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Yoshikawa Y, Ogino Y, Okai T, Oya H, Hoshi Y, Nakano K. Prediction of the effect of electrical defibrillation by using spectral feature parameters. Comput Biol Med 2024; 182:109123. [PMID: 39244961 DOI: 10.1016/j.compbiomed.2024.109123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Revised: 08/13/2024] [Accepted: 09/04/2024] [Indexed: 09/10/2024]
Abstract
This paper proposes a system for predicting the effect of electrical defibrillation using spectral feature parameters. The proposed method consists of two-stage prediction. The first stage involves predicting whether electrical defibrillation is "Successful" or "Ineffective." As the next stage, if the proposed prediction system determines "Ineffective," the proposed system discriminates between "VF recurrence" or "Failure" for electrical defibrillation. To develop the prediction system, feature parameters for the target electrocardiograms (ECGs) were first extracted by using the wavelet transform and spectral analysis. Next, effective feature parameters for prediction are selected through an analysis of variance. Moreover, in the preprocessing phase, the Synthetic Minority Oversampling Technique method and standardization are introduced. Finally, support vector machines with some kernel functions and the regularization method are utilized to predict the three states, i.e., "Successful," "Failure," and "VF recurrence," for electrical defibrillation in two phases. In this paper, we present our analysis method for ECGs and evaluate the effectiveness of the proposed prediction system.
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Affiliation(s)
- Y Yoshikawa
- Tokyo City University, 1-28-1 Tamazutsumi, Setagaya-ku, 158-8557, Tokyo, Japan.
| | - Y Ogino
- The University of Electro-Communications, 1-5-1 Chofugaoka, Chofu, 182-8585, Tokyo, Japan
| | - T Okai
- Tokyo City University, 1-28-1 Tamazutsumi, Setagaya-ku, 158-8557, Tokyo, Japan
| | - H Oya
- Tokyo City University, 1-28-1 Tamazutsumi, Setagaya-ku, 158-8557, Tokyo, Japan
| | - Y Hoshi
- Tokyo City University, 1-28-1 Tamazutsumi, Setagaya-ku, 158-8557, Tokyo, Japan
| | - K Nakano
- The University of Electro-Communications, 1-5-1 Chofugaoka, Chofu, 182-8585, Tokyo, Japan
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2
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Liu Y, Zhou T, Yang Q, Lu Y, Yang Z, Jiang J. An acoustic method (Spectral Flux) to analyze ECG signals for optimizing timing for defibrillation in a porcine model of ventricular fibrillation. Resusc Plus 2024; 17:100572. [PMID: 38370316 PMCID: PMC10869897 DOI: 10.1016/j.resplu.2024.100572] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Revised: 01/25/2024] [Accepted: 01/29/2024] [Indexed: 02/20/2024] Open
Abstract
Aim Spectral Flux (SF), which is based on common algorithms in the audio processing field, was applied to quantitatively analyze ECG signals to optimize the timing of defibrillation. With the aim of proving the performance in optimizing the timing of defibrillation, SF was compared with Amplitude Spectrum Area (AMSA) in a porcine model of ventricular fibrillation (VF) in a retrospective analysis experiment. Methods A total of 56 male domestic pigs, weighing 40 ± 5 kg, were induced to undergo VF. Animals were then left untreated for 10 min, and after 6 min of cardiopulmonary resuscitation (CPR) defibrillation was performed. The respective SF and AMSA values were calculated every minute during VF and CPR. Comparisons were made through receiver operating characteristic (ROC) curves, one-way analyses of variance (one-way ANOVA), and scatterplots for the successful initial defibrillation sample (positive samples, Group R) and the failed initial defibrillation sample (negative samples, Group N) to illustrate the performance in optimizing the timing of defibrillation for the AMSA and SF methods. Result Values of SF and AMSA gradually decreased during the 10 min VF period and increased in during the 6 min CPR period. The scatterplots showed that both metrics had the ability to distinguish positive and negative samples (p < .001). Meanwhile, ROC curves showed that SF (area under the curve, AUC = 0.798, p < .001) had the same ability as AMSA (AUC = 0.737, p < .001) to predict the successful defibrillation (Z = 1.35, p = 0.177). Moreover, when comparing the values for AMSA and SF between the successful initial defibrillation samples (Group R) and the failed initial defibrillation samples (Group N), the results showed that the values of both AMSA and SF in Group R were significantly higher than those in Group N (p < .001). Conclusion In the present study, SF method had the same ability as AMSA to predict successful defibrillation with significantly higher values in cases of successful defibrillation than the instances in which defibrillation failed. Additionally, SF method might be more stable than AMSA for filtering out the higher frequency interference signals due to the narrower frequency range and had higher specificity and predictive accuracy than AMSA. So SF method had high clinical potential to optimize the timing of defibrillation. Nevertheless, further animal and clinical studies are still needed to confirm the effectiveness and practicality of SF as a predictive module for defibrillators in clinical practice.
