Dataset Information

Automated mechanical cardiopulmonary resuscitation devices versus manual chest compressions in the treatment of cardiac arrest: protocol of a systematic review and meta-analysis comparing machine to human.

ABSTRACT:

Introduction

Cardiac arrest is a leading cause of death in industrialised countries. Cardiopulmonary resuscitation (CPR) guidelines follow the principles of closed chest compression as described for the first time in 1960. Mechanical CPR devices are designed to improve chest compression quality, thus considering the improvement of resuscitation outcomes. This protocol outlines a systematic review and meta-analysis methodology to assess trials investigating the therapeutic effect of automated mechanical CPR devices at the rate of return of spontaneous circulation, neurological state and secondary endpoints (including short-term and long-term survival, injuries and surrogate parameters for CPR quality) in comparison with manual chest compressions in adults with cardiac arrest.

Methods and analysis

A sensitive search strategy will be employed in established bibliographic databases from inception until the date of search, followed by forward and backward reference searching. We will include randomised and quasi-randomised trials in qualitative analysis thus comparing mechanical to manual CPR. Studies reporting survival outcomes will be included in quantitative analysis. Two reviewers will assess independently publications using a predefined data collection form. Standardised tools will be used for data extraction, risks of bias and quality of evidence. If enough studies are identified for meta-analysis, the measures of association will be calculated by dint of bivariate random-effects models. Statistical heterogeneity will be evaluated by I²-statistics and explored through sensitivity analysis. By comprehensive subgroup analysis we intend to identify subpopulations who may benefit from mechanical or manual CPR techniques. The reporting follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement.

Ethics and dissemination

No ethical approval will be needed because data from previous studies will be retrieved and analysed. Most resuscitation studies are conducted under an emergency exception for informed consent. This publication contains data deriving from a dissertation project. We will disseminate the results through publication in a peer-reviewed journal and at scientific conferences.

Prospero registration number

CRD42017051633.

SUBMITTER: Obermaier M

PROVIDER: S-EPMC7887349 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature

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Publications

Automated mechanical cardiopulmonary resuscitation devices versus manual chest compressions in the treatment of cardiac arrest: protocol of a systematic review and meta-analysis comparing machine to human.

Obermaier Manuel M Zimmermann Johannes B JB Popp Erik E Weigand Markus A MA Weiterer Sebastian S Dinse-Lambracht Alexander A Muth Claus-Martin CM Nußbaum Benedikt L BL Gräsner Jan-Thorsten JT Seewald Stephan S Jensen Katrin K Seide Svenja E SE

BMJ open 20210215 2

<h4>Introduction</h4>Cardiac arrest is a leading cause of death in industrialised countries. Cardiopulmonary resuscitation (CPR) guidelines follow the principles of closed chest compression as described for the first time in 1960. Mechanical CPR devices are designed to improve chest compression quality, thus considering the improvement of resuscitation outcomes. This protocol outlines a systematic review and meta-analysis methodology to assess trials investigating the therapeutic effect of autom ...[more]

PMID: 33589455

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Project description:AimPropose new metrics of impulsiveness of manual chest compressions (CCs) that account for shape and duration, separate the characteristics of the compressive part of the CC cycle from those of the recoil part, and are uncorrelated to CC depth and rate.MethodsWe conducted a retrospective analysis of adult out-of-hospital cardiac arrest monitor-defibrillator recordings having CPR data. Specifically, episodes of adult patients with ≥ 1000 compressions free of leaning were examined. CCs were obtained from the depth signal of the valid episodes, and we calculated the novel metrics: compression area index (CAI), recoil area index (RAI), compression impulsiveness index (CII) and recoil impulsiveness index (RII). Generalized linear mixed-effects models and Jonckheere-Terpstra trend analyses were employed to measure differences between populations and trends, and the absolute value of Pearson's correlation coefficient |r| was used to report dependence between variables. Statistics are reported as median and interquartile range.ResultsWe analyzed 982,340 CCs corresponding to 453 episodes, for which we calculated their CAI, RAI and duty cycle (DC). We analyzed the metrics for various populations: age, sex, any ROSC achieved and disposition, and found that CAI was significantly different according to patient disposition and RAI relative to age and sex (p<0.05). None of the metrics was correlated strongly to depth or rate (|r| values of 0.22 or smaller), and all of them varied for CC series corresponding to the same rescuer over the course of resuscitation (ptrend<0.05). However, we observed that the metrics are not balanced, in that for any value of DC, CAI and RAI span almost their entire ranges.ConclusionThe proposed metrics correctly and completely describe manual CC waveforms, improve upon the DC, since they depend on the signal waveform, and provide additional information to current indicators of quality CPR, depth and rate. Furthermore, they allow to differentiate the compressive and recoil parts of the CC cycle, reflecting influence of the rescuer (via CAI or CII) and of the biomechanics of the patient's chest (via RAI or RII). Thus, they have the potential to contribute to better understanding CPR dynamics and, eventually, to enhanced quality of CPR practice as additional indicators of proper manual CC technique.

