scholarly journals 8 - ECG characteristics of Pulseless Electrical Activity associated with Return of Spontaneous Circulation in Out-of-Hospital Cardiac Arrest

Author(s):  
Andoni Elola Artano ◽  
Elisabete Aramendi
Entropy ◽  
2021 ◽  
Vol 23 (7) ◽  
pp. 847
Author(s):  
Jon Urteaga ◽  
Elisabete Aramendi ◽  
Andoni Elola ◽  
Unai Irusta ◽  
Ahamed Idris

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.


Circulation ◽  
2021 ◽  
Vol 144 (Suppl_2) ◽  
Author(s):  
Eirik Unneland ◽  
Anders Norvik ◽  
Shaun McGovern ◽  
David Buckler ◽  
Unai Irusta ◽  
...  

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.


PLoS ONE ◽  
2017 ◽  
Vol 12 (4) ◽  
pp. e0175257 ◽  
Author(s):  
Hiroyuki Koami ◽  
Yuichiro Sakamoto ◽  
Ryota Sakurai ◽  
Miho Ohta ◽  
Hisashi Imahase ◽  
...  

Circulation ◽  
2018 ◽  
Vol 138 (Suppl_2) ◽  
Author(s):  
Gunnar W Skjeflo ◽  
Eirik Skogvoll ◽  
Jan Pål Loennechen ◽  
Theresa M Olasveengen ◽  
Lars Wik ◽  
...  

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.


Circulation ◽  
2008 ◽  
Vol 118 (suppl_18) ◽  
Author(s):  
Jocelyn Berdowski ◽  
Andra Schmohl ◽  
Rudolph W Koster

Objective- In November 2005, updated resuscitation guidelines were introduced world-wide, and will be revised again in 2010. This study aims to determine how long it takes to implement new guidelines. Methods- This was a prospective observational study. From July 2005 to January 2008, we included all patients with a non traumatic out-of-hospital cardiac arrest. Ambulance paramedics sent all continuous ECG registrations with impedance signal by modem. We excluded ECGs from patients with Return Of Spontaneous Circulation at arrival, incomplete ECG registrations, ECGs with technical deficits or with continuous chest compressions. The same guidelines needed to be used in over 75% of the registration time in order to be labeled. We classified ECGs as guidelines 2000 if the c:v ratio was 15:2, shock blocks were present and there was rhythm analysis after each shock; guidelines 2005 if the c:v ratio was 30:2, a single shock protocol was used and chest compressions was immediately resumed after shock or rhythm analysis in a no shock scenario. We accepted 10% deviations in the amount of compressions (13–17 for 2000 guidelines, 27–33 for 2005). Results- Of the 1703 analyzable ECGs, we classified 827 (48.6%) as guidelines 2000 and 624 (36.6%) as guidelines 2005. In the remaining 252 ECGs (14.8%) 31 used guidelines 1992, 137 applied guidelines 2000 with c:v ratio of 30:2 and 84 did not show distinguishable guideline usage. Since the introduction in November 2005, it took 17 months to apply new guidelines in over 80% of the cases (figure 1 ). Conclusion- Guideline changes are slowly implemented by professionals. This needs to be taken in consideration when new guideline revisions are considered.


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