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Affiliation(s)
- Yuanshan Liu
- Department of Emergency, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Tianen Zhou
- Department of Emergency, the First People’s Hospital of Foshan, Foshan, China
| | - Qiyu Yang
- School of Automation, Guangdong University of Technology, Guangzhou, China
| | - Yujing Lu
- School of Automation, Guangdong University of Technology, Guangzhou, China
| | - Zhengfei Yang
- Department of Emergency, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Jun Jiang
- Department of Emergency, the First People’s Hospital of Foshan, Foshan, China
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Jaureguibeitia X, Coult J, Sashidhar D, Blackwood J, Kutz JN, Kudenchuk PJ, Rea TD, Kwok H. Instantaneous amplitude: Association of ventricular fibrillation waveform measures at time of shock with outcome in out-of-hospital cardiac arrest. J Electrocardiol 2023; 80:11-16. [PMID: 37086596 DOI: 10.1016/j.jelectrocard.2023.04.002] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Revised: 03/31/2023] [Accepted: 04/04/2023] [Indexed: 04/24/2023]
Abstract
BACKGROUND Prompt defibrillation is key to successful resuscitation from ventricular fibrillation out-of-hospital cardiac arrest (VF-OHCA). Preliminary evidence suggests that the timing of shock relative to the amplitude of the VF ECG waveform may affect the likelihood of resuscitation. We investigated whether the VF waveform amplitude at the time of shock (instantaneous amplitude) predicts outcome independent of other validated waveform measures. METHODS We conducted a retrospective study of VF-OHCA patients ≥18 old. We evaluated three VF waveform measures for each shock: instantaneous amplitude at the time of shock, and maximum amplitude and amplitude spectrum area (AMSA) over a 3-s window preceding the shock. Linear mixed-effects modeling was used to determine whether instantaneous amplitude was associated with shock-specific return of organized rhythm (ROR) or return of spontaneous circulation (ROSC) independent of maximum amplitude or AMSA. RESULTS The 566 eligible patients received 1513 shocks, resulting in ROR of 62.0% (938/1513) and ROSC of 22.3% (337/1513). In unadjusted regression, an interquartile increase in instantaneous amplitude was associated with ROR (Odds ratio [OR] [95% confidence interval] = 1.27 [1.11-1.45]) and ROSC (OR = 1.27 [1.14-1.42]). However, instantaneous amplitude was not associated with ROR (OR = 1.13 [0.97-1.30]) after accounting for maximum amplitude, nor with ROR (OR = 1.00 [0.87-1.15]) or ROSC (OR = 1.05 [0.93-1.18]) after accounting for AMSA. By contrast, AMSA and maximum amplitude remained independently associated with ROR and ROSC. CONCLUSIONS We did not observe an independent association between instantaneous amplitude and shock-specific outcomes. Efforts to time shock to the maximal amplitude of the VF waveform are unlikely to affect resuscitation outcome.
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Affiliation(s)
- Xabier Jaureguibeitia
- Department of Communications Engineering, University of the Basque Country, Bilbao, Spain.
| | - Jason Coult
- Department of Medicine, University of Washington, Seattle, WA, USA
| | - Diya Sashidhar
- Department of Applied Mathematics, University of Washington, Seattle, WA, USA
| | - Jennifer Blackwood
- Division of Emergency Medical Services, Public Health Seattle & King County, Seattle, WA, USA
| | - J Nathan Kutz
- Department of Applied Mathematics, University of Washington, Seattle, WA, USA
| | - Peter J Kudenchuk
- Division of Emergency Medical Services, Public Health Seattle & King County, Seattle, WA, USA; Department of Medicine, Division of Cardiology, University of Washington, Seattle, WA, USA
| | - Thomas D Rea
- Department of Medicine, University of Washington, Seattle, WA, USA; Division of Emergency Medical Services, Public Health Seattle & King County, Seattle, WA, USA
| | - Heemun Kwok
- Department of Emergency Medicine, University of Washington, Seattle, WA, USA
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Soar J, Böttiger BW, Carli P, Couper K, Deakin CD, Djärv T, Lott C, Olasveengen T, Paal P, Pellis T, Perkins GD, Sandroni C, Nolan JP. [Adult advanced life support]. Notf Rett Med 2021; 24:406-446. [PMID: 34121923 PMCID: PMC8185697 DOI: 10.1007/s10049-021-00893-x] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/20/2021] [Indexed: 12/19/2022]
Abstract
These European Resuscitation Council Advanced Life Support guidelines are based on the 2020 International Consensus on Cardiopulmonary Resuscitation Science with Treatment Recommendations. This section provides guidelines on the prevention of and ALS treatments for both in-hospital cardiac arrest and out-of-hospital cardiac arrest.
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Affiliation(s)
- Jasmeet Soar
- Southmead Hospital, North Bristol NHS Trust, Bristol, Großbritannien
| | - Bernd W. Böttiger
- Department of Anaesthesiology and Intensive Care Medicine, Universitätsklinikum Köln, Köln, Deutschland
| | - Pierre Carli
- SAMU de Paris, Center Hospitalier Universitaire Necker Enfants Malades, Assistance Publique Hôpitaux de Paris, and Université Paris Descartes, Paris, Frankreich
| | - Keith Couper
- Critical Care Unit, University Hospitals Birmingham NHS Foundation Trust, Birmingham, Großbritannien
- Warwick Medical School, University of Warwick, Coventry, Großbritannien
| | - Charles D. Deakin
- University Hospital Southampton NHS Foundation Trust, Southampton, Großbritannien
- South Central Ambulance Service NHS Foundation Trust, Otterbourne, Großbritannien
| | - Therese Djärv
- Dept of Acute and Reparative Medicine, Karolinska University Hospital, Stockholm, Schweden
- Department of Medicine Solna, Karolinska Institutet, Stockholm, Schweden
| | - Carsten Lott
- Department of Anesthesiology, University Medical Center, Johannes Gutenberg-Universität Mainz, Mainz, Deutschland
| | - Theresa Olasveengen
- Department of Anesthesiology, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Oslo, Norwegen
| | - Peter Paal
- Department of Anaesthesiology and Intensive Care Medicine, Hospitallers Brothers Hospital, Paracelsus Medical University, Salzburg, Österreich
| | - Tommaso Pellis
- Department of Anaesthesia and Intensive Care, Azienda Sanitaria Friuli Occidentale, Pordenone, Italien
| | - Gavin D. Perkins
- Warwick Medical School and University Hospitals Birmingham NHS Foundation Trust, University of Warwick, Coventry, Großbritannien
| | - Claudio Sandroni
- Department of Intensive Care, Emergency Medicine and Anaesthesiology, Fondazione Policlinico Universitario A. Gemelli-IRCCS, Rom, Italien
- Institute of Anaesthesiology and Intensive Care Medicine, Università Cattolica del Sacro Cuore, Rom, Italien
| | - Jerry P. Nolan
- Warwick Medical School, Coventry, Großbritannien, Consultant in Anaesthesia and Intensive Care Medicine Royal United Hospital, University of Warwick, Bath, Großbritannien
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5
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Soar J, Böttiger BW, Carli P, Couper K, Deakin CD, Djärv T, Lott C, Olasveengen T, Paal P, Pellis T, Perkins GD, Sandroni C, Nolan JP. European Resuscitation Council Guidelines 2021: Adult advanced life support. Resuscitation 2021; 161:115-151. [PMID: 33773825 DOI: 10.1016/j.resuscitation.2021.02.010] [Citation(s) in RCA: 474] [Impact Index Per Article: 158.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
These European Resuscitation Council Advanced Life Support guidelines, are based on the 2020 International Consensus on Cardiopulmonary Resuscitation Science with Treatment Recommendations. This section provides guidelines on the prevention of and ALS treatments for both in-hospital cardiac arrest and out-of-hospital cardiac arrest.