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:IntroductionOur goal was to systematically review contemporary literature comparing the relative effectiveness of two mechanical compression devices (LUCAS and AutoPulse) to manual compression for achieving return of spontaneous circulation (ROSC) in patients undergoing cardiopulmonary resuscitation (CPR) after an out-of-hospital cardiac arrest (OHCA).MethodsWe searched medical databases systematically for randomized controlled trials (RCT) and observational studies published between January 1, 2000-October 1, 2020 that compared mechanical chest compression (using any device) with manual chest compression following OHCA. We only included studies in the English language that reported ROSC outcomes in adult patients in non-trauma settings to conduct random-effects metanalysis and trial sequence analysis (TSA). Multivariate meta-regression was performed using preselected covariates to account for heterogeneity. We assessed for risk of biases in randomization, allocation sequence concealment, blinding, incomplete outcome data, and selective outcome reporting.ResultsA total of 15 studies (n = 18474), including six RCTs, two cluster RCTs, five retrospective case-control, and two phased prospective cohort studies, were pooled for analysis. The pooled estimates' summary effect did not indicate a significant difference (Mantel-Haenszel odds ratio = 1.16, 95% confidence interval, 0.97 to 1.39, P = 0.11, I2 = 0.83) between mechanical and manual compressions during CPR for ROSC. The TSA showed firm evidence supporting the lack of improvement in ROSC using mechanical compression devices. The Z-curves successfully crossed the TSA futility boundary for ROSC, indicating sufficient evidence to draw firm conclusions regarding these outcomes. Multivariate meta-regression demonstrated that 100% of the between-study variation could be explained by differences in average age, the proportion of females, cardiac arrests with shockable rhythms, witnessed cardiac arrest, bystander CPR, and the average time for emergency medical services (EMS) arrival in the study samples, with the latter three attaining statistical significance.ConclusionMechanical compression devices for resuscitation in cardiac arrests are not associated with improved rates of ROSC. Their use may be more beneficial in non-ideal situations such as lack of bystander CPR, unwitnessed arrest, and delayed EMS response times. Studies done to date have enough power to render further studies on this comparison futile.

Project description:Importance:Out-of-hospital cardiac arrest is a common scenario facing prehospital emergency medical services (EMS) professionals and nearly always involves either manual or mechanical cardiopulmonary resuscitation (CPR). Mechanical CPR devices are expensive and prior clinical trials have not provided evidence of benefit for patients when compared with manual CPR. Objectives:To investigate the use of mechanical CPR in the prehospital setting and determine whether patient demographic characteristics or geographical location is associated with its use. Design, Setting, and Participants:A retrospective cross-sectional study was performed using the 2010 through 2016 National Emergency Medical Services Information System data. Participants included all patients identified by EMS professionals as having out-of-hospital cardiac arrest. Main Outcomes and Measures:Use of CPR, categorized as manual or mechanical. Results:From 2010 to 2016, 892?022 patients (38.6% female, 60.4% male, missing for 1%; mean [SD] age, 61.1 [20.5] years) with out-of-hospital cardiac arrest were identified by EMS professionals. Overall, manual CPR was used for 618?171 patients (69.3%) and mechanical CPR was used for 45?493 patients (5.1%). The risk-standardized rate of mechanical CPR use, accounting for patient demographic and geographical characteristics, rose from 1.9% in 2010 to 8.0% in 2016 (P?<?.001). In multivariable analyses, use of mechanical CPR devices was increasingly likely over time among patients identified with out-of-hospital cardiac arrest treated by EMS professionals, increasing from an adjusted odds ratio of 1.58 (95% CI, 1.42-1.77; P?<?.001) when comparing 2011 with 2010, to an adjusted odds ratio of 11.32 (95% CI, 10.22-12.54; P?<?.001) when comparing 2016 with 2010. In addition, several other patient demographic and geographical characteristics were associated with a higher likelihood of receiving mechanical CPR, including being 65 years or older, being male, being Hispanic, as well as receiving treatment in the Northeast Census Region, in a suburban location, or in a zip code with a median annual income greater than $20?000. Conclusions and Relevance:Mechanical CPR device use increased more than 4-fold among patients with out-of-hospital cardiac arrest treated by EMS professionals. Given the high costs of mechanical CPR devices, better evidence is needed to determine whether these devices improve clinically meaningful outcomes for patients treated for out-of-hospital cardiac arrest by prehospital EMS professionals to justify the significant increase in their use.

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

Automated mechanical cardiopulmonary resuscitation devices versus manual chest compressions in the treatment of cardiac arrest: protocol of a systematic review and meta-analysis comparing machine to human.

Introduction

Methods and analysis

Ethics and dissemination

Prospero registration number

Publications

Automated mechanical cardiopulmonary resuscitation devices versus manual chest compressions in the treatment of cardiac arrest: protocol of a systematic review and meta-analysis comparing machine to human.

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