Abstract 13102: Outcomes of Primary and Secondary Pulseless Electrical Activity During In-Hospital Cardiac Arrest

Circulation ◽  
2021 ◽  
Vol 144 (Suppl_2) ◽  
Author(s):  
Eirik Unneland ◽  
Anders Norvik ◽  
Shaun McGovern ◽  
David Buckler ◽  
Unai Irusta ◽  
...  
Keyword(s):  
Cardiac Arrest ◽  
Electrical Activity ◽  
Additive Model ◽  
Pulseless Electrical Activity ◽  
Spontaneous Circulation ◽  
Clinical State ◽  
Time To Event ◽  
Time To Event Data ◽  
Average Patient ◽  
Hospital Cardiac Arrest

Background: Pulseless Electrical Activity (PEA) is common during in-hospital cardiac arrest. We investigated the development of four types of PEA: PEA as presenting clinical state (primary) and PEA secondary to transient return of spontaneous circulation (ROSC), ventricular fibrillation/tachycardia (VF/VT), or asystole (ASY). Methods: We analyzed 660 episodes of cardiac arrest at one Norwegian and three U.S. hospitals. ECG, chest compressions and ventilations were recorded by defibrillators during CPR. Clinical states were annotated using a graphical application. We quantified the transition intensities from PEA to ROSC (i.e. the immediate probability of a transition), and the observed half-lives for the four types of PEA (i.e. how quickly PEA develops into another clinical state), using Aalen’s additive model for time-to-event data. Results: The transition intensities to ROSC from primary PEA (n=386) and secondary PEA after ASY (n=226) were about 0.08 per minute, peaking at 6 and 9 min, respectively (figure, left). Thus, an average patient in these types of PEA has about 8% chance to achieve ROSC in one minute. Much higher transition intensities to ROSC of about 0.20 per min were observed for secondary PEA after transient ROSC (n=209) or VF/VT (n=225), peaking at 10 and 5 min, respectively. Half-live times for the four types of PEA (figure, right) were 8.5 min, 6.8 min, 4.6 min and 1.6 min, for primary PEA, and secondary PEA after ASY, transient ROSC and VF/VT, respectively. Discussion: The observed clinical development of PEA in terms of intensity, peak intensity and half-lives during resuscitation differs substantially between the four types of PEA. The chance of obtaining ROSC is considerably lower in primary PEA or PEA after ASY, compared to PEA following transient ROSC or after VF/VT. This may increase understanding of the nature of PEA and the process leading to ROSC; and allow for simple prognostic assessments during a resuscitation attempt.

Abstract 157: Multicenter Evaluation of Etiology and Observed Transition Intensities Between Pulseless Electrical Activity and Return of Spontaneous Circulation

Circulation ◽  
2020 ◽  
Vol 142 (Suppl_4) ◽  
Author(s):  
Eirik Unneland ◽  
Anders Norvik ◽  
Shaun K. McGovern ◽  
David G. Buckler ◽  
Unai Irusta ◽  
...  
Keyword(s):  
Cardiac Arrest ◽  
Electrical Activity ◽  
Additive Model ◽  
Pulseless Electrical Activity ◽  
Spontaneous Circulation ◽  
Time To Event ◽  
Transition Intensity ◽  
Time To Event Data ◽  
Current State ◽  
Average Patient

Background: Cardiac arrest presents with one of three clinical states; Cardiac standstill (asystole), Pulseless Electrical Activity (PEA), or ventricular fibrillation/tachycardia (VF/VT). PEA results from multiple etiologies and accounts for most in-hospital cardiac arrests. We quantified the dynamic nature of transitions in and out of PEA, in terms of hospital sites and presumed etiology. Methods: We analyzed 538 episodes of cardiac arrest at one Norwegian hospital and three U.S. hospitals. ECG, chest compressions and ventilations were recorded by defibrillators during CPR. Each event was assessed using a graphical application. We quantified the transition intensity, i.e. the immediate probability of a transition given the current state, between PEA and ROSC using Aalen’s additive model for time-to-event data. Results: The overall transition intensity from PEA to ROSC was about 0.10 min -1 , so an average patient in PEA has about 10 % chance to gain ROSC the following 1 minute. The intensity peaked at 7 minutes of CPR (Figure), with some heterogeneity between hospitals (0.07 to 0.11 min -1 ). The reverse transition intensity from ROSC to PEA was rather constant at 0.10 min -1 (range 0.05-0.11). Information regarding assumed cardiac or non-cardiac etiology was available in 208 episodes (123 cardiac). Patients with a cardiac etiology had a marginally smaller (-0.03 min -1 ) chance of making the transition from PEA to ROSC (p=0.049), but etiology did not impact on the ROSC to PEA transition (p=0.39). Discussion: For transitions between PEA and ROSC we observed an overall intensity of 0.1 min -1 , with some hospital heterogeneity. This may be due to heterogeneity in the underlying patient populations. We found the probability of transitioning from PEA to ROSC to increase from the start event recording until an average peak intensity at 7 minutes. This information may increase the clinicians’ understanding of the process from PEA to ROSC.