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Affiliation(s)
- Jasmeet Soar
- Southmead Hospital, North Bristol NHS Trust, Bristol, UK.
| | - Bernd W Böttiger
- Department of Anaesthesiology and Intensive Care Medicine, University Hospital of Cologne, Cologne, Germany
| | - Pierre Carli
- SAMU de Paris, Centre Hospitalier Universitaire Necker Enfants Malades, Assistance Publique Hôpitaux de Paris, and Université Paris Descartes, Paris, France
| | - Keith Couper
- Critical Care Unit, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK; Warwick Medical School, University of Warwick, Coventry,UK
| | - Charles D Deakin
- University Hospital Southampton NHS Foundation Trust, Southampton, UK; South Central Ambulance Service NHS Foundation Trust, Otterbourne,UK
| | - Therese Djärv
- Dept of Acute and Reparative Medicine, Karolinska University Hospital, Stockholm, Sweden, Department of Medicine Solna, Karolinska Institutet,Stockholm, Sweden
| | - Carsten Lott
- Department of Anesthesiology, University Medical Center, Johannes Gutenberg-Universitaet Mainz, Germany
| | - Theresa Olasveengen
- Department of Anesthesiology, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Norway
| | - Peter Paal
- Department of Anaesthesiology and Intensive Care Medicine, Hospitallers Brothers Hospital, Paracelsus Medical University, Salzburg, Austria
| | - Tommaso Pellis
- Department of Anaesthesia and Intensive Care, Azienda Sanitaria Friuli Occidentale, Italy
| | - Gavin D Perkins
- University of Warwick, Warwick Medical School and University Hospitals Birmingham NHS Foundation Trust, Coventry, UK
| | - Claudio Sandroni
- Department of Intensive Care, Emergency Medicine and Anaesthesiology, Fondazione Policlinico Universitario A. Gemelli-IRCCS, Rome, Italy; Institute of Anaesthesiology and Intensive Care Medicine, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Jerry P Nolan
- University of Warwick, Warwick Medical School, Coventry, CV4 7AL; Royal United Hospital, Bath, UK
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ECG waveform dataset for predicting defibrillation outcome in out-of-hospital cardiac arrested patients. Data Brief 2020; 34:106635. [PMID: 33364270 PMCID: PMC7753135 DOI: 10.1016/j.dib.2020.106635] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2020] [Revised: 12/04/2020] [Accepted: 12/07/2020] [Indexed: 11/30/2022] Open
Abstract
The provided database of 260 ECG signals was collected from patients with out-of-hospital cardiac arrest while treated by the emergency medical services. Each ECG signal contains a 9 second waveform showing ventricular fibrillation, followed by 1 min of post-shock waveform. Patients’ ECGs are made available in multiple formats. All ECGs recorded during the prehospital treatment are provided in PFD files, after being anonymized, printed in paper, and scanned. For each ECG, the dataset also includes the whole digitized waveform (9 s pre- and 1 min post-shock each) and numerous features in temporal and frequency domain extracted from the 9 s episode immediately prior to the first defibrillation shock. Based on the shock outcome, each ECG file has been annotated by three expert cardiologists, - using majority decision -, as successful (56 cases), unsuccessful (195 cases), or indeterminable (9 cases). The code for preprocessing, for feature extraction, and for limiting the investigation to different temporal intervals before the shock is also provided. These data could be reused to design algorithms to predict shock outcome based on ventricular fibrillation analysis, with the goal to optimize the defibrillation strategy (immediate defibrillation versus cardiopulmonary resuscitation and/or drug administration) for enhancing resuscitation.
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7
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Coult J, Rea TD, Blackwood J, Kudenchuk PJ, Liu C, Kwok H. A method to predict ventricular fibrillation shock outcome during chest compressions. Comput Biol Med 2020; 129:104136. [PMID: 33278632 DOI: 10.1016/j.compbiomed.2020.104136] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2020] [Revised: 11/11/2020] [Accepted: 11/18/2020] [Indexed: 12/22/2022]
Abstract
BACKGROUND Out-of-hospital ventricular fibrillation (VF) cardiac arrest is a leading cause of death. Quantitative analysis of the VF electrocardiogram (ECG) can predict patient outcomes and could potentially enable a patient-specific, guided approach to resuscitation. However, VF analysis during resuscitation is confounded by cardiopulmonary resuscitation (CPR) artifact in the ECG, challenging continuous application to guide therapy throughout resuscitation. We therefore sought to design a method to predict VF shock outcomes during CPR. METHODS Study data included 4577 5-s VF segments collected during and without CPR prior to defibrillation attempts in N = 1151 arrest patients. Using training data (460 patients), an algorithm was designed to predict the VF shock outcomes of defibrillation success (return of organized ventricular rhythm) and functional survival (Cerebral Performance Category 1-2). The algorithm was designed with variable-frequency notch filters to reduce CPR artifact in the ECG based on real-time chest compression rate. Ten ECG features and three dichotomous patient characteristics were developed to predict outcomes. These variables were combined using support vector machines and logistic regression. Algorithm performance was evaluated by area under the receiver operating characteristic curve (AUC) to predict outcomes in validation data (691 patients). RESULTS AUC (95% Confidence Interval) for predicting defibrillation success was 0.74 (0.71-0.77) during CPR and 0.77 (0.74-0.79) without CPR. AUC for predicting functional survival was 0.75 (0.72-0.78) during CPR and 0.76 (0.74-0.79) without CPR. CONCLUSION A novel algorithm predicted defibrillation success and functional survival during ongoing CPR following VF arrest, providing a potential proof-of-concept towards real-time guidance of resuscitation therapy.
