ambulance including loading/unloading). Stationary mCPR with the respective device served as control. A four-person team carried an intubated and bag-ventilated mannequin under mCPR to assess device-stability (displacement, pressure point correctness), compliance with 2015 ERC guideline criteria for high-quality chest compressions (frequency, proportion of recommended pressure depth and compression-ventilation ratio) and user satisfaction (by standardized questionnaire). Results All devices performed comparable to stationary use. Displacement rates ranged from 83% (AM) to 11% (L2). Two incorrect pressure points occurred over 15,962 compressions (0.013%). Guideline-compliant pressure depth was > 90% in all devices. Electrically powered devices showed constant frequencies while muscle-powered AM showed more variability (median 100/min, interquartile range 9). Although physical effort of AM use was comparable (median 4.0 vs. 4.5 on visual scale up to 10), participants preferred electrical devices. Conclusion All devices showed good to very good performance although device-stability, guideline compliance and user satisfaction varied by design. Our results underline the importance to check stability and connection to patient under transport.

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Effect of the Use of Metronome Feedback on the Quality of Pediatric Cardiopulmonary Resuscitation

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph18158087 ◽

2021 ◽

Vol 18 (15) ◽

pp. 8087

Author(s):

Dongjun Yang ◽

Wongyu Lee ◽

Jehyeok Oh

Keyword(s):

Cardiopulmonary Resuscitation ◽

Chest Compression ◽

Healthcare Providers ◽

Compression Rate ◽

Audio Feedback ◽

Simple Method ◽

Compression Depth ◽

Pediatric Resuscitation ◽

Cpr Quality

Although the use of audio feedback with devices such as metronomes during cardiopulmonary resuscitation (CPR) is a simple method for improving CPR quality, its effect on the quality of pediatric CPR has not been adequately evaluated. In this study, 64 healthcare providers performed CPR (with one- and two-handed chest compression (OHCC and THCC, respectively)) on a pediatric resuscitation manikin (Resusci Junior QCPR), with and without audio feedback using a metronome (110 beats/min). CPR was performed on the floor, with a compression-to-ventilation ratio of 30:2. For both OHCC and THCC, the rate of achievement of an adequate compression rate during CPR was significantly higher when performed with metronome feedback than that without metronome feedback (CPR with vs. without feedback: 100.0% (99.0, 100.0) vs. 94.0% (69.0, 99.0), p < 0.001, for OHCC, and 100.0% (98.5, 100.0) vs. 91.0% (34.5, 98.5), p < 0.001, for THCC). However, the rate of achievement of adequate compression depth during the CPR performed was significantly higher without metronome feedback than that with metronome feedback (CPR with vs. without feedback: 95.0% (23.5, 99.5) vs. 98.5% (77.5, 100.0), p = 0.004, for OHCC, and 99.0% (95.5, 100.0) vs. 100.0% (99.0, 100.0), p = 0.003, for THCC). Although metronome feedback during pediatric CPR could increase the rate of achievement of adequate compression rates, it could cause decreased compression depth.

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Effectiveness of mechanical chest compression for out-of-hospital cardiac arrest patients in an emergency department

Journal of the Chinese Medical Association ◽

10.1016/j.jcma.2015.01.005 ◽

2015 ◽

Vol 78 (6) ◽

pp. 360-363 ◽

Cited By ~ 2

Author(s):

Ching-Kuo Lin ◽

Mei-Chin Huang ◽

Yu-Tung Feng ◽

Wei-Hsuan Jeng ◽

Te-Cheng Chung ◽

...

Keyword(s):

Emergency Department ◽

Cardiac Arrest ◽

Chest Compression ◽

Mechanical Chest Compression ◽

Hospital Cardiac Arrest

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Recognition of Abnormal Chest Compression Depth Using One-Dimensional Convolutional Neural Networks

Sensors ◽

10.3390/s21030846 ◽

2021 ◽

Vol 21 (3) ◽

pp. 846

Author(s):

Liang Zhao ◽

Yu Bao ◽

Yu Zhang ◽

Ruidong Ye ◽

Aijuan Zhang

Keyword(s):

Chest Compression ◽

Sensor Data ◽

Evaluation Procedure ◽

Support Vector ◽

Portable Devices ◽

Sensor Signal ◽

Distance Measurements ◽

Compression Depth ◽

One Dimensional ◽

Starting Point

When the displacement of an object is evaluated using sensor data, its movement back to the starting point can be used to correct the measurement error of the sensor. In medicine, the movements of chest compressions also involve a reciprocating movement back to the starting point. The traditional method of evaluating the effects of chest compression depth (CCD) is to use an acceleration sensor or gyroscope to obtain chest compression movement data; from these data, the displacement value can be calculated and the CCD effect evaluated. However, this evaluation procedure suffers from sensor errors and environmental interference, limiting its applicability. Our objective is to reduce the auxiliary computing devices employed for CCD effectiveness evaluation and improve the accuracy of the evaluation results. To this end, we propose a one-dimensional convolutional neural network (1D-CNN) classification method. First, we use the chest compression evaluation criterion to classify the pre-collected sensor signal data, from which the proposed 1D-CNN model learns classification features. After training, the model is used to classify and evaluate sensor signal data instead of distance measurements; this effectively avoids the influence of pressure occlusion and electromagnetic waves. We collect and label 937 valid CCD results from an emergency care simulator. In addition, the proposed 1D-CNN structure is experimentally evaluated and compared against other CNN models and support vector machines. The results show that after sufficient training, the proposed 1D-CNN model can recognize the CCD results with an accuracy rate of more than 95%. The execution time suggests that the model balances accuracy and hardware requirements and can be embedded in portable devices.

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