Dataset Information

Variability in chest compression rate calculations during pediatric cardiopulmonary resuscitation.

ABSTRACT:

Aim

The mathematical method used to calculate chest compression (CC) rate during cardiopulmonary resuscitation varies in the literature and across device manufacturers. The objective of this study was to determine the variability in calculated CC rates by applying four published methods to the same dataset.

Methods

This study was a secondary investigation of the first 200 pediatric cardiac arrest events with invasive arterial line waveform data in the ICU-RESUScitation Project (NCT02837497). Instantaneous CC rates were calculated during periods of uninterrupted CCs. The defined minimum interruption length affects rate calculation (e.g., if an interruption is defined as a break in CCs ≥ 2 s, the lowest possible calculated rate is 30 CCs/min). Average rates were calculated by four methods: 1) rate with an interruption defined as ≥ 1 s; 2) interruption ≥ 2 s; 3) interruption ≥ 3 s; 4) method #3 excluding top and bottom quartiles of calculated rates. American Heart Association Guideline-compliant rate was defined as 100-120 CCs/min. A clinically important change was defined as ±5 CCs/min. The percentage of events and epochs (30 s periods) that changed Guideline-compliant status was calculated.

Results

Across calculation methods, mean CC rates (118.7-119.5/min) were similar. Comparing all methods, 14 events (7%) and 114 epochs (6%) changed Guideline-compliant status.

Conclusion

Using four published methods for calculating CC rate, average rates were similar, but 7% of events changed Guideline-compliant status. These data suggest that a uniform calculation method (interruption ≥ 1 s) should be adopted to decrease variability in resuscitation science.

SUBMITTER: Landis WP

PROVIDER: S-EPMC7296394 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature

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Publications

Variability in chest compression rate calculations during pediatric cardiopulmonary resuscitation.

Landis William P WP Morgan Ryan W RW Reeder Ron W RW Graham Kathryn K Siems Ashley A Diddle J Wesley JW Pollack Murray M MM Maa Tensing T Fernandez Richard P RP Yates Andrew R AR Tilford Bradley B Ahmed Tageldin T Meert Kathleen L KL Schneiter Carleen C Bishop Robert R Mourani Peter M PM Naim Maryam Y MY Friess Stuart S Burns Candice C Manga Arushi A Franzon Deborah D Tabbutt Sarah S McQuillen Patrick S PS Horvat Christopher M CM Bochkoris Matthew M Carcillo Joseph A JA Huard Leanna L Federman Myke M Sapru Anil A Viteri Shirley S Hehir David A DA Notterman Daniel A DA Holubkov Richard R Dean J Michael JM Nadkarni Vinay M VM Berg Robert A RA Wolfe Heather A HA Sutton Robert M RM

Resuscitation 20200220

<h4>Aim</h4>The mathematical method used to calculate chest compression (CC) rate during cardiopulmonary resuscitation varies in the literature and across device manufacturers. The objective of this study was to determine the variability in calculated CC rates by applying four published methods to the same dataset.<h4>Methods</h4>This study was a secondary investigation of the first 200 pediatric cardiac arrest events with invasive arterial line waveform data in the ICU-RESUScitation Project (NC ...[more]

PMID: 32088254

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Project description:ObjectivesNew chest compression detection technology allows for the recording and graphical depiction of clinical cardiopulmonary resuscitation (CPR) chest compressions. The authors sought to determine the inter-rater reliability of chest compression pattern classifications by human raters. Agreement with automated chest compression classification was also evaluated by computer analysis.MethodsThis was an analysis of chest compression patterns from cardiac arrest patients enrolled in the ongoing Resuscitation Outcomes Consortium (ROC) Continuous Chest Compressions Trial. Thirty CPR process files from patients in the trial were selected. Using written guidelines, research coordinators from each of eight participating ROC sites classified each chest compression pattern as 30:2 chest compressions, continuous chest compressions (CCC), or indeterminate. A computer algorithm for automated chest compression classification was also developed for each case. Inter-rater agreement between manual classifications was tested using Fleiss's kappa. The criterion standard was defined as the classification assigned by the majority of manual raters. Agreement between the automated classification and the criterion standard manual classifications was also tested.ResultsThe majority of the eight raters classified 12 chest compression patterns as 30:2, 12 as CCC, and six as indeterminate. Inter-rater agreement between manual classifications of chest compression patterns was κ = 0.62 (95% confidence interval [CI] = 0.49 to 0.74). The automated computer algorithm classified chest compression patterns as 30:2 (n = 15), CCC (n = 12), and indeterminate (n = 3). Agreement between automated and criterion standard manual classifications was κ = 0.84 (95% CI = 0.59 to 0.95).ConclusionsIn this study, good inter-rater agreement in the manual classification of CPR chest compression patterns was observed. Automated classification showed strong agreement with human ratings. These observations support the consistency of manual CPR pattern classification as well as the use of automated approaches to chest compression pattern analysis.

Project description:BackgroundQuality of cardiopulmonary resuscitation (CPR) is key to increase survival from cardiac arrest. Providing chest compressions with adequate rate and depth is difficult even for well-trained rescuers. The use of real-time feedback devices is intended to contribute to enhance chest compression quality. These devices are typically based on the double integration of the acceleration to obtain the chest displacement during compressions. The integration process is inherently unstable and leads to important errors unless boundary conditions are applied for each compression cycle. Commercial solutions use additional reference signals to establish these conditions, requiring additional sensors. Our aim was to study the accuracy of three methods based solely on the acceleration signal to provide feedback on the compression rate and depth.Materials and methodsWe simulated a CPR scenario with several volunteers grouped in couples providing chest compressions on a resuscitation manikin. Different target rates (80, 100, 120, and 140 compressions per minute) and a target depth of at least 50 mm were indicated. The manikin was equipped with a displacement sensor. The accelerometer was placed between the rescuer's hands and the manikin's chest. We designed three alternatives to direct integration based on different principles (linear filtering, analysis of velocity, and spectral analysis of acceleration). We evaluated their accuracy by comparing the estimated depth and rate with the values obtained from the reference displacement sensor.ResultsThe median (IQR) percent error was 5.9% (2.8-10.3), 6.3% (2.9-11.3), and 2.5% (1.2-4.4) for depth and 1.7% (0.0-2.3), 0.0% (0.0-2.0), and 0.9% (0.4-1.6) for rate, respectively. Depth accuracy depended on the target rate (p < 0.001) and on the rescuer couple (p < 0.001) within each method.ConclusionsAccurate feedback on chest compression depth and rate during CPR is possible using exclusively the chest acceleration signal. The algorithm based on spectral analysis showed the best performance. Despite these encouraging results, further research should be conducted to asses the performance of these algorithms with clinical data.

Dataset Information

Variability in chest compression rate calculations during pediatric cardiopulmonary resuscitation.

Aim

Methods

Results

Conclusion

Publications

Variability in chest compression rate calculations during pediatric cardiopulmonary resuscitation.

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