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Affiliation(s)
- Jason Coult
- Department of Medicine, University of Washington, Seattle, WA, USA; Center for Progress in Resuscitation, University of Washington, Seattle, WA, USA.
| | - Thomas D Rea
- Department of Medicine, University of Washington, Seattle, WA, USA; Center for Progress in Resuscitation, University of Washington, Seattle, WA, USA; King County Emergency Medical Services, Public Health, Seattle & King County, Seattle, WA, USA
| | - Jennifer Blackwood
- Center for Progress in Resuscitation, University of Washington, Seattle, WA, USA; King County Emergency Medical Services, Public Health, Seattle & King County, Seattle, WA, USA
| | - Peter J Kudenchuk
- Department of Medicine, University of Washington, Seattle, WA, USA; Center for Progress in Resuscitation, University of Washington, Seattle, WA, USA; King County Emergency Medical Services, Public Health, Seattle & King County, Seattle, WA, USA
| | - Chenguang Liu
- Philips Emergency Care & Resuscitation, Bothell, WA, USA
| | - Heemun Kwok
- Center for Progress in Resuscitation, University of Washington, Seattle, WA, USA; Department of Emergency Medicine, University of Washington, Seattle, WA, USA
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Soar J, Berg KM, Andersen LW, Böttiger BW, Cacciola S, Callaway CW, Couper K, Cronberg T, D'Arrigo S, Deakin CD, Donnino MW, Drennan IR, Granfeldt A, Hoedemaekers CWE, Holmberg MJ, Hsu CH, Kamps M, Musiol S, Nation KJ, Neumar RW, Nicholson T, O'Neil BJ, Otto Q, de Paiva EF, Parr MJA, Reynolds JC, Sandroni C, Scholefield BR, Skrifvars MB, Wang TL, Wetsch WA, Yeung J, Morley PT, Morrison LJ, Welsford M, Hazinski MF, Nolan JP. Adult Advanced Life Support: 2020 International Consensus on Cardiopulmonary Resuscitation and Emergency Cardiovascular Care Science with Treatment Recommendations. Resuscitation 2020; 156:A80-A119. [PMID: 33099419 PMCID: PMC7576326 DOI: 10.1016/j.resuscitation.2020.09.012] [Citation(s) in RCA: 135] [Impact Index Per Article: 33.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
This 2020 International Consensus on Cardiopulmonary Resuscitation and Emergency Cardiovascular Care Science With Treatment Recommendations for advanced life support includes updates on multiple advanced life support topics addressed with 3 different types of reviews. Topics were prioritized on the basis of both recent interest within the resuscitation community and the amount of new evidence available since any previous review. Systematic reviews addressed higher-priority topics, and included double-sequential defibrillation, intravenous versus intraosseous route for drug administration during cardiac arrest, point-of-care echocardiography for intra-arrest prognostication, cardiac arrest caused by pulmonary embolism, postresuscitation oxygenation and ventilation, prophylactic antibiotics after resuscitation, postresuscitation seizure prophylaxis and treatment, and neuroprognostication. New or updated treatment recommendations on these topics are presented. Scoping reviews were conducted for anticipatory charging and monitoring of physiological parameters during cardiopulmonary resuscitation. Topics for which systematic reviews and new Consensuses on Science With Treatment Recommendations were completed since 2015 are also summarized here. All remaining topics reviewed were addressed with evidence updates to identify any new evidence and to help determine which topics should be the highest priority for systematic reviews in the next 1 to 2 years.
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Berg KM, Soar J, Andersen LW, Böttiger BW, Cacciola S, Callaway CW, Couper K, Cronberg T, D’Arrigo S, Deakin CD, Donnino MW, Drennan IR, Granfeldt A, Hoedemaekers CW, Holmberg MJ, Hsu CH, Kamps M, Musiol S, Nation KJ, Neumar RW, Nicholson T, O’Neil BJ, Otto Q, de Paiva EF, Parr MJ, Reynolds JC, Sandroni C, Scholefield BR, Skrifvars MB, Wang TL, Wetsch WA, Yeung J, Morley PT, Morrison LJ, Welsford M, Hazinski MF, Nolan JP, Issa M, Kleinman ME, Ristagno G, Arafeh J, Benoit JL, Chase M, Fischberg BL, Flores GE, Link MS, Ornato JP, Perman SM, Sasson C, Zelop CM. Adult Advanced Life Support: 2020 International Consensus on Cardiopulmonary Resuscitation and Emergency Cardiovascular Care Science With Treatment Recommendations. Circulation 2020; 142:S92-S139. [DOI: 10.1161/cir.0000000000000893] [Citation(s) in RCA: 42] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
This
2020 International Consensus on Cardiopulmonary Resuscitation and Emergency Cardiovascular Care Science With Treatment Recommendations
for advanced life support includes updates on multiple advanced life support topics addressed with 3 different types of reviews. Topics were prioritized on the basis of both recent interest within the resuscitation community and the amount of new evidence available since any previous review. Systematic reviews addressed higher-priority topics, and included double-sequential defibrillation, intravenous versus intraosseous route for drug administration during cardiac arrest, point-of-care echocardiography for intra-arrest prognostication, cardiac arrest caused by pulmonary embolism, postresuscitation oxygenation and ventilation, prophylactic antibiotics after resuscitation, postresuscitation seizure prophylaxis and treatment, and neuroprognostication. New or updated treatment recommendations on these topics are presented. Scoping reviews were conducted for anticipatory charging and monitoring of physiological parameters during cardiopulmonary resuscitation. Topics for which systematic reviews and new Consensuses on Science With Treatment Recommendations were completed since 2015 are also summarized here. All remaining topics reviewed were addressed with evidence updates to identify any new evidence and to help determine which topics should be the highest priority for systematic reviews in the next 1 to 2 years.
