See through ECG technology during cardiopulmonary resuscitation to analyze rhythm and predict defibrillation outcome

Affatato, Roberta; Li, Yongqin; Ristagno, Giuseppe

doi:10.1097/mcc.0000000000000297

Cited by 26 publications

(31 citation statements)

References 45 publications

(3 reference statements)

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“…We evaluated a total of 27 waveform measures which were grouped into four categories. 13,21 Individual time-domain measures quantify ECG characteristics such as amplitude and slope. Individual frequency measures include those calculated from Fourier or time-frequency transforms.…”

Section: Methodsmentioning

confidence: 99%

“…2 An improved strategy would use a waveform measure that achieves consistent prognostic performance throughout an arrest even during ongoing chest compressions. 13,14 However, a pilot investigation of two waveform measures during chest compressions has confirmed a significant reduction in prognostic performance compared to compression-free analysis, a finding that challenges integration of real-time waveform measures into best-practice uninterrupted CPR. 15 Whether this finding is generalizable to other individual waveform measures, or combinations of measures, is unknown.…”

Section: Introductionmentioning

confidence: 99%

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Ventricular Fibrillation Waveform Analysis During Chest Compressions to Predict Survival From Cardiac Arrest

Coult

Blackwood

Sherman

et al. 2019

Circ: Arrhythmia and Electrophysiology

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Background: Quantitative measures of the ventricular fibrillation (VF) electrocardiogram (ECG) waveform can assess myocardial physiology and predict cardiac arrest outcomes, making these measures a candidate to help guide resuscitation. Chest compressions are typically paused for waveform measure calculation, as compressions cause ECG artifact. However, such pauses contradict resuscitation guideline recommendations to minimize CPR interruptions. We evaluated a comprehensive group of VF measures with and without ongoing compressions to determine their performance under both conditions for predicting functionally-intact survival, the study’s primary outcome. Methods: Five-second VF ECG segments were collected with and without chest compressions prior to 2755 defibrillation shocks from 1151 out-of-hospital cardiac arrest patients. Twenty-four individual measures and three combination measures were implemented. Measures were optimized to predict functionally-intact survival (Cerebral Performance Category score ≤ 2) using 460 training cases, and their performance evaluated using 691 independent test cases. Results: Measures predicted functionally-intact survival on test data with an area under the receiver operating characteristic curve (AUC) ranging from 0.56-0.75 (median=0.73) without chest compressions and from 0.53-0.75 (median=0.69) with compressions (p<0.001 for difference). Of all measures evaluated, the support vector machine model ranked highest both without chest compressions (AUC=0.75, 95% CI 0.73-0.78) and with compressions (AUC=0.75, 95% CI 0.72-0.78) (p=0.75 for difference). Conclusions: VF waveform measures predict functionally-intact survival when calculated during chest compressions, but prognostic performance is generally reduced compared to compression-free analysis. However, support vector machine models exhibited similar performance with and without compressions while also achieving the highest AUC. Such machine learning models may therefore offer means to guide resuscitation during uninterrupted CPR.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Ventricular Fibrillation Waveform Analysis During Chest Compressions to Predict Survival From Cardiac Arrest

Coult

Blackwood

Sherman

et al. 2019

Circ: Arrhythmia and Electrophysiology

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“…supports the potential value of artifact correction where 4.7% of all recorded MV waveforms were manually removed from analysis during clinician review of waveform quality, including artifacts from endotracheal suctioning and ventilator disconnect 27 . Automated artifact correction has been studied elsewhere in critical care where correction of electrocardiogram chest compression artifact has been used to visualize the underlying cardiac rhythm to minimize breaks in chest compressions during cardiopulmonary resuscitation 40 . These efforts have led to at least one commercial product using this approach 41 , demonstrating the potential to translate such technologies to clinical application.…”

Section: Discussionmentioning

confidence: 99%

Development and Validation of a Multi-Algorithm Analytic Platform to Detect Off-Target Mechanical Ventilation