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

Entropy ◽  
2021 ◽  
Vol 23 (7) ◽  
pp. 847
Author(s):  
Jon Urteaga ◽  
Elisabete Aramendi ◽  
Andoni Elola ◽  
Unai Irusta ◽  
Ahamed Idris
Keyword(s):  
Machine Learning ◽  
Cardiac Arrest ◽  
Electrical Activity ◽  
Area Under The Curve ◽  
Pulseless Electrical Activity ◽  
Learning Model ◽  
Spontaneous Circulation ◽  
Machine Learning Model ◽  
Balance Accuracy ◽  
Hospital Cardiac Arrest

Pulseless electrical activity (PEA) is characterized by the disassociation of the mechanical and electrical activity of the heart and appears as the initial rhythm in 20–30% of out-of-hospital cardiac arrest (OHCA) cases. Predicting whether a patient in PEA will convert to return of spontaneous circulation (ROSC) is important because different therapeutic strategies are needed depending on the type of PEA. The aim of this study was to develop a machine learning model to differentiate PEA with unfavorable (unPEA) and favorable (faPEA) evolution to ROSC. An OHCA dataset of 1921 5s PEA signal segments from defibrillator files was used, 703 faPEA segments from 107 patients with ROSC and 1218 unPEA segments from 153 patients with no ROSC. The solution consisted of a signal-processing stage of the ECG and the thoracic impedance (TI) and the extraction of the TI circulation component (ICC), which is associated with ventricular wall movement. Then, a set of 17 features was obtained from the ECG and ICC signals, and a random forest classifier was used to differentiate faPEA from unPEA. All models were trained and tested using patientwise and stratified 10-fold cross-validation partitions. The best model showed a median (interquartile range) area under the curve (AUC) of 85.7(9.8)% and a balance accuracy of 78.8(9.8)%, improving the previously available solutions at more than four points in the AUC and three points in balanced accuracy. It was demonstrated that the evolution of PEA can be predicted using the ECG and TI signals, opening the possibility of targeted PEA treatment in OHCA.

Abstract 110: The Dynamic Clinical Course of Resuscitation in Children With Cardiac Arrest

Circulation ◽  
2018 ◽  
Vol 138 (Suppl_2) ◽  
Author(s):  
Trond Nordseth ◽  
Dana Niles ◽  
Trygve Eftestøl ◽  
Vinay Nadkarni ◽  
Robert Sutton ◽  
...  
Keyword(s):  
Cardiac Arrest ◽  
Clinical Course ◽  
Pulseless Electrical Activity ◽  
Spontaneous Circulation ◽  
Clinical State ◽  
State Transitions ◽  
Temporary Increase ◽  
Older Children ◽  
Chest Compressions ◽  
Hospital Cardiac Arrest

Background: During cardiac arrest, a child may be in one of five clinical states (“rhythms”): 1) Bradycardia with poor perfusion; 2) Pulseless Electrical Activity (PEA); 3) Ventricular Fibrillation or Pulseless Ventricular Tachycardia (VF/VT); 4) Asystole; or 5) Spontaneous Circulation (ROSC). The aim of study was to investigate and quantify the dynamic characteristics of this process. Methods: We prospectively acquired data on rhythm and clinical states using recording defibrillators during active CPR. Recordings were analyzed as a multi-state statistical model, focusing on transitions between PEA (including bradycardia with poor perfusion), VF/VT, Asystole, and ROSC (defined as an organized electrical rhythm without chest compressions >= 1 minute). Instantaneous transition rates were obtained by smoothing the Nelson-Aalen estimator of cumulative intensities. Results: In 74 Cardiac Arrest events with evaluable data, median patient age was 15 years [range 1.75 to 22.9; IQR 11 to 17]. Fifty percent had a respiratory etiology and 51 % were female. PEA was the most frequent presenting cardiac arrest rhythm (38 %); followed by VF/VT (24 %), bradycardia (22 %), and asystole (16 %). Starting from time of defibrillator attachment (median 3 minutes into the event) as shown in the figure that shows 45 minutes of CPR, the prevalence of sustained ROSC reached an asymptotic value of 30 % at 20 minutes. We observed a temporary surge of PEA at about 12 minutes, resulting from a doubling (from 0.1 to 0.2 transitions/min) of the instantaneous transition rate of ROSC to PEA during this period. Conclusion: We provide a quantitative overview of the dynamic process of clinical state transitions during in-hospital cardiac arrest and resuscitation in older children and adolescents. A notable feature was a temporary increase in the prevalence of PEA at 12 minutes.