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10
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Ivanović MD, Hannink J, Ring M, Baronio F, Vukčević V, Hadžievski L, Eskofier B. Predicting defibrillation success in out-of-hospital cardiac arrested patients: Moving beyond feature design. Artif Intell Med 2020; 110:101963. [PMID: 33250144 DOI: 10.1016/j.artmed.2020.101963] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2019] [Revised: 08/23/2020] [Accepted: 09/22/2020] [Indexed: 10/23/2022]
Abstract
OBJECTIVE Optimizing timing of defibrillation by evaluating the likelihood of a successful outcome could significantly enhance resuscitation. Previous studies employed conventional machine learning approaches and hand-crafted features to address this issue, but none have achieved superior performance to be widely accepted. This study proposes a novel approach in which predictive features are automatically learned. METHODS A raw 4s VF episode immediately prior to first defibrillation shock was feed to a 3-stage CNN feature extractor. Each stage was composed of 4 components: convolution, rectified linear unit activation, dropout and max-pooling. At the end of feature extractor, the feature map was flattened and connected to a fully connected multi-layer perceptron for classification. For model evaluation, a 10 fold cross-validation was employed. To balance classes, SMOTE oversampling method has been applied to minority class. RESULTS The obtained results show that the proposed model is highly accurate in predicting defibrillation outcome (Acc = 93.6 %). Since recommendations on classifiers suggest at least 50 % specificity and 95 % sensitivity as safe and useful predictors for defibrillation decision, the reported sensitivity of 98.8 % and specificity of 88.2 %, with the analysis speed of 3 ms/input signal, indicate that the proposed model possesses a good prospective to be implemented in automated external defibrillators. CONCLUSIONS The learned features demonstrate superiority over hand-crafted ones when performed on the same dataset. This approach benefits from being fully automatic by fusing feature extraction, selection and classification into a single learning model. It provides a superior strategy that can be used as a tool to guide treatment of OHCA patients in bringing optimal decision of precedence treatment. Furthermore, for encouraging replicability, the dataset has been made publicly available to the research community.
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Affiliation(s)
- Marija D Ivanović
- Vinca Institute of Nuclear Scientists, University of Belgrade, Belgrade, Serbia.
| | - Julius Hannink
- Machine Learning and Data Analytics Lab, Department of Computer Science, Friedrich-Alexander University Erlangen-Nuremberg, Erlangen, Germany
| | - Matthias Ring
- Machine Learning and Data Analytics Lab, Department of Computer Science, Friedrich-Alexander University Erlangen-Nuremberg, Erlangen, Germany
| | - Fabio Baronio
- CNR and Department of Information Engineering, University of Brescia, Brescia, Italy
| | - Vladan Vukčević
- School of Medicine, University of Belgrade, Belgrade, Serbia
| | - Ljupco Hadžievski
- Vinca Institute of Nuclear Scientists, University of Belgrade, Belgrade, Serbia; Diasens, Belgrade, Serbia
| | - Bjoern Eskofier
- Machine Learning and Data Analytics Lab, Department of Computer Science, Friedrich-Alexander University Erlangen-Nuremberg, Erlangen, Germany
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Chicote B, Aramendi E, Irusta U, Owens P, Daya M, Idris A. Value of capnography to predict defibrillation success in out-of-hospital cardiac arrest. Resuscitation 2019; 138:74-81. [PMID: 30836170 PMCID: PMC6504568 DOI: 10.1016/j.resuscitation.2019.02.028] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2018] [Revised: 02/12/2019] [Accepted: 02/18/2019] [Indexed: 11/21/2022]
Abstract
BACKGROUND AND AIM Unsuccessful defibrillation shocks adversely affect survival from out-of-hospital cardiac arrest (OHCA). Ventricular fibrillation (VF) waveform analysis is the tool-of-choice for the non-invasive prediction of shock success, but surrogate markers of perfusion like end-tidal CO2 (EtCO2) could improve the prediction. The aim of this study was to evaluate EtCO2 as predictor of shock success, both individually and in combination with VF-waveform analysis. MATERIALS AND METHODS In total 514 shocks from 214 OHCA patients (75 first shocks) were analysed. For each shock three predictors of defibrillation success were automatically calculated from the device files: two VF-waveform features, amplitude spectrum area (AMSA) and fuzzy entropy (FuzzyEn), and the median EtCO2 (MEtCO2) in the minute before the shock. Sensitivity, specificity, receiver operating characteristic (ROC) curves and area under the curve (AUC) were calculated, for each predictor individually and for the combination of MEtCO2 and VF-waveform predictors. Separate analyses were done for first shocks and all shocks. RESULTS MEtCO2 in first shocks was significantly higher for successful than for unsuccessful shocks (31mmHg/25mmHg, p<0.05), but differences were not significant for all shocks (32mmHg/29mmHg, p>0.05). MEtCO2 predicted shock success with an AUC of 0.66 for first shocks, but was not a predictor for all shocks (AUC 0.54). AMSA and FuzzyEn presented AUCs of 0.76 and 0.77 for first shocks, and 0.75 and 0.75 for all shocks. For first shocks, adding MEtCO2 improved the AUC of AMSA and FuzzyEn to 0.79 and 0.83, respectively. CONCLUSIONS MEtCO2 predicted defibrillation success only for first shocks. Adding MEtCO2 to VF-waveform analysis in first shocks improved prediction of shock success. VF-waveform features and MEtCO2 were automatically calculated from the device files, so these methods could be introduced in current defibrillators adding only new software.
