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Nas J, van Dongen LH, Thannhauser J, Hulleman M, van Royen N, Tan HL, Bonnes JL, Koster RW, Brouwer MA, Blom MT. The effect of the localisation of an underlying ST-elevation myocardial infarction on the VF-waveform: A multi-centre cardiac arrest study. Resuscitation 2021; 168:11-18. [PMID: 34500021 DOI: 10.1016/j.resuscitation.2021.08.049] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2021] [Revised: 08/25/2021] [Accepted: 08/31/2021] [Indexed: 11/29/2022]
Abstract
INTRODUCTION In cardiac arrest, ventricular fibrillation (VF) waveform characteristics such as amplitude spectrum area (AMSA) are studied to identify an underlying myocardial infarction (MI). Observational studies report lower AMSA-values in patients with than without underlying MI. Moreover, experimental studies with 12-lead ECG-recordings show lowest VF-characteristics when the MI-localisation matches the ECG-recording direction. However, out-of-hospital cardiac arrest (OHCA)-studies with defibrillator-derived VF-recordings are lacking. METHODS Multi-centre (Amsterdam/Nijmegen, the Netherlands) cohort-study on the association between AMSA, ST-elevation MI (STEMI) and its localisation. AMSA was calculated from defibrillator pad-ECG recordings (proxy for lead II, inferior vantage point); STEMI-localisation was determined using ECG/angiography/autopsy findings. RESULTS We studied AMSA-values in 754 OHCA-patients. There were statistically significant differences between no STEMI, anterior STEMI and inferior STEMI (Nijmegen: no STEMI 13.0mVHz [7.9-18.6], anterior STEMI 7.5mVHz [5.6-13.8], inferior STEMI 7.5mVHz [5.4-11.8], p = 0.006. Amsterdam: 11.7mVHz [5.0-21.9], 9.6mVHz [4.6-17.2], and 6.9mVHz [3.2-16.0], respectively, p = 0.001). Univariate analyses showed significantly lower AMSA-values in inferior STEMI vs. no STEMI; there was no significant difference between anterior and no STEMI. After correction for confounders, adjusted absolute AMSA-values were numerically lowest for inferior STEMI in both cohorts, and the relative differences in AMSA between inferior and no STEMI was 1.4-1.7 times larger than between anterior and no STEMI. CONCLUSION This multi-centre VF-waveform OHCA-study showed significantly lower AMSA in case of underlying STEMI, with a more pronounced difference for inferior than for anterior STEMI. Confirmative studies on the impact of STEMI-localisation on the VF-waveform are warranted, and might contribute to earlier diagnosis of STEMI during VF.
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Affiliation(s)
- J Nas
- Department of Cardiology, Radboud University Medical Center, Geert Grooteplein Zuid 10, 6525 GA Nijmegen, the Netherlands.
| | - L H van Dongen
- Department of Cardiology, Amsterdam UMC, location AMC, Meibergdreef 9, 1105AZ Amsterdam, the Netherlands
| | - J Thannhauser
- Department of Cardiology, Radboud University Medical Center, Geert Grooteplein Zuid 10, 6525 GA Nijmegen, the Netherlands
| | - M Hulleman
- Department of Cardiology, Amsterdam UMC, location AMC, Meibergdreef 9, 1105AZ Amsterdam, the Netherlands
| | - N van Royen
- Department of Cardiology, Radboud University Medical Center, Geert Grooteplein Zuid 10, 6525 GA Nijmegen, the Netherlands
| | - H L Tan
- Department of Cardiology, Amsterdam UMC, location AMC, Meibergdreef 9, 1105AZ Amsterdam, the Netherlands; Netherlands Heart Institute, Utrecht, the Netherlands
| | - J L Bonnes
- Department of Cardiology, Radboud University Medical Center, Geert Grooteplein Zuid 10, 6525 GA Nijmegen, the Netherlands
| | - R W Koster
- Department of Cardiology, Amsterdam UMC, location AMC, Meibergdreef 9, 1105AZ Amsterdam, the Netherlands
| | - M A Brouwer
- Department of Cardiology, Radboud University Medical Center, Geert Grooteplein Zuid 10, 6525 GA Nijmegen, the Netherlands
| | - M T Blom
- Department of Cardiology, Amsterdam UMC, location AMC, Meibergdreef 9, 1105AZ Amsterdam, the Netherlands