Adams

Lieng

Kühn

et al. 2017

Sci Rep

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Healthcare-specific analytic software is needed to process the large volumes of streaming physiologic waveform data increasingly available from life support devices such as mechanical ventilators. Detection of clinically relevant events from these data streams will advance understanding of critical illness, enable real-time clinical decision support, and improve both clinical outcomes and patient experience. We used mechanical ventilation waveform data (VWD) as a use case to address broader issues of data access and analysis including discrimination between true events and waveform artifacts. We developed an open source data acquisition platform to acquire VWD, and a modular, multi-algorithm analytic platform (ventMAP) to enable automated detection of off-target ventilation (OTV) delivery in critically-ill patients. We tested the hypothesis that use of artifact correction logic would improve the specificity of clinical event detection without compromising sensitivity. We showed that ventMAP could accurately detect harmful forms of OTV including excessive tidal volumes and common forms of patient-ventilator asynchrony, and that artifact correction significantly improved the specificity of event detection without decreasing sensitivity. Our multi-disciplinary approach has enabled automated analysis of high-volume streaming patient waveform data for clinical and translational research, and will advance the study and management of critically ill patients requiring mechanical ventilation.

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“…Auch Affatato et al [20] beschreiben die sich bis zur Schockabgabe wiederholende Rhythmusanalyse als potenzielle Hauptursache der Perischockpause. Eine Möglichkeit, dies zu vermeiden, stellt die fortlaufende Analyse des EKG während laufender Reanimation dar.…”

Section: Introductionunclassified

“…Obwohl die genannten Methoden eine Sensitivität von über 99 % aufweisen, ist die Spezifität mit unter 93 % nicht akzeptabel. Die Techniken, besonders zum erkennen nicht schockbarer Rhythmen, müssen noch deutlich verbessert werden, haben dann jedoch das Potenzial, die Kompressionspause vor dem Schock deutlich zu verkürzen [20].…”

Section: Introductionunclassified

Limitationen automatisierter externer Defibrillatoren im Notfalleinsatz: ein Erfahrungsbericht aus dem Kölner Straßenkarneval

Wanke

Odendahl²,

Herrmann

et al. 2017

Notarzt

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Zusammenfassung Fallbericht Ohne erkennbare Ursache kollabiert im Straßenkarneval ein 25-jähriger Mann und wird reanimationspflichtig. Reanimationsmaßahmen werden sofort durch den vor Ort befindlichen Sanitätsdienst eingeleitet, ein automatisierter externer Defibrillator (AED) wird genutzt und ein Schock ausgelöst. Im weiteren Verlauf folgen eine 71-minütige Reanimation mit insgesamt 12 Defibrillationen und anschließendem Wiedereinsetzen eines Spontankreislaufs (Return of spontaneous Circulation, ROSC). In der Klinik ergaben die weiteren Untersuchungen eine wiederkehrende Schmalkomplextachykardie im EKG. Aufgrund der optimalen Ersthelfermaßnahmen, der sofortigen notfallmedizinischen Versorgung und der kontinuierlichen Reanimationsmaßnahmen war der Verlauf sehr günstig. Der Patient wurde nach Implantation eines Kardioverter-Defibrillators (ICD) entlassen. Der Verdacht auf eine kongenitale Ionenkanalerkrankung als Ursache des Herz-Kreislauf-Stillstands konnte bislang durch Genanalysen noch nicht bestätigt werden. Folgerung Obwohl der Einsatz von AED im Sanitätsdienst sicher sinnvoll ist, zeigt der Fallbericht, dass das kompressionsfreie Intervall (No Flow Time) durch die Nutzung eines AED auch bei einem gut trainierten Team mit 26 Sekunden inakzeptabel hoch ist. Dies scheint jedoch zum größten Teil den technischen Einstellungen des Geräts und der suboptimalen Anpassung an die Umgebungsbedingungen geschuldet zu sein. Eine manuelle Defibrillation sollte außerdem immer der automatisierten vorgezogen werden.

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See through ECG technology during cardiopulmonary resuscitation to analyze rhythm and predict defibrillation outcome

Cited by 26 publications

References 45 publications

Ventricular Fibrillation Waveform Analysis During Chest Compressions to Predict Survival From Cardiac Arrest

Ventricular Fibrillation Waveform Analysis During Chest Compressions to Predict Survival From Cardiac Arrest

Development and Validation of a Multi-Algorithm Analytic Platform to Detect Off-Target Mechanical Ventilation

Limitationen automatisierter externer Defibrillatoren im Notfalleinsatz: ein Erfahrungsbericht aus dem Kölner Straßenkarneval

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