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Affiliation(s)
- Beatriz Chicote
- Communications Engineering Department, University of the Basque Country UPV/EHU, Ingeniero Torres Quevedo Plaza, 1, 48013 Bilbao, Spain.
| | - Elisabete Aramendi
- Communications Engineering Department, University of the Basque Country UPV/EHU, Ingeniero Torres Quevedo Plaza, 1, 48013 Bilbao, Spain
| | - Unai Irusta
- Communications Engineering Department, University of the Basque Country UPV/EHU, Ingeniero Torres Quevedo Plaza, 1, 48013 Bilbao, Spain
| | - Pamela Owens
- Department of Emergency Medicine, University of Texas Southwesterm Medical Center (UTSW), 5323 Harry Hines Blvd, Dallas, TX, USA
| | - Mohamud Daya
- Department of Emergency Medicine, Oregon Health & Science University, 3181 SW Sam Jackson Park Rd, Portland, OR 97239-3098, USA
| | - Ahamed Idris
- Department of Emergency Medicine, University of Texas Southwesterm Medical Center (UTSW), 5323 Harry Hines Blvd, Dallas, TX, USA
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12
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Coult J, Blackwood J, Sherman L, Rea TD, Kudenchuk PJ, Kwok H. Ventricular Fibrillation Waveform Analysis During Chest Compressions to Predict Survival From Cardiac Arrest. Circ Arrhythm Electrophysiol 2019; 12:e006924. [PMID: 30626208 PMCID: PMC6532650 DOI: 10.1161/circep.118.006924] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
BACKGROUND Quantitative measures of the ventricular fibrillation (VF) 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 because compressions cause ECG artifact. However, such pauses contradict resuscitation guideline recommendations to minimize cardiopulmonary resuscitation 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 before 2755 defibrillation shocks from 1151 out-of-hospital cardiac arrest patients. Twenty-four individual measures and 3 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 ranging from 0.56 to 0.75 (median, 0.73) without chest compressions and from 0.53 to 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 (area under the receiver operating characteristic curve, 0.75; 95% CI, 0.73-0.78) and with compressions (area under the receiver operating characteristic curve, 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 with compression-free analysis. However, support vector machine models exhibited similar performance with and without compressions while also achieving the highest area under the receiver operating characteristic curve. Such machine learning models may, therefore, offer means to guide resuscitation during uninterrupted cardiopulmonary resuscitation.
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Affiliation(s)
- Jason Coult
- Department of Bioengineering, University of Washington,
Seattle, WA
- Center for Progress in Resuscitation, University of
Washington, Seattle, WA
| | - Jennifer Blackwood
- Center for Progress in Resuscitation, University of
Washington, Seattle, WA
- King County Emergency Medical Services, Seattle King County
Department of Public Health, Seattle, WA
| | - Lawrence Sherman
- Department of Bioengineering, University of Washington,
Seattle, WA
- Center for Progress in Resuscitation, University of
Washington, Seattle, WA
- Department of Medicine, University of Washington School of
Medicine, Seattle, WA
| | - Thomas D. Rea
- Center for Progress in Resuscitation, University of
Washington, Seattle, WA
- King County Emergency Medical Services, Seattle King County
Department of Public Health, Seattle, WA
- Department of Medicine, University of Washington School of
Medicine, Seattle, WA
| | - Peter J. Kudenchuk
- Center for Progress in Resuscitation, University of
Washington, Seattle, WA
- King County Emergency Medical Services, Seattle King County
Department of Public Health, Seattle, WA
- Division of Cardiology, University of Washington School of
Medicine, Seattle, WA
| | - Heemun Kwok
- Center for Progress in Resuscitation, University of
Washington, Seattle, WA
- Department of Emergency Medicine, University of Washington
School of Medicine, Seattle, WA
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13
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Ivanović MD, Ring M, Baronio F, Calza S, Vukčević V, Hadžievski L, Maluckov A, Eskofier B. ECG derived feature combination versus single feature in predicting defibrillation success in out-of-hospital cardiac arrested patients. Biomed Phys Eng Express 2018. [DOI: 10.1088/2057-1976/aaebec] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
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14
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Chicote B, Irusta U, Aramendi E, Alcaraz R, Rieta JJ, Isasi I, Alonso D, Baqueriza MDM, Ibarguren K. Fuzzy and Sample Entropies as Predictors of Patient Survival Using Short Ventricular Fibrillation Recordings during out of Hospital Cardiac Arrest. ENTROPY (BASEL, SWITZERLAND) 2018; 20:E591. [PMID: 33265680 PMCID: PMC7513119 DOI: 10.3390/e20080591] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/06/2018] [Revised: 08/04/2018] [Accepted: 08/07/2018] [Indexed: 12/23/2022]
Abstract
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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Affiliation(s)
- Beatriz Chicote
- Department of Communications Engineering, University of the Basque Country (UPV/EHU), 48013 Bilbao, Spain
| | - Unai Irusta
- Department of Communications Engineering, University of the Basque Country (UPV/EHU), 48013 Bilbao, Spain
| | - Elisabete Aramendi
- Department of Communications Engineering, University of the Basque Country (UPV/EHU), 48013 Bilbao, Spain
| | - Raúl Alcaraz
- Research Group in Electronic, Biomedical and Telecommunication Engineering, University of Castilla-La Mancha (UCLM), 16071 Cuenca, Spain
| | - José Joaquín Rieta
- BioMIT.org, Electronic Engineering Department, Universitat Politécnica de Valencia (UPV), 46022 Valencia, Spain
| | - Iraia Isasi
- Department of Communications Engineering, University of the Basque Country (UPV/EHU), 48013 Bilbao, Spain
| | - Daniel Alonso
- Emergency Medical System (Emergentziak-Osakidetza), Basque Health Service, 20014 Donostia, Spain
| | - María del Mar Baqueriza
- Emergency Medical System (Emergentziak-Osakidetza), Basque Health Service, 20014 Donostia, Spain
| | - Karlos Ibarguren
- Emergency Medical System (Emergentziak-Osakidetza), Basque Health Service, 20014 Donostia, Spain
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15
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Application of Entropy-Based Features to Predict Defibrillation Outcome in Cardiac Arrest. ENTROPY 2016. [DOI: 10.3390/e18090313] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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16
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He M, Gong Y, Li Y, Mauri T, Fumagalli F, Bozzola M, Cesana G, Latini R, Pesenti A, Ristagno G. Combining multiple ECG features does not improve prediction of defibrillation outcome compared to single features in a large population of out-of-hospital cardiac arrests. CRITICAL CARE : THE OFFICIAL JOURNAL OF THE CRITICAL CARE FORUM 2015; 19:425. [PMID: 26652159 PMCID: PMC4674958 DOI: 10.1186/s13054-015-1142-z] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/10/2015] [Accepted: 11/18/2015] [Indexed: 11/15/2022]
Abstract
Introduction Quantitative electrocardiographic (ECG) waveform analysis provides a noninvasive reflection of the metabolic milieu of the myocardium during resuscitation and is a potentially useful tool to optimize the defibrillation strategy. However, whether combining multiple ECG features can improve the capability of defibrillation outcome prediction in comparison to single feature analysis is still uncertain. Methods A total of 3828 defibrillations from 1617 patients who experienced out-of-hospital cardiac arrest were analyzed. A 2.048-s ECG trace prior to each defibrillation without chest compressions was used for the analysis. Sixteen predictive features were optimized through the training dataset that included 2447 shocks from 1050 patients. Logistic regression, neural network and support vector machine were used to combine multiple features for the prediction of defibrillation outcome. Performance between single and combined predictive features were compared by area under receiver operating characteristic curve (AUC), sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and prediction accuracy (PA) on a validation dataset that consisted of 1381 shocks from 567 patients. Results Among the single features, mean slope (MS) outperformed other methods with an AUC of 0.876. Combination of complementary features using neural network resulted in the highest AUC of 0.874 among the multifeature-based methods. Compared to MS, no statistical difference was observed in AUC, sensitivity, specificity, PPV, NPV and PA when multiple features were considered. Conclusions In this large dataset, the amplitude-related features achieved better defibrillation outcome prediction capability than other features. Combinations of multiple electrical features did not further improve prediction performance.
