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Training approaches for the deployment of a mechanical chest compression device: a randomised controlled manikin study.

ABSTRACT: OBJECTIVES:To evaluate the effect of training strategy on team deployment of a mechanical chest compression device. DESIGN:Randomised controlled manikin trial. SETTING:Large teaching hospital in the UK. PARTICIPANTS:Twenty teams, each comprising three clinicians. Participating individuals were health professionals with intermediate or advanced resuscitation training. INTERVENTIONS:Teams were randomised in a 1:1 ratio to receive either standard mechanical chest compression device training or pit-crew device training. Training interventions lasted up to 1?h. Performance was measured immediately after training in a standardised simulated cardiac arrest scenario in which teams were required to deploy a mechanical chest compression device. PRIMARY AND SECONDARY OUTCOME MEASURES:Primary outcome was chest compression flow fraction in the minute preceding the first mechanical chest compression. Secondary outcomes included cardiopulmonary resuscitation quality and mechanical device deployment metrics, and non-technical skill performance. Outcomes were assessed using video recordings of the test scenario. RESULTS:In relation to the primary outcome of chest compression flow fraction in the minute preceding the first mechanical chest compression, we found that pit-crew training was not superior to standard training (0.76 (95% CI 0.73 to 0.79)?vs 0.77 (95% CI 0.73 to 0.82), mean difference -0.01 (95% CI -0.06 to 0.03), P=0.572). There was also no difference between groups in performance in relation to any secondary outcome. CONCLUSIONS:Pit-crew training, compared with standard training, did not improve team deployment of a mechanical chest device in a simulated cardiac arrest scenario. TRIAL REGISTRATION NUMBER:ISRCTN43049287; Pre-results.

SUBMITTER: Couper K

PROVIDER: S-EPMC5879816 | biostudies-literature | 2018 Feb

REPOSITORIES: biostudies-literature

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Training approaches for the deployment of a mechanical chest compression device: a randomised controlled manikin study.

Couper Keith K Velho Rochelle M RM Quinn Tom T Devrell Anne A Lall Ranjit R Orriss Barry B Yeung Joyce J Perkins Gavin D GD

BMJ open 20180201 2

<h4>Objectives</h4>To evaluate the effect of training strategy on team deployment of a mechanical chest compression device.<h4>Design</h4>Randomised controlled manikin trial.<h4>Setting</h4>Large teaching hospital in the UK.<h4>Participants</h4>Twenty teams, each comprising three clinicians. Participating individuals were health professionals with intermediate or advanced resuscitation training.<h4>Interventions</h4>Teams were randomised in a 1:1 ratio to receive either standard mechanical chest ...[more]

PMID: 29391379

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Project description:New 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.This 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.The 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 ? = 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 ? = 0.84 (95% CI = 0.59 to 0.95).In 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:ObjectivesThere is considerable interest in reducing the cost of clinical trials. Linkage of trial data to administrative datasets and disease-specific registries may improve trial efficiency, but it has not been reported in resuscitation trials conducted in the UK. To assess the feasibility of using national administrative and clinical datasets to follow up patients transported to hospital following attempted resuscitation in a cluster randomised trial of a mechanical chest compression device in out-of-hospital cardiac arrest.MethodsHospital data on trial participants were requested from Hospital Episode Statistics (HES), the Intensive Care National Audit and Research Centre, and Myocardial Ischaemia National Audit Project and National Audit of Percutaneous Coronary Interventions, using unique patient identifiers. Linked data were received between June 2014 and June 2015.ResultsOf 4471 patients randomised in the pre-hospital randomised assessment of a mechanical compression device in cardiac arrest (PARAMEDIC) trial, 2398 (53.6%) were not known to be deceased at emergency department arrival and were eligible for linkage. We achieved an overall match rate of 86.7% in the combined HES accident and emergency, inpatient and critical care dataset, with variable match rates (4.2%-80.4%) in individual datasets. Patient demographics, cardiac arrest-related characteristics and major outcomes were predominantly similar between HES matched and unmatched groups, in the linkage apart from location, response time and return of spontaneous circulation (ROSC) at handover.ConclusionsThis study shows that it is feasible to track patients from the prehospital setting through to hospital admission using routinely available administrative datasets with a moderate to high degree of success. This approach has the potential to complement the trial data with the demographic and clinical management information about the studied cohort, as well as to improve the efficiency and reduce the costs of follow-up in cardiac arrest trials.Clinical trial registrationISRCTN08233942; Post-results.