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Bender D, Morgan RW, Nadkarni VM, Berg RA, Zhang B, Kilbaugh TJ, Sutton RM, Nataraj C. MLWAVE: A novel algorithm to classify primary versus secondary asphyxia-associated ventricular fibrillation. Resusc Plus 2021; 5:100052. [PMID: 33569548 PMCID: PMC7869586 DOI: 10.1016/j.resplu.2020.100052] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2020] [Revised: 11/08/2020] [Accepted: 11/10/2020] [Indexed: 11/04/2022] Open
Abstract
INTRODUCTION/HYPOTHESIS The outcome of cardiopulmonary resuscitation (CPR) depends on timely recognition of the underlying cause of cardiac arrest. Ventricular fibrillation (VF) waveform analysis to differentiate primary VF from secondary asphyxia-associated VF may allow tailoring of therapies to improve cardiac arrest outcomes. Therefore, the primary goal of this investigation was to develop a novel technique utilizing wavelet synchrosqueezed transform (WSST) and decision-tree classifier that was specifically adapted to discriminate between these two incidents of VF. METHODS Secondary analytical investigation of electrocardiography (ECG) data obtained from swine models of either primary VF (n=18) or secondary asphyxia-associated VF (7min of asphyxia prior to VF induction; n=12). In the primary analysis, WSST technique was applied to the first 35s of the VF ECG signal to identify the most differentiating characteristics of the signal for use as features to develop a machine learning algorithm to classify the arrest as either primary VF vs. secondary asphyxia-associated VF. The performance of this new interactive Machine Learning algorithm with Wavelet Energy features of ECG (MLWAVE) was assessed using both classification accuracy and area under the receiver operating characteristic curve (AUCROC). To evaluate the validity of the new technique, the amplitude spectrum area (AMSA)-based technique, a well-established defibrillation classification method, was also applied to the same ECG signals. The classification accuracy and AUCROC were then compared between the two techniques. RESULTS For the primary analysis evaluating the first 35s of the VF waveform, the MLWAVE technique classified the type of VF with high accuracy (28/28 [100%], AUCROC: 1.00). The MLWAVE technique performed better than the AMSA technique across all comparisons, but given the small sample sizes, differences were not statistically significant (accuracy: 100% vs. 85.7%; p=0.24; AUCROC: 1.00 vs. 0.82; p=0.24). CONCLUSION This analytical investigation illustrates the advantages of the MLWAVE signal processing method which was associated with 100% accuracy in classifying the type of VF waveform: primary vs. asphyxia-associated. Such classification could lead to personalized tailoring of resuscitation (e.g., immediate defibrillation vs. continued CPR and treatment of reversible cardiac arrest causes before defibrillation) to improve outcomes for cardiac arrest.
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Affiliation(s)
- Dieter Bender
- Villanova Center for Analytics of Dynamic Systems, Villanova University, Villanova, PA, USA
| | - Ryan W. Morgan
- Department of Anesthesiology and Critical Care Medicine, The Children’s Hospital of Philadelphia, University of Pennsylvania, Philadelphia, PA, USA
| | - Vinay M. Nadkarni
- Department of Anesthesiology and Critical Care Medicine, The Children’s Hospital of Philadelphia, University of Pennsylvania, Philadelphia, PA, USA
| | - Robert A. Berg
- Department of Anesthesiology and Critical Care Medicine, The Children’s Hospital of Philadelphia, University of Pennsylvania, Philadelphia, PA, USA
| | - Bingqing Zhang
- Healthcare Analytics Unit, Department of Biomedical and Health Informatics, The Children’s Hospital of Philadelphia, Philadelphia, PA, USA
| | - Todd J. Kilbaugh
- Department of Anesthesiology and Critical Care Medicine, The Children’s Hospital of Philadelphia, University of Pennsylvania, Philadelphia, PA, USA
| | - Robert M. Sutton
- Department of Anesthesiology and Critical Care Medicine, The Children’s Hospital of Philadelphia, University of Pennsylvania, Philadelphia, PA, USA
| | - C. Nataraj
- Moritz Professor & Director, Villanova Center for Analytics of Dynamic Systems, Villanova University, Villanova, PA, USA
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