Two minutes CPR versus five cycles CPR prior to reanalysis of the cardiac rhythm: A prospective, randomized simulator-based trial

Weichert, Veronika; Sellmann, Timur; Wetzchewald, Dietmar; Gasch, B.; Hunziker, Sabina; Marsch, Stephan

doi:10.1016/j.resuscitation.2015.07.023

Cited by 7 publications

(9 citation statements)

References 22 publications

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Section: Discussionsupporting

confidence: 89%

“…As far as secondary outcomes are concerned, our results confirm substantial variance in adherence to CPR algorithms and parts thereof previously described [16,17,22,27,28]. In keeping with the results of a previous trial [28] our results confirm that temporal targets (2 min) are more difficult to follow than numerical targets (200 chest compressions). Surprisingly, only 75% of teams adhered to an unambiguous timing of airway management (no ventilation at all in the first three cycles and start of ventilation in the fourth cycle in "CCR"; immediate advanced airway management in "Arnsberg").…”

Section: Discussionsupporting

confidence: 89%

“…Current guidelines recommend a changeover or rotation of the person performing chest compressions about every two minutes corresponding approximately to the length of a compression cycle [27,28]. If followed, this would result in three or more changeovers during the studied time interval.…”

Section: Secondary Outcomesmentioning

confidence: 99%

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Hands-On Times, Adherence to Recommendations and Variance in Execution among Three Different CPR Algorithms: A Prospective Randomized Single-Blind Simulator-Based Trial

Rifai

Sellmann

Wetzchewald

et al. 2020

IJERPH

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Background: Alternative cardiopulmonary resuscitation (CPR) algorithms, introduced to improve outcomes after cardiac arrest, have so far not been compared in randomized trials with established CPR guidelines. Methods: 286 physician teams were confronted with simulated cardiac arrests and randomly allocated to one of three versions of a CPR algorithm: (1) current International Liaison Committee on Resuscitation (ILCOR) guidelines (“ILCOR”), (2) the cardiocerebral resuscitation (“CCR”) protocol (3 cycles of 200 uninterrupted chest compressions with no ventilation), or (3) a local interpretation of the current guidelines (“Arnsberg“, immediate insertion of a supraglottic airway and cycles of 200 uninterrupted chest compressions). The primary endpoint was percentage of hands-on time. Results: Median percentage of hands-on time was 88 (interquartile range (IQR) 6) in “ILCOR” teams, 90 (IQR 5) in “CCR” teams (p = 0.001 vs. “ILCOR”), and 89 (IQR 4) in “Arnsberg” teams (p = 0.032 vs. “ILCOR”; p = 0.10 vs. “CCR”). “ILCOR” teams delivered fewer chest compressions and deviated more from allocated targets than “CCR” and “Arnsberg” teams. “CCR” teams demonstrated the least within-team and between-team variance. Conclusions: Compared to current ILCOR guidelines, two alternative CPR algorithms advocating cycles of uninterrupted chest compressions resulted in very similar hands-on times, fewer deviations from targets, and less within-team and between-team variance in execution.

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Section: Discussionsupporting

confidence: 89%

Section: Discussionsupporting

confidence: 89%

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Hands-On Times, Adherence to Recommendations and Variance in Execution among Three Different CPR Algorithms: A Prospective Randomized Single-Blind Simulator-Based Trial

Rifai

Sellmann

Wetzchewald

et al. 2020

IJERPH

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“…Lack of sensitivity to temporal constraints was the primary behavior associated with rules with clear multiminute time targets. That finding is consistent with prior work indicating that teams under stress have difficulty perceiving the passage of time relative to time-based targets (Weichert, et al, 2015).…”

Section: Behavioral Analysissupporting

confidence: 91%

A Strategic Approach to Determining the Relative Impact of Team Behavior on Guideline Violations

