Conflict of Interest Declaration
The ILCOR Continuous Evidence Evaluation process is guided by a rigorous ILCOR Conflict of Interest policy. The following Task Force members and other authors were recused from the discussion as they declared a conflict of interest: (Kuzovlev none, Olasveengen has received research funding from Zoll Foundation and Laerdal Foundation)
The following Task Force members and other authors declared an intellectual conflict of interest and this was acknowledged and managed by the Task Force Chairs and Conflict of Interest committees: (none applicable)
CoSTR Citation
Kuzovlev A, Mancini MB, Avis S, Brooks S, Castren M, Chung S, Considine J, Kudenchuk P, Perkins G, Ristagno G, Semeraro F, Smith C, Smyth M , Morley PT, Olasveengen TM -on behalf of the International Liaison Committee on Resuscitation Basic Life Support Task Force.
Analysis of rhythm during chest compression during Cardiac Arrest in Adults Consensus on Science with Treatment Recommendations [Internet] Brussels, Belgium: International Liaison Committee on Resuscitation (ILCOR) Basic Life Support Task Force, 2019 Dec 17. Available from: http://ilcor.org
Methodological Preamble
The continuous evidence evaluation process for the production of Consensus on Science with Treatment Recommendations (CoSTR) started with a systematic review of basic life support conducted by Artem Kuzovlev and Theresa M. Olasveengen with involvement of clinical content experts. Evidence for adult literature was sought and considered by the Basic Life Support Adult Task Force. These data were taken into account when formulating the Treatment Recommendations.
PICOST
The PICOST (Population, Intervention, Comparator, Outcome, Study Designs and Timeframe)
Population: Adults in any setting (in-hospital or out-of-hospital) with cardiac arrest
Intervention: Analysis of cardiac rhythm during chest compressions
Comparators: Standard care (analysis of cardiac rhythm during pauses in chest compressions).
Outcomes: Survival to hospital discharge with good neurological outcome and survival to hospital discharge were ranked as critical outcomes. Return of spontaneous circulation (ROSC) was ranked as an important outcome. CPR quality metrics such time chest compression fraction, pauses in compressions, compressions per minute, time to commencing CPR, or time to first shock etc. were included as important outcomes.
Study Designs: Randomized controlled trials (RCTs) and non-randomized studies (non-randomized controlled trials, interrupted time series, controlled before-and-after studies, cohort studies) are eligible for inclusion. Unpublished studies (e.g., conference abstracts, trial protocols) are excluded.
It is anticipated that there will be insufficient studies from which to draw a conclusion; case series will be included in the initial search and included as long as they contain ≥ 5 cases.
Timeframe: All years and all languages were included as long as there was an English abstract; unpublished studies (e.g., conference abstracts, trial protocols) were excluded. Literature search updated to Sept 23, 2019.
Consensus on Science
There are currently no human studies that address the identified critical outcomes criteria of favorable neurologic outcome, survival, or ROSC or the important outcomes criteria of CPR quality, time to commencing CPR, or time to first shock.
Fourteen full-text papers were identified and reviewed (Li 2007 131, Tan 2008 S409, Werther 2009 1301, Li 2012 78, Aramendi 2012 692, Babaeizadeh 2014 798, Gong, 2014 140438, Partridge 2015 133, Zhang 2016 67, Rad 2016 44, Gong 2017 471, Zhang 2017 111, Fumagalli 2018 248, Hu 2019 1), and while they did not evaluate the effect of artifact-filtering algorithms for analysis of electrocardiographic rhythm during CPR on any of our critical or important outcomes, they provided insights into the feasibility and potential benefits of this technology. Most of these studies use previously collected ECG, electrical impedance and/or accelerometer signals from cardiac arrests cases to evaluate the ability of various algorithms (Li 2007 131, Tan 2008 S409, Werther 2009 1301, Li 2012 78, Aramendi 2012 692, Babaeizadeh 2014 798, Zhang 2016 67, Gong 2017 471, Fumagalli 2018 248, Hu 2019 1) or machine learning (Rad 2016 44) to detect shockable rhythms during chest compressions. There are also studies evaluating artifact-filtering algorithms in animal models (Gong, 2014 140438, Zhang 2017 111) and simulation studies (Partridge 2015 133). Sensitivities and specificities are generally reported in the 90-99% range, but none of these studies have evaluated their use in real-time clinical settings.
Treatment Recommendations
We suggest against the routine use of artifact-filtering algorithms for analysis of electrocardiographic rhythm during CPR (weak recommendation, very low certainty of evidence).
We suggest the usefulness of artifact-filtering algorithms for analysis of electrocardiographic rhythm during CPR be assessed in clinical trials or research initiatives (weak recommendation, very low certainty of evidence).
Justification and Evidence to Decision Framework Highlights
In making a recommendation against routine use, we placed priority on avoiding the costs of introducing a new technology where the effectiveness or harm on patient outcomes remains to be determined.
In making a recommendation for further research; the task force is acknowledging a) there is thus far insufficient evidence to support a decision for or against routine use, b) further research has potential for reducing uncertainty about the effects and c) further research is thought to be of good value for the anticipated costs.
The task force also acknowledges that some EMS systems may already have implemented artifact-filtering algorithms for analysis of electrocardiographic rhythm during CPR, and as such wish to strongly encourage such systems to report on their experiences to build the evidence base regarding the use of these technologies in clinical practice.
Knowledge Gaps
There were no studies identified that evaluated feasibility, efficacy or effectiveness of artifact-filtering algorithms for analysis of electrocardiographic rhythm during CPR in any setting for any patient population.
Attachments
Evidence-to Decision Table: Analysis of rhythm during chest compression
References
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Babaeizadeh S, Firoozabadi R, Han C, Helfenbein ED. Analyzing cardiac rhythm in the presence of chest compression artifact for automated shock advisory. J Electrocardiol. 2014;47(6):798-803.
Fumagalli F, Silver AE, Tan Q, Zaidi N, Ristagno G.Cardiac rhythm analysis during ongoing cardiopulmonary resuscitation using the Analysis During Compressions with Fast Reconfirmation technology. Heart Rhythm. 2018 Feb;15(2):248-255.
Gong Y, Yu T, Chen B, He M, Li Y. Removal of cardiopulmonary resuscitation artifacts with an enhanced adaptive filtering method: an experimental trial. Biomed Res Int. 2014;2014:140438.
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Zhang G, Wu T, Wan Z et al. A new method to detect ventricular fibrillation from CPRartifact-corrupted ECG based on the ECG alone. Biomedical Signal Processing and Control. 2016;67–75.
Zhang G, Wu T, Wan Z, Song Z, Yu M, Wang D, Li L, Chen F, Xu X. A method to differentiate between ventricular fibrillation and asystole during chest compressions using artifact-corrupted ECG alone. Comput Methods Programs Biomed. 2017;141:111-117.