ECG changes during resuscitation of patients with initial pulseless electrical activity are associated with return of spontaneous circulation

Abstract

Changes in ECG characteristics during ALS in cardiac arrest presenting as PEA are related to prognosis. An increase in heart rate was observed in the last 3-6 min before ROSC was obtained.

Heart rate and QRS duration as biomarkers predict the immediate outcome from pulseless electrical activity

“…28 Changes in QRSd during cardiac arrest has also been described. 11 The narrowing of the QRS complex observed in our study may be a response to improved myocardial perfusion or relieved right ventricular strain during resuscitation.…”

Heart rate and QRS duration as biomarkers predict the immediate outcome from pulseless electrical activity

“…28 Changes in QRSd during cardiac arrest has also been described. 11 The narrowing of the QRS complex observed in our study may be a response to improved myocardial perfusion or relieved right ventricular strain during resuscitation.…”

Cessation of Resuscitation in Pulseless Electrical Activity Out of Hospital Cardiac Arrest: A Mixed Methods Study of Senior Paramedic Decision Making

“…These results were similar to one study which found a higher death rate in patients with a low, wide QRS, (24) however, incongruent with another study which reported ECGs as a prognostic marker inaccurate. (25) In this study, 44% of patients progressed to a shockable rhythm and 16% achieved ROSC, albeit intermittent. Previous studies found progression to a shockable rhythm (26,27) and ROSC (6,7) increased the chance of survivability.…”

A Machine Learning Model for the Prognosis of Pulseless Electrical Activity during Out-of-Hospital Cardiac Arrest

“…The algorithm consisted of the extraction of the ICC component of the TI (associated with mechanical wall movement), an ECG and ICC feature extraction phase, and an RF classifier. The solution outperformed previous solutions both in the AUC (four to nineteen points) and BAC (three to sixteen points) [ 12 , 14 , 23 , 24 , 26 ]. Several aspects of the solution explained the better performance.…”

“…All the methods were implemented in MATLAB and then evaluated using this study’s dataset and data partitions. The previous proposals included: (1) a preliminary version of the proposed method based on an RF classifier, but using only one ECG feature (AMSA) and one ICC feature ( ) [ 26 ]; (2) an LR model using ECG-only features proposed by Alonso et al [ 24 ] for the immediate prediction of the evolution of cardiac arrest rhythms, including PEA; (3) single-ECG feature models based on the heart rate [ 12 ] and the width of the QRS complexes [ 14 ]. In the original studies [ 12 , 14 ], the HR and QRS widths were manually measured, but in an automatic system, these values have to be automatically computed from the ECG.…”

“…The previous proposals included: (1) a preliminary version of the proposed method based on an RF classifier, but using only one ECG feature (AMSA) and one ICC feature ( ) [ 26 ]; (2) an LR model using ECG-only features proposed by Alonso et al [ 24 ] for the immediate prediction of the evolution of cardiac arrest rhythms, including PEA; (3) single-ECG feature models based on the heart rate [ 12 ] and the width of the QRS complexes [ 14 ]. In the original studies [ 12 , 14 ], the HR and QRS widths were manually measured, but in an automatic system, these values have to be automatically computed from the ECG. The wavedec wavelet-based algorithm was applied both for QRS detection and HR calculations, and for ECG delineation and QRS width calculations [ 43 ], and then, we used these features in a single-feature LR classifier.…”

Heart rate and QRS duration as biomarkers predict the immediate outcome from pulseless electrical activity

“…28 Changes in QRSd during cardiac arrest has also been described. 11 The narrowing of the QRS complex observed in our study may be a response to improved myocardial perfusion or relieved right ventricular strain during resuscitation.…”

Cessation of Resuscitation in Pulseless Electrical Activity Out of Hospital Cardiac Arrest: A Mixed Methods Study of Senior Paramedic Decision Making

“…These results were similar to one study which found a higher death rate in patients with a low, wide QRS, (24) however, incongruent with another study which reported ECGs as a prognostic marker inaccurate. (25) In this study, 44% of patients progressed to a shockable rhythm and 16% achieved ROSC, albeit intermittent. Previous studies found progression to a shockable rhythm (26,27) and ROSC (6,7) increased the chance of survivability.…”

A Machine Learning Model for the Prognosis of Pulseless Electrical Activity during Out-of-Hospital Cardiac Arrest