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Affiliation(s)
- Mi He
- School of Biomedical Engineering, Third Military Medical University and Chongqing University, 30 Gaotanyan Main Street, Chongqing, 400038, China.
| | - Yushun Gong
- School of Biomedical Engineering, Third Military Medical University and Chongqing University, 30 Gaotanyan Main Street, Chongqing, 400038, China.
| | - Yongqin Li
- School of Biomedical Engineering, Third Military Medical University and Chongqing University, 30 Gaotanyan Main Street, Chongqing, 400038, China.
| | - Tommaso Mauri
- Department of Anesthesia, Critical Care and Emergency, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Via Francesco Sforza, 35, 20122, Milan, Italy.
| | - Francesca Fumagalli
- IRCCS-Istituto di Ricerche Farmacologiche "Mario Negri", Via Privata Giuseppe La Masa, 19, 20156, Milan, Italy.
| | - Marcella Bozzola
- Azienda Regionale Emergenza Urgenza (AREU), Via Alfredo Campanini, 6, 20124, Milan, Italy.
| | - Giancarlo Cesana
- Research Centre on Public Health, Department of Statistics and Quantitative Methods, University of Milano-Bicocca, Piazza dell'Ateneo Nuovo, 1, 20126, Milan, Italy.
| | - Roberto Latini
- IRCCS-Istituto di Ricerche Farmacologiche "Mario Negri", Via Privata Giuseppe La Masa, 19, 20156, Milan, Italy.
| | - Antonio Pesenti
- Department of Anesthesia, Critical Care and Emergency, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Via Francesco Sforza, 35, 20122, Milan, Italy. .,Azienda Regionale Emergenza Urgenza (AREU), Via Alfredo Campanini, 6, 20124, Milan, Italy.
| | - Giuseppe Ristagno
- IRCCS-Istituto di Ricerche Farmacologiche "Mario Negri", Via Privata Giuseppe La Masa, 19, 20156, Milan, Italy.
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17
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Abstract
Background—
This study sought to validate the ability of amplitude spectrum area (AMSA) to predict defibrillation success and long-term survival in a large population of out-of-hospital cardiac arrests.
Methods and Results—
ECGs recorded by automated external defibrillators from different manufacturers were obtained from patients with cardiac arrests occurring in 8 city areas. A database, including 2447 defibrillations from 1050 patients, was used as the derivation group, and an additional database, including 1381 defibrillations from 567 patients, served as validation. A 2-second ECG window before defibrillation was analyzed, and AMSA was calculated. Univariable and multivariable regression analyses and area under the receiver operating characteristic curve were used for associations between AMSA and study end points: defibrillation success, sustained return of spontaneous circulation, and long-term survival. Among the 2447 defibrillations of the derivation database, 26.2% were successful. AMSA was significantly higher before a successful defibrillation than a failing one (13±5 versus 6.8±3.5 mV-Hz) and was an independent predictor of defibrillation success (odds ratio, 1.33; 95% confidence interval, 1.20–1.37) and sustained return of spontaneous circulation (odds ratio, 1.22; 95% confidence interval, 1.17–1.26). Area under the receiver operating characteristic curve for defibrillation success prediction was 0.86 (95% confidence interval, 0.85–0.88). AMSA was also significantly associated with long-term survival. The following AMSA thresholds were identified: 15.5 mV-Hz for defibrillation success and 6.5 mV-Hz for defibrillation failure. In the validation database, AMSA ≥15.5 mV-Hz had a positive predictive value of 84%, whereas AMSA ≤6.5 mV-Hz had a negative predictive value of 98%.
Conclusions—
In this large derivation-validation study, AMSA was validated as an accurate predictor of defibrillation success. AMSA also appeared as a predictor of long-term survival.
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18
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The ventricular fibrillation waveform approach to direct postshock chest compressions in a swine model of VF arrest. J Emerg Med 2014; 48:373-81. [PMID: 25488413 DOI: 10.1016/j.jemermed.2014.09.057] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2014] [Revised: 08/21/2014] [Accepted: 09/30/2014] [Indexed: 11/23/2022]
Abstract
BACKGROUND In retrospective swine and human investigations of ventricular fibrillation (VF) cardiac arrest, the amplitude-spectral area (AMSA), determined from the VF waveform, can predict defibrillation and a return of spontaneous circulation (ROSC). OBJECTIVES We hypothesized that an algorithm using AMSA in real time to direct postshock chest compression (CC) duration would shorten the time to ROSC and improve neurological outcome in a swine model of VF cardiac arrest with acute myocardial infarction (AMI) or nonischemic myocardium. METHODS AMI was induced by occlusion of the left anterior descending artery. VF was untreated for 10 min. Animals were randomized to either traditional resuscitation with 2 min of CC after each shock or to an AMSA-guided algorithm where postshock CCs were shortened to 1 min if the preshock AMSA exceeded 20 mV-Hz. RESULTS A total of 48 animals were studied, 12 in each group (AMI vs. normal, and traditional vs. AMSA-guided). There was a nonsignificant shorter time to ROSC with an AMSA-guided approach in AMI swine (17.2 ± 3.4 vs. 18.5 ± 4.7 min, p = NS), and in normal swine (13.5 ± 1.1 vs. 14.4 ± 1.2, p = NS). Neurological outcome was similar between traditional and AMSA-guided animals. AMSA predicted ROSC (p < 0.001), and a threshold of 20 mV-Hz gave a sensitivity of 89%, with specificity of 29%. CONCLUSION Although AMSA predicts ROSC in a swine model of VF arrest in both AMI and normal swine, a waveform-guided approach that uses AMSA to direct postshock CC duration does not significantly shorten the time to ROSC or alter neurological outcome.
