Estimation of optimal pediatric chest compression depth by using computed tomography

Aim. To determine whether the chest compression depth of at least 1/3 of the Anteroposterior (AP) diameter of the chest and about 5 cm is appropriate for children of all age groups via chest computed tomography. Methods. The AP diameter of the chest, anterior chest wall diameter, and compressible diameter (Cd) were measured at the lower half of the sternum for patients aged 1-18 years using chest computed tomography. The mean ratio of 5 cm compression to the Cd of adult patients was used as the lower limit, and the mean ratio of 6 cm compression to the Cd of adult patients was used as the upper limit. Also, the depth of chest compression resulting in a residual depth <1 cm was considered to cause internal injury potentially. With the upper and lower limits, the compression ratios to the Cd were compared when compressions were performed at a depth of 1/3 the AP diameter of the chest and 5 cm for patients aged 1-18 years. Results. Among children aged 1-7 years, compressing 5 cm was deeper than 1/3 the AP diameter. Also, among children aged 1-5 years, 5 cm did not leave a residual depth of 1 cm, potentially causing intrathoracic injury. Conclusion. Current pediatric resuscitation guidelines of chest compression depth for children were too deep for younger children aged 1-7 years.

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Estimation of optimal chest compression depth based on chest computed tomography in Thai patients

10.26226/morressier.59b171e9d462b8028d896253 ◽

2017 ◽

Author(s):

Nalinas Khunkhlai

Keyword(s):

Computed Tomography ◽

Chest Compression ◽

Compression Depth ◽

Chest Computed Tomography

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Computed Tomography Imaging in Simulated Ongoing Cardiopulmonary Resuscitation: No Need to Switch Off the Chest Compression Device during Image Acquisition

Diagnostics ◽

10.3390/diagnostics11061122 ◽

2021 ◽

Vol 11 (6) ◽

pp. 1122

Author(s):

Jessica Graef ◽

Bernd A. Leidel ◽

Keno K. Bressem ◽

Janis L. Vahldiek ◽

Bernd Hamm ◽

...

Keyword(s):

Computed Tomography ◽

Cardiopulmonary Resuscitation ◽

Chest Compression ◽

Image Acquisition ◽

Circulatory Arrest ◽

Motion Artifacts ◽

Current Standard ◽

Ct Protocol ◽

Life Threatening ◽

Reconstruction Time

Computed tomography (CT) represents the current standard for imaging of patients with acute life-threatening diseases. As some patients present with circulatory arrest, they require cardiopulmonary resuscitation. Automated chest compression devices are used to continue resuscitation during CT examinations, but tend to cause motion artifacts degrading diagnostic evaluation of the chest. The aim was to investigate and evaluate a CT protocol for motion-free imaging of thoracic structures during ongoing mechanical resuscitation. The standard CT trauma protocol and a CT protocol with ECG triggering using a simulated ECG were applied in an experimental setup to examine a compressible thorax phantom during resuscitation with two different compression devices. Twenty-eight phantom examinations were performed, 14 with AutoPulse® and 14 with corpuls cpr®. With each device, seven CT examinations were carried out with ECG triggering and seven without. Image quality improved significantly applying the ECG-triggered protocol (p < 0.001), which allowed almost artifact-free chest evaluation. With the investigated protocol, radiation exposure was 5.09% higher (15.51 mSv vs. 14.76 mSv), and average reconstruction time of CT scans increased from 45 to 76 s. Image acquisition using the proposed CT protocol prevents thoracic motion artifacts and facilitates diagnosis of acute life-threatening conditions during continuous automated chest compression.

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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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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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