“…The algorithm consisted of the extraction of the ICC component of the TI (associated with mechanical wall movement), an ECG and ICC feature extraction phase, and an RF classifier. The solution outperformed previous solutions both in the AUC (four to nineteen points) and BAC (three to sixteen points) [ 12 , 14 , 23 , 24 , 26 ]. Several aspects of the solution explained the better performance.…”

“…All the methods were implemented in MATLAB and then evaluated using this study’s dataset and data partitions. The previous proposals included: (1) a preliminary version of the proposed method based on an RF classifier, but using only one ECG feature (AMSA) and one ICC feature ( ) [ 26 ]; (2) an LR model using ECG-only features proposed by Alonso et al [ 24 ] for the immediate prediction of the evolution of cardiac arrest rhythms, including PEA; (3) single-ECG feature models based on the heart rate [ 12 ] and the width of the QRS complexes [ 14 ]. In the original studies [ 12 , 14 ], the HR and QRS widths were manually measured, but in an automatic system, these values have to be automatically computed from the ECG.…”

“…The previous proposals included: (1) a preliminary version of the proposed method based on an RF classifier, but using only one ECG feature (AMSA) and one ICC feature ( ) [ 26 ]; (2) an LR model using ECG-only features proposed by Alonso et al [ 24 ] for the immediate prediction of the evolution of cardiac arrest rhythms, including PEA; (3) single-ECG feature models based on the heart rate [ 12 ] and the width of the QRS complexes [ 14 ]. In the original studies [ 12 , 14 ], the HR and QRS widths were manually measured, but in an automatic system, these values have to be automatically computed from the ECG. The wavedec wavelet-based algorithm was applied both for QRS detection and HR calculations, and for ECG delineation and QRS width calculations [ 43 ], and then, we used these features in a single-feature LR classifier.…”

Heart rate and QRS duration as biomarkers predict the immediate outcome from pulseless electrical activity

“…28 Changes in QRSd during cardiac arrest has also been described. 11 The narrowing of the QRS complex observed in our study may be a response to improved myocardial perfusion or relieved right ventricular strain during resuscitation.…”

Cessation of Resuscitation in Pulseless Electrical Activity Out of Hospital Cardiac Arrest: A Mixed Methods Study of Senior Paramedic Decision Making

“…These results were similar to one study which found a higher death rate in patients with a low, wide QRS, (24) however, incongruent with another study which reported ECGs as a prognostic marker inaccurate. (25) In this study, 44% of patients progressed to a shockable rhythm and 16% achieved ROSC, albeit intermittent. Previous studies found progression to a shockable rhythm (26,27) and ROSC (6,7) increased the chance of survivability.…”

A Machine Learning Model for the Prognosis of Pulseless Electrical Activity during Out-of-Hospital Cardiac Arrest

“…The algorithm consisted of the extraction of the ICC component of the TI (associated with mechanical wall movement), an ECG and ICC feature extraction phase, and an RF classifier. The solution outperformed previous solutions both in the AUC (four to nineteen points) and BAC (three to sixteen points) [ 12 , 14 , 23 , 24 , 26 ]. Several aspects of the solution explained the better performance.…”

“…All the methods were implemented in MATLAB and then evaluated using this study’s dataset and data partitions. The previous proposals included: (1) a preliminary version of the proposed method based on an RF classifier, but using only one ECG feature (AMSA) and one ICC feature ( ) [ 26 ]; (2) an LR model using ECG-only features proposed by Alonso et al [ 24 ] for the immediate prediction of the evolution of cardiac arrest rhythms, including PEA; (3) single-ECG feature models based on the heart rate [ 12 ] and the width of the QRS complexes [ 14 ]. In the original studies [ 12 , 14 ], the HR and QRS widths were manually measured, but in an automatic system, these values have to be automatically computed from the ECG.…”

“…The previous proposals included: (1) a preliminary version of the proposed method based on an RF classifier, but using only one ECG feature (AMSA) and one ICC feature ( ) [ 26 ]; (2) an LR model using ECG-only features proposed by Alonso et al [ 24 ] for the immediate prediction of the evolution of cardiac arrest rhythms, including PEA; (3) single-ECG feature models based on the heart rate [ 12 ] and the width of the QRS complexes [ 14 ]. In the original studies [ 12 , 14 ], the HR and QRS widths were manually measured, but in an automatic system, these values have to be automatically computed from the ECG. The wavedec wavelet-based algorithm was applied both for QRS detection and HR calculations, and for ECG delineation and QRS width calculations [ 43 ], and then, we used these features in a single-feature LR classifier.…”