Project description:BackgroundCardiac arrest (CA) is a serious threat to human health. Cardiopulmonary resuscitation (CPR) is an effective treatment for CA. Early and high-quality CPR is closely related to the survival rate of patients with CA. But manual chest compression has a lot of defects. To solve the defects and improve the quality of CPR, mechanical CPR device was invented. However, it has still controversy whether manual chest compression or mechanical chest compression is better. This systematic review was aimed to investigate the difference in clinical outcomes between manual chest compression and Lund University Cardiac Assist System (LUCAS) assisted CPR in patients with out-hospital CA.MethodsOriginal research studies, conducted on adult out-of-hospital CA, were included. PubMed/Medline, EMBASE, Scopus, Cochrane Library, CNKI, and Wanfang database were searched from the setting to February 21, 2019. Odds ratio (OR) with 95% confidence interval (CI) was selected as effect scale index for evaluation of the difference in return of spontaneous circulation (ROSC), survival to hospital admission, survival to hospital discharge, and survival to 30 days. Random effects model was used in this study to estimate overall mean effects.ResultsA total of 6 articles, including 4 randomized controlled trials and 2 nonrandomized controlled trials, were selected. And 8501 subjects were involved to analyze the clinical outcomes of LUCAS and manual chest compression for patients with out-hospital CA. Comparisons of ROSC (33.3% vs 33.0%, P?=?.98; OR?=?1; 95% CI: [0.89,1.13]), survival to hospital admission (22.7% vs 24.3%, P?=?.32; OR?=?0.86; 95% CI: [0.65,1.15]), survival to hospital discharge (8.6% vs 10.7%, P?=?.50; OR?=?0.92; 95% CI: [0.73,1.17]), and survival to 30 days (7.5% vs 8.5%, P?=?.50; OR?=?0.92; 95% CI: [0.73,1.17]) were made. No significant difference was found.ConclusionThe synthesis of available evidence does not support that mechanical chest compression with LUCAS device improves clinical outcome in out-of-hospital CA patients compared with manual chest compression. Large scale studies with improved designs are still needed in the future.

Project description:BackgroundThe use of mechanical chest compression devices on patients in cardiac arrest has not shown benefits in previous trials. This is surprising, given that these devices can deliver consistently high-quality chest compressions without interruption. It is possible that this discrepancy is due to the no-flow time (NFT) during the application of the device. In this study, we aimed to demonstrate a reduction in no-flow time during cardiopulmonary resuscitation (CPR) with mechanical chest compression devices following 10 min of structured training in novices.Methods270 medical students were recruited for the study. The participants were divided as a convenience sample into two groups. Both groups were instructed in how to use the device according to the manufacturer's specifications. The control group trained in teams of three, according to their own needs, to familiarise themselves with the device. The intervention group received 10 min of structured team training, also in teams of three. The participants then had to go through a CPR scenario in an ad-hoc team of three, in order to evaluate the training effect.ResultsThe median NFT was 26.0 s (IQR: 20.0-30.0) in the intervention group and 37.0 s (IQR: 29.0-42.0) in the control group (p < 0.001). In a follow-up examination of the intervention group four months after the training, the NFT was 34.5 s (IQR: 24.0-45.8). This represented a significant deterioration (p = 0.015) and was at the same level as the control group immediately after training (p = 0.650). The position of the compression stamp did not differ significantly between the groups. Groups that lifted the manikin to position the backboard achieved an NFT of 35.0 s (IQR: 27.5-42.0), compared to 41.0 s (IQR: 36.5-50.5) for the groups that turned the manikin to the side (p = 0.074).ConclusionsThis simulation-based study demonstrated that structured training can significantly reduce the no-flow time when using mechanical resuscitation devices, even in ad-hoc teams. However, this benefit seems to be short-lived: after four months no effect could be detected.

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Training approaches for the deployment of a mechanical chest compression device: a randomised controlled manikin study.

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Training approaches for the deployment of a mechanical chest compression device: a randomised controlled manikin study.

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