Abstract 150: Impact of Epinephrine on ECG Changes During Resuscitation From Out-of-Hospital Cardiac Arrest With Initial Pulseless Electrical Activity

Circulation ◽  
2018 ◽  
Vol 138 (Suppl_2) ◽  
Author(s):  
Gunnar W Skjeflo ◽  
Eirik Skogvoll ◽  
Jan Pål Loennechen ◽  
Theresa M Olasveengen ◽  
Lars Wik ◽  
...  
Keyword(s):  
Heart Rate ◽  
Cardiac Arrest ◽  
Electrical Activity ◽  
Multivariate Regression ◽  
Life Support ◽  
Controlled Trial ◽  
Pulseless Electrical Activity ◽  
Qrs Complex ◽  
Epinephrine Administration ◽  
Hospital Cardiac Arrest

Introduction: Presence of electrocardiographic rhythm, documented by the electrocardiogram (ECG), in the absence of palpable pulses defines pulseless electrical activity (PEA). Our aims were to examine the development of ECG characteristics during advanced life support (ALS) from Out-of-Hospital-Cardiac-Arrest (OHCA) with initial PEA, and to explore the effects of epinephrine on these characteristics. Methods: Patients with OHCA and initial PEA in a randomized controlled trial of ALS with or without intravenous access and medications were included. QRS widths and heart-rates were measured in recorded ECG signals during pauses in compressions. Statistical analysis was carried out by multivariate regression (MANOVA). Results: Defibrillator recordings from 170 episodes of cardiac arrest were analyzed, 4840 combined measurements of QRS complex width and heart rate were made. By the multivariate regression model both whether epinephrine was administered and whether return of spontaneous circulation (ROSC) was obtained were significantly associated with changes in QRS width and heart rate. For both control and epinephrine groups, ROSC was preceded by decreasing QRS width and increasing rate, but in the epinephrine group an increase in rate without a decrease in QRS width was associated with poor outcome (fig). Conclusion: The QRS complex characteristics are affected by epinephrine administration during ALS, but still yields valuable prognostic information.

QRS complex characteristics and patient outcomes in out-of-hospital pulseless electrical activity cardiac arrest

2020 ◽  
Vol 38 (1) ◽  
pp. 53-58
Author(s):  
Jung Ho Kim ◽  
Hyun Wook Ryoo ◽  
Jong-yeon Kim ◽  
Jae Yun Ahn ◽  
Sungbae Moon ◽  
...  
Keyword(s):  
Cardiac Arrest ◽  
Electrical Activity ◽  
Hospital Discharge ◽  
Neurological Outcome ◽  
Prospective Observational Study ◽  
Pulseless Electrical Activity ◽  
Secondary Outcome ◽  
Qrs Complex ◽  
The Relationship ◽  
Hospital Cardiac Arrest

BackgroundPulseless electrical activity (PEA) is increasingly observed in out-of-hospital cardiac arrest (OHCA), but outcomes are still poor. We aimed to assess the relationship between QRS characteristics and outcomes of patients with OHCA with initial PEA (OHCA-P).MethodsThis prospective observational study included patients aged at least 18 years who developed OHCA-P between 1 January 2016 and 31 December 2018, and were enrolled in the Daegu Emergency Medical Services registry, South Korea. We performed multivariable logistic regression analyses to identify the associations between QRS characteristics and OHCA-P outcomes, in which QRS complexes were considered separately (model 1) and simultaneously (model 2). The primary outcome was survival to hospital discharge and the secondary outcome was a favourable neurological outcome.ResultsOf the 3659 patients with OHCA, 576 were enrolled (median age 73 years; 334 men). A higher QRS amplitude was associated with survival to hospital discharge and a favourable neurological outcome in model 1 (adjusted OR (aOR) 1.077 and 1.106, respectively; 95% CI 1.021 to 0.136 and 1.029 to 1.190, respectively) and model 2 (aOR 1.084 and 1.123, respectively; 95% CI 1.026 to 1.145 and 1.036 to 1.216, respectively). A QRS width of <120 ms was associated with survival to hospital discharge and a favourable neurological outcome in model 1 (aOR 3.371 and 4.634, respectively; 95% CI 1.633 to 6.960 and 1.562 to 13.144, respectively) and model 2 (aOR 3.213 and 5.103, respectively; 95% CI 1.568 to 6.584 and 1.682 to 15.482, respectively). Survival to hospital discharge and neurological outcome were not associated with QRS frequency.ConclusionOHCA-P outcomes were better when the initial QRS complex showed a higher amplitude or narrower width.

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