Ashby

Smith

Gaines

et al. 2018

Proceedings of the International Symposium on Human Factors and

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Excellence in teamwork is an essential part of achieving high levels of performance in the delivery of healthcare and in the promotion of patient safety across a range of disciplines. There have been numerous efforts to promote adherence to guidelines and evidence-best practice. Advanced cardiac life support (ACLS) refers to a set of clinical interventions for several life-threatening conditions. Adherence to ACLS guidelines maximizes the probability of successful resuscitation and minimizes the long-term consequences for the patient. However, adherences rates to guidelines are known to be low despite quality guidelines and significant investment in training. Part of the problem has to do with the fact that most ACLS teams spontaneously assemble based on the availability of personnel at a given time on a hospitable floor, thus presenting formidable challenges for training as a team. The objective of this study was to produce a taxonomy of resuscitation team behaviors that impact adherence to the ACLS guidelines. Towards that end, the guidelines were translated into sets of rules to develop a means for measuring adherence and to investigate the role of team behaviors in guideline violations. The mean adherence to rules was highly variable, but comparatively low. We describe problems associated with adherence to CPR rules, ventilation and medication administration. Specifically, rule violations could be explained by behaviors such as misplaced attention, lack of sensitivity to temporal constraints, lapses in communication and failed execution of a subtask. Some of these behaviors are arguably the product of individual skills and others reflect a lack of cohesiveness, leadership and situation awareness in the spontaneously assembled teams. The specific implications for training are explored.

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“…The quality of CPR decreases as time passes, which ultimately affects its success rate (29). So, data from the first 2 minutes is more effective and widely used in evaluating the quality of CPR (30). In an actual rescue of drowning or asphyxiating cardiac arrest patients, five cycles (2 minutes) of CPR should be administered before initiating the emergency medical service system.…”

Section: Signal Datamentioning

confidence: 99%

Automatic identification of compressions and ventilations during CPR based on the fuzzy c-means clustering and deep belief network

Zhang

Wei

et al. 2020

Ann Transl Med

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Background: A transthoracic impedance (TTI) signal is an important indicator of the quality of chest compressions (CCs) during cardiopulmonary resuscitation (CPR). We proposed an automatic detection algorithm including the wavelet decomposition, fuzzy c-means (FCM) clustering, and deep belief network (DBN) to identify the compression and ventilation waveforms for evaluating the quality of CPR.Methods: TTI signals were collected from a cardiac arrest model that electrically induced cardiac arrest in pigs. All signals were denoised using the wavelet and morphology method. The potential compression and ventilation waveforms were marked using an algorithm with a multi-resolution window. The compressions and ventilations in these waveforms were identified and classified using the FCM clustering and DBN methods.Results: Using the FCM clustering method, the positive predictive values (PPVs) for compressions and ventilations were 99.7% and 95.7%, respectively. The sensitivities of recognition were 99.8% for compressions and 95.1% for ventilations. The DBN approach exhibited similar PPV and sensitivity results to the FCM clustering method. The time cost was satisfactory using either of these techniques.Conclusions: Our findings suggest that FCM clustering and DBN can be utilized to effectively and accurately evaluate CPR quality, and provide information for improving the success rate of CPR. Our realtime algorithms using FCM clustering and DBN eliminated most distortions and noises effectively, and correctly identified the compression and ventilation waveforms with a low time cost.

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Two minutes CPR versus five cycles CPR prior to reanalysis of the cardiac rhythm: A prospective, randomized simulator-based trial

Cited by 7 publications

References 22 publications

Hands-On Times, Adherence to Recommendations and Variance in Execution among Three Different CPR Algorithms: A Prospective Randomized Single-Blind Simulator-Based Trial

Hands-On Times, Adherence to Recommendations and Variance in Execution among Three Different CPR Algorithms: A Prospective Randomized Single-Blind Simulator-Based Trial

A Strategic Approach to Determining the Relative Impact of Team Behavior on Guideline Violations

Automatic identification of compressions and ventilations during CPR based on the fuzzy c-means clustering and deep belief network

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