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Median frequencies of prolonged ventricular fibrillation treated by V-A ECMO correspond to a return of spontaneous circulation rate. Int J Artif Organs 2014; 37:48-57. [PMID: 24634334 DOI: 10.5301/ijao.5000291] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/16/2013] [Indexed: 12/18/2022]
Abstract
BACKGROUND The aim of our study was to analyze, in a pig model of prolonged ventricular fibrillation (VF) treated by veno-arterial extracorporeal membrane oxygenation (ECMO), the time dependent changes of VF wavelet frequency obtained from intracardial signals and its relations to return of spontaneous circulation (ROSC). METHODS 11 female pigs (50.3 ± 3.4 kg) under general anesthesia had undergone 15 min of VF with ECMO flow of 5 to 10 ml/kg per min simulating "untreated" VF followed by continued VF with full ECMO flow of 100 ml/kg per min. The median frequency (MF) of VF from right ventricular apex, coronary perfusion pressure, myocardial oxygen metabolism and resuscitability were determined. RESULTS Median (interquartile range) of MF of fibrillatory wavelets in minute 15 of low ECMO flow [9.7 Hz (8.3; 10.1)] was not significantly changed in comparison to minute 1 [10.5 Hz (9.8; 12.4)], p = 0.12. Five minutes after full ECMO initiation MF increased [11.6 Hz (10.6; 13.5)], p = 0.04 (compared to minute 15 of VF) and did not deteriorate during the rest of ECMO treatment. Out of all subjects, three animals did not reach ROSC. Those subjects demonstrated deeper decrease of MF at the VF minute 15 as compared to others [-2.4 Hz (-2.5; -2.3) vs. -0.6 Hz (-1.6; -0.1)] and continuously significantly higher increase in MF on full ECMO support [4.3 Hz (2.9; 5.6) vs. 1.1 Hz (0.6; 1.6)] with p = 0.05 for both observations, respectively. CONCLUSIONS The veno-arterial ECMO reperfusion influences MF of VF wavelet obtained from right ventricular apex. The course of changes in wavelet frequency corresponds to a presence of later ROSC.
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Amplitude spectrum area to guide resuscitation—A retrospective analysis during out-of-hospital cardiopulmonary resuscitation in 609 patients with ventricular fibrillation cardiac arrest. Resuscitation 2013; 84:1697-703. [DOI: 10.1016/j.resuscitation.2013.08.017] [Citation(s) in RCA: 50] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2013] [Revised: 07/29/2013] [Accepted: 08/20/2013] [Indexed: 11/18/2022]
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21
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Deakin CD. À la carte defibrillation poised to enter the fixed price resuscitation menu. Resuscitation 2013; 84:1639-40. [PMID: 24096011 DOI: 10.1016/j.resuscitation.2013.09.016] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2013] [Accepted: 09/24/2013] [Indexed: 01/21/2023]
Affiliation(s)
- Charles D Deakin
- University of Southampton, University Hospital Southampton NHS Foundation Trust, Tremona Road, Southampton SO16 6YD, UK.
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Guerra S, Boscari F, Avogaro A, Di Camillo B, Sparacino G, de Kreutzenberg SV. Hemodynamics assessed via approximate entropy analysis of impedance cardiography time series: effect of metabolic syndrome. Am J Physiol Heart Circ Physiol 2011; 301:H592-8. [DOI: 10.1152/ajpheart.01195.2010] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
The metabolic syndrome (MS), a predisposing condition for cardiovascular disease, presents disturbances in hemodynamics; impedance cardiography (ICG) can assess these alterations. In subjects with MS, the morphology of the pulses present in the ICG time series is more irregular/complex than in normal subjects. Therefore, the aim of the present study was to quantitatively assess the complexity of ICG times series in 53 patients, with or without MS, through a nonlinear analysis algorithm, the approximate entropy, a method employed in recent years for the study of several biological signals, which provides a scalar index, ApEn. We correlated ApEn computed from ICG times series data during fasting and postprandial phase with the presence of alterations in the parameters defining MS [Adult Treatment Panel (ATP) III (Grundy SM, Brewer HB Jr, Cleeman JI, Smith SC Jr, Lenfant C; National Heart, Lung, and Blood Institute; American Heart Association. Circulation 109: 433–438, 2004) and the International Diabetes Federation (IDF) definition]. Results show that ApEn was significantly higher in subjects with MS compared with those without (1.81 ± 0.09 vs. 1.65 ± 0.13; means ± SD; P = 0.0013, with ATP III definition; 1.82 ± 0.09 vs. 1.67 ± 0.12; P = 0.00006, with the IDF definition). We also demonstrated that ApEn increase parallels the number of components of MS. ApEn was then correlated to each MS component: mean ApEn values of subjects belonging to the first and fourth quartiles of the distribution of MS parameters were statistically different for all parameters but HDL cholesterol. No difference was observed between ApEn values evaluated in fasting and postprandial states. In conclusion, we identified that MS is characterized by an increased complexity of ICG signals: this may have a prognostic relevance in subjects with this condition.
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Affiliation(s)
| | | | - Angelo Avogaro
- Clinical and Experimental Medicine, University of Padova; and
- Venetian Institute of Molecular Medicine, Padova, Italy
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