Jason Coult1, Thomas D Rea2, Jennifer Blackwood3, Peter J Kudenchuk2, Chenguang Liu4, Heemun Kwok5. 1. Department of Medicine, University of Washington, Seattle, WA, USA; Center for Progress in Resuscitation, University of Washington, Seattle, WA, USA. Electronic address: jasonc4@uw.edu. 2. 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. 3. Center for Progress in Resuscitation, University of Washington, Seattle, WA, USA; King County Emergency Medical Services, Public Health, Seattle & King County, Seattle, WA, USA. 4. Philips Emergency Care & Resuscitation, Bothell, WA, USA. 5. Center for Progress in Resuscitation, University of Washington, Seattle, WA, USA; Department of Emergency Medicine, University of Washington, Seattle, WA, USA.
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.
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 arrestpatients. 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.
Authors: Brooke Bessen; Jason Coult; Jennifer Blackwood; Cindy H Hsu; Peter Kudenchuk; Thomas Rea; Heemun Kwok Journal: J Am Heart Assoc Date: 2021-09-25 Impact factor: 5.501