Ventricular fibrillation waveform measures and the etiology of cardiac arrest

Hidano, Danelle; Coult, Jason; Blackwood, Jennifer; Fahrenbruch, Carol; Kwok, Heemun; Kudenchuk, Peter J.; Rea, Thomas D.

doi:10.1016/j.resuscitation.2016.10.007

Cited by 11 publications

(11 citation statements)

References 33 publications

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“…The evidence updates restricted the search to January 2008 to January 2020 and identified one large RCT conducted in 2013 45 and 20 observational studies. 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 In addition, there is an ongoing multicentre RCT of real-time amplitude spectrum area to guide defibrillation (NCT03237910). Although the VF waveform analyses and outcomes studied were highly heterogeneous, given the amount of data available, an updated SysRev was suggested.…”

Section: Defibrillation Strategies For Ventricular Fibrillation or Pumentioning

confidence: 99%

Adult Advanced Life Support: 2020 International Consensus on Cardiopulmonary Resuscitation and Emergency Cardiovascular Care Science with Treatment Recommendations

et al. 2020

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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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Section: Defibrillation Strategies For Ventricular Fibrillation or Pumentioning

confidence: 99%

Adult Advanced Life Support: 2020 International Consensus on Cardiopulmonary Resuscitation and Emergency Cardiovascular Care Science with Treatment Recommendations

et al. 2020

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“…Among the different studies on the impact of a previous MI, data are consistent, demonstrating lower VF amplitude characteristics in the leads adjacent to the area of infarction [13,[16][17][18]. In contrast, results of the studies on acute MI yielded contrasting findings in the setting of OHCA [13][14][15]. Despite the frequent occurrence of LV dilatation and hypertrophy, and the associated increased risk of sudden cardiac death, the impact of the LV diameter and mass on VF characteristics has so far only scarcely been studied [27].…”

Section: Human Studiesmentioning

confidence: 89%

“…Measures related to the VF amplitude Acute myocardial infarction [13][14][15] = ↓/= Previous myocardial infarction [13,[16][17][18]22] ↓/= ↓ Increased LV diameter [17] ↓ = Increased LV mass = ↑/= Low LV ejection fraction [33] ↓ Unk Beta blockers [28] ↓ Unk Class III anti-arrhythmic drugs [17] ↓ = Longer arrest duration [34,35] ↓ ↓ CPR [36,37] ↑ ↑ CPR: cardiopulmonary resuscitation, LV: left ventricular, unk: unknown, VF: ventricular fibrillation.…”

Section: Measures Related To the Vf Cycle Lengthmentioning

confidence: 99%

“…be lower in the presence than in the absence of an acute myocardial infarction (MI) [10,11]. In contrast, studies in OHCA-patients yielded inconsistent results with regard to the impact of an acute MI [13][14][15]. Besides acute factors, there is robust evidence in both patients and animals that pre-existent cardiac disease affects VF characteristics as well [12,[16][17][18].…”

Section: Introductionmentioning

confidence: 99%

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Ventricular fibrillation waveform characteristics of the surface ECG: Impact of the left ventricular diameter and mass

et al. 2017

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Frequency characteristics of VF were consistently lower in case of an increased LV diameter. Whereas LV mass does not affect the frequency of the VF waveform, amplitudes seem higher with increasing mass. These findings add to the current knowledge of factors that modulate VF characteristics of the surface ECG and provide insight into factors which may be accounted for in future studies on VF-guided resuscitative interventions.

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“…For example, the occurrence of amplitude variations observed on the electrograms were used as a determinant in whether a defibrillating shock would be successful in resuscitating the heart from an arrhythmic episode [25]. Features that characterize the electrograms (such as the amplitude spectrum area) were observed to be a marker that predict successful shock outcome [26], but have been only marginally successful in understanding the etiology of the heart [27]. The analysis of the normal electrical activations in a bipolar electrogram demonstrated that these activations to could be used to determine the organi-zational aspect of the arrhythmia [28].…”

Section: Current Researchmentioning

confidence: 99%

Morphologically constrained adaptive signal decompositions in studying ventricular arrhythmias

Balasundaram¹

2021

Preprint

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Ventricular fibrillation (VF) is one of the major causes for sudden cardiac deaths (SCD). The duration from the onset of VF to SCD is a few minutes, making it difficult to study VF. This dissertation proposes methods to extract meaningful information from VF electrograms and formulate associations to underlying structural and physiological properties of the cardiac tissue and clinical events of interest during VF. This was achieved by analyzing clues in the electrograms during VF to infer the underlying anatomical and physiological properties of the cardiac tissue and certain clinical events of interest, which is otherwise not easily available. The proposed methods will be of great assistance for the diagnosis and treatment planning of cardiac arrhythmias. The proposed adaptive time-frequency (TF) signal decomposition was separated into two categories based on two purposes: (1) Time-specific event detection and (2) Time-averaged VA characterization. For the time-specific event detection (in this work rotor detection), electrogram signal features related to the rotor event were identified with an adaptive TF decomposition and amodified criterion function. Using the proposed features and a linear discriminant analysis based classifier with leave-one-out cross validation, overall classification accuracies of 80.77% and 79.41% were achieved in detecting rotor events and separating them from similar but non-rotor events. In the time-averaged ventricular arrhythmia characterization, previously established signal features were used to associate electrogram clues to the structural and physiological characteristics of the cardiac tissue. Using label-consistent K-means singular value decomposition dictionary learning process, dictionaries of TF basis functions were generated to capture specific electric structures and physiological characteristics of the underlying cardiac tissue. The association of these characteristics with the extracted electrogram clues were validated using a cross-validation technique. The cross-validated results ranged from 65.58% to 81.80% for the 7 characteristics used in this study. Further to this, to build a decision-support system with non-linear separable capabilities that could automate and infer the heart events and/or characteristics from the identified electrogram signal structures, neural network models were generated. The cross-validated accuracies ranged from 66.99% to 85.90% for each of the developed models for the decision-support system.

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Ventricular fibrillation waveform measures and the etiology of cardiac arrest

Cited by 11 publications

References 33 publications

Adult Advanced Life Support: 2020 International Consensus on Cardiopulmonary Resuscitation and Emergency Cardiovascular Care Science with Treatment Recommendations

Adult Advanced Life Support: 2020 International Consensus on Cardiopulmonary Resuscitation and Emergency Cardiovascular Care Science with Treatment Recommendations

Ventricular fibrillation waveform characteristics of the surface ECG: Impact of the left ventricular diameter and mass

Morphologically constrained adaptive signal decompositions in studying ventricular arrhythmias

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