scholarly journals Computerized Analysis of the Ventricular Fibrillation Waveform Allows Identification of Myocardial Infarction: A Proof‐of‐Concept Study for Smart Defibrillator Applications in Cardiac Arrest

2020 ◽  
Vol 9 (19) ◽  
Author(s):  
Jos Thannhauser ◽  
Joris Nas ◽  
Dennis J. Rebergen ◽  
Sjoerd W. Westra ◽  
Joep L. R. M. Smeets ◽  
...  
Keyword(s):  
Myocardial Infarction ◽  
Cardiac Arrest ◽  
Ventricular Fibrillation ◽  
Waveform Analysis ◽  
Proof Of Concept ◽  
Concept Study ◽  
Computerized Analysis ◽  
C Statistic ◽  
Acute Mi ◽  
Model C

Background In cardiac arrest, computerized analysis of the ventricular fibrillation (VF) waveform provides prognostic information, while its diagnostic potential is subject of study. Animal studies suggest that VF morphology is affected by prior myocardial infarction (MI), and even more by acute MI. This experimental in‐human study reports on the discriminative value of VF waveform analysis to identify a prior MI. Outcomes may provide support for in‐field studies on acute MI. Methods and Results We conducted a prospective registry of implantable cardioverter defibrillator recipients with defibrillation testing (2010–2014). From 12‐lead surface ECG VF recordings, we calculated 10 VF waveform characteristics. First, we studied detection of prior MI with lead II, using one key VF characteristic (amplitude spectrum area [AMSA]). Subsequently, we constructed diagnostic machine learning models: model A, lead II, all VF characteristics; model B, 12‐lead, AMSA only; and model C, 12‐lead, all VF characteristics. Prior MI was present in 58% (119/206) of patients. The approach using the AMSA of lead II demonstrated a C‐statistic of 0.61 (95% CI, 0.54–0.68). Model A performance was not significantly better: 0.66 (95% CI, 0.59–0.73), P =0.09 versus AMSA lead II. Model B yielded a higher C‐statistic: 0.75 (95% CI, 0.68–0.81), P <0.001 versus AMSA lead II. Model C did not improve this further: 0.74 (95% CI, 0.67–0.80), P =0.66 versus model B. Conclusions This proof‐of‐concept study provides the first in‐human evidence that MI detection seems feasible using VF waveform analysis. Information from multiple ECG leads rather than from multiple VF characteristics may improve diagnostic accuracy. These results require additional experimental studies and may serve as pilot data for in‐field smart defibrillator studies, to try and identify acute MI in the earliest stages of cardiac arrest.

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

2019 ◽  
Vol 12 (1) ◽  
Author(s):  
Jason Coult ◽  
Jennifer Blackwood ◽  
Lawrence Sherman ◽  
Thomas D. Rea ◽  
Peter J. Kudenchuk ◽  
...  
Keyword(s):  
Cardiac Arrest ◽  
Ventricular Fibrillation ◽  
Waveform Analysis ◽  
Chest Compressions

The Modified Glasgow Outcome Score for the prediction of outcome in patients after cardiac arrest: a prospective clinical proof of concept study

2012 ◽  
Vol 101 (7) ◽  
pp. 533-543 ◽  
Author(s):  
Obaida R. Rana ◽  
Jörg W. Schröder ◽  
Julia S. Kühnen ◽  
Esra Saygili ◽  
Christopher Gemein ◽  
...  
Keyword(s):  
Cardiac Arrest ◽  
Glasgow Outcome Score ◽  
Proof Of Concept ◽  
Prediction Of Outcome ◽  
Concept Study ◽  
Outcome Score

scholarly journals Ventricular Fibrillation Cardiac Arrest in Young Female from Diffuse Left Anterior Descending Coronary Vasospasm

2019 ◽  
Vol 3 (4) ◽  
pp. 395-397
Author(s):  
Christopher Wilson ◽  
Eric Melnychuk ◽  
John Bernett
Keyword(s):  
Myocardial Infarction ◽  
Emergency Department ◽  
Cardiac Arrest ◽  
Chest Pain ◽  
Coronary Artery ◽  
Ventricular Fibrillation ◽  
Young Female ◽  
Coronary Vasospasm ◽  
Fatal Complication ◽  
St Elevation

This is a case of the most severe and potentially fatal complication of coronary artery vasospasm. We report a case of a 40-year-old female presenting to the emergency department (ED) via emergency medical services with chest pain. The patient experienced a ventricular fibrillation cardiac arrest while in the ED. Post-defibrillation electrocardiogram showed changes suggestive of an ST-elevation myocardial infarction (STEMI). Cardiac catheterization showed severe left anterior descending spasm with no evidence of disease. Coronary vasospasm is a consideration in the differential causes of ventricular fibrillation and STEMI seen in the ED.

scholarly journals Non-Invasive Venous waveform Analysis (NIVA) for volume assessment during complex cranial vault reconstruction: A proof-of-concept study in children

PLoS ONE ◽  
2020 ◽  
Vol 15 (7) ◽  
pp. e0235933 ◽  
Author(s):  
Jenna H. Sobey ◽  
Srijaya K. Reddy ◽  
Kyle M. Hocking ◽  
Monica E. Polcz ◽  
Christy M. Guth ◽  
...  
Keyword(s):  
Cranial Vault ◽  
Waveform Analysis ◽  
Proof Of Concept ◽  
Concept Study ◽  
Non Invasive ◽  
Volume Assessment

scholarly journals Fuzzy and Sample Entropies as Predictors of Patient Survival Using Short Ventricular Fibrillation Recordings during out of Hospital Cardiac Arrest

Entropy ◽  
2018 ◽  
Vol 20 (8) ◽  
pp. 591 ◽  
Author(s):  
Beatriz Chicote ◽  
Unai Irusta ◽  
Elisabete Aramendi ◽  
Raúl Alcaraz ◽  
José Rieta ◽  
...  
Keyword(s):  
Cardiac Arrest ◽  
Ventricular Fibrillation ◽  
Cardiopulmonary Resuscitation ◽  
Prediction Accuracy ◽  
Patient Survival ◽  
Myocardial Damage ◽  
Sample Entropy ◽  
Waveform Analysis ◽  
Embedding Dimension ◽  
Hospital Cardiac Arrest

Optimal defibrillation timing guided by ventricular fibrillation (VF) waveform analysis would contribute to improved survival of out-of-hospital cardiac arrest (OHCA) patients by minimizing myocardial damage caused by futile defibrillation shocks and minimizing interruptions to cardiopulmonary resuscitation. Recently, fuzzy entropy (FuzzyEn) tailored to jointly measure VF amplitude and regularity has been shown to be an efficient defibrillation success predictor. In this study, 734 shocks from 296 OHCA patients (50 survivors) were analyzed, and the embedding dimension (m) and matching tolerance (r) for FuzzyEn and sample entropy (SampEn) were adjusted to predict defibrillation success and patient survival. Entropies were significantly larger in successful shocks and in survivors, and when compared to the available methods, FuzzyEn presented the best prediction results, marginally outperforming SampEn. The sensitivity and specificity of FuzzyEn were 83.3% and 76.7% when predicting defibrillation success, and 83.7% and 73.5% for patient survival. Sensitivities and specificities were two points above those of the best available methods, and the prediction accuracy was kept even for VF intervals as short as 2s. These results suggest that FuzzyEn and SampEn may be promising tools for optimizing the defibrillation time and predicting patient survival in OHCA patients presenting VF.

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