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

Abstract

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.

Artificial intelligence to support out-of-hospital cardiac arrest care: A scoping review

“…The majority were carried out through collaborations between multinational groups in Europe and the United States utilizing ECG waveform data from multicenter databases. Among these studies, six (25%) aimed to predict defibrillation outcomes in ventricular fibrillation (VF) 30 , 31 , 32 , 33 , 34 , 35 , five (21%) focused on rhythm classification 36 , 37 , 38 , 39 , 40 , five (21%) developed algorithms to advise defibrillation versus no defibrillation 41 , 42 , 43 , 44 , 45 , four (17%) focused specific on the classification of pulseless electrical activity (PEA) (i.e., PEA vs pseudo-PEA vs pulsed rhythm, or favorable-PEA vs non-favorable-PEA) 46 , 47 , 48 , 49 , two (8%) aimed to predict survival outcomes 30 , 50 and two (8%) aimed to suppress CPR artifact to improve ECG segment analysis 36 , 37 . Additional studies aimed to develop ECG based classification algorithms to predict rearrest in the immediate post-ROSC period 51 , predict the presence of a pulse during CPR 52 , and predict myocardial infarction/acute coronary artery occlusion during CPR 53…”

“…Among the studies of ECG segment analysis, fourteen used ML 35 , 36 , 30 , 31 , 32 , 38 , 39 , 40 , 47 , 48 , 49 , 50 , 51 , 52 , nine used DL 33 , 34 , 37 , 38 , 41 , 42 , 43 , 44 , 45 , 46 , and one used both ML and DL 38 . All studies utilized ECG waveform characteristics (i.e., VF amplitude/frequency, QRS size/amplitude) obtained from manual defibrillators, automated external defibrillators, and/or Holter monitors.…”

Artificial Intelligence and Machine Learning in Prehospital Emergency Care: A Systematic Scoping Review

“…The minority of studies utilised multimodal inputs (n=37, 34•9%), with few models using text (n=8), audio (n=5), images (n=1), or videos (n=0) as inputs. Seven studies used inputs that did not fall into one of our predefined categories; these inputs included capnography (38,107), thoracic impedance (19,38,100,101,117), and accelerometer-based chest compression depth data (98).…”

Evaluation of different machine learning algorithms for predicting the length of stay in the emergency departments: a single-centre study

“…As a result, several methodologies have been proposed in the literature to study the factors that influence LOS in healthcare processes. Among them, regression models and artificial intelligence techniques have been widely applied with satisfactory performance to predict the LOS ( 13 – 19 ) and to address healthcare-related problems, such as elaboration and analysis of biomedical data and signals ( 20 – 26 ), development of clinical decision-making support systems ( 27 , 28 ), and quality assessment of medicine services. In fact, LOS has been already employed as a target output in healthcare, and other studies have recently aimed at predicting it in different fields ( 29 , 30 ).…”