Pulseless Electrical Activity: Detection of Underlying Causes in a Prehospital Setting

Outcome, compliance with inclusion criteria and cost of extracorporeal cardiopulmonary resuscitation (ECPR) in out-of-hospital cardiac arrest: A retrospective cohort study

Introduction

The primary aim was to describe the outcome, the compliance with inclusion criteria and the characteristics of patients who underwent extracorporeal cardiopulmonary resuscitation (ECPR) for out-of-hospital cardiac arrest (OHCA). The secondary aim was to calculate the cost of ECPR for the patients and the public Belgian healthcare system.

Methods

Single-centre retrospective cohort study in Antwerp University Hospital. We included all patients who underwent ECPR for OHCA from 2018 to 2020. Medical records were assessed to determine the clinical outcome and invoices were assessed to calculate the charged fees. We collected all relevant cost components at the most detailed level (micro costing technique).

Results

Sixty-five patients who received ECPR for OHCA were included. Thirty-eight patients (58%) died within one week after ECPR initiation. After one year, twelve patients (18.5%) were still alive of which ten (15.4%) had a good neurological outcome (Cerebral Performance Category (CPC) 1 or 2). Forty-nine patients (75.4%) met the ECPR inclusion criteria. A total of 2,552,498.34 euro was charged. The patients and the public Belgian healthcare system contributed to a 255,250 euro cost for each survivor after one year with good neurological outcome.

Conclusion

Our analysis highlights the complex interplay between clinical efficacy and financial implications in the utilization of ECPR. While ECPR demonstrates potential in improving survival rates and neurological outcomes among cardiac arrest patients, its adoption presents substantial economic challenges. Inappropriate patient selection may lead to significant increases in resource utilisation without improved outcome.

Paramedic-Performed Carotid Artery Ultrasound Heralds Return of Spontaneous Circulation in Out-of-Hospital Cardiac Arrest: A Case Report

Point-of-Care Ultrasound (POCUS) has been demonstrated to have multiple applications in the care of critically ill and injured patients, especially given its portability and ease of use. These characteristics of POCUS make it ideal for use in the prehospital environment as well. We present a case that highlights a novel application of ultrasound in the prehospital management of out-of-hospital cardiac arrest (OHCA).

Rat model of asphyxia-induced cardiac arrest and resuscitation

Neurologic injury after cardiopulmonary resuscitation is the main cause of the low survival rate and poor quality of life among patients who have experienced cardiac arrest. In the United States, as the American Heart Association reported, emergency medical services respond to more than 347,000 adults and more than 7,000 children with out-of-hospital cardiac arrest each year. In-hospital cardiac arrest is estimated to occur in 9.7 per 1,000 adult cardiac arrests and 2.7 pediatric events per 1,000 hospitalizations. Yet the pathophysiological mechanisms of this injury remain unclear. Experimental animal models are valuable for exploring the etiologies and mechanisms of diseases and their interventions. In this review, we summarize how to establish a standardized rat model of asphyxia-induced cardiac arrest. There are four key focal areas: (1) selection of animal species; (2) factors to consider during modeling; (3) intervention management after return of spontaneous circulation; and (4) evaluation of neurologic function. The aim was to simplify a complex animal model, toward clarifying cardiac arrest pathophysiological processes. It also aimed to help standardize model establishment, toward facilitating experiment homogenization, convenient interexperimental comparisons, and translation of experimental results to clinical application.

Prehospital Ultrasound: A Narrative Review

Background: Point-of-care ultrasound is rapidly becoming more prevalent in the prehospital environment. Though considered a relatively new intervention in this setting, there is growing literature that aims to explore the use of prehospital ultrasound by EMS personnel.Methods: To better understand and report the state of the science on prehospital ultrasound, we conducted a narrative review of the literature.Results: Following a keyword search of MEDLINE in Ovid from inception to August 2, 2022, 2,564 records were identified and screened. Based on review of abstracts and full texts, with addition of seven articles via bibliography review, 193 records were included. Many included studies detail usage in air medical and other critical care transport environments. Clinicians performing prehospital ultrasound are often physicians or other advanced practice personnel who have previous ultrasound experience, which facilitates implementation in the prehospital setting. Emerging literature details training programs for prehospital personnel who are novices to ultrasound, and implementation for some study types appears feasible without prior experience. Unique use scenarios that show promise include during critical care transport, for triage in austere settings, and for thoracic evaluation of patients at risk of life-threatening pathology.Conclusion: There is a growing mostly observational body of literature describing the use of ultrasound by prehospital personnel. Prehospital ultrasound has demonstrated feasibility for specific conditions, yet interventional studies evaluating benefit to patient outcomes are absent.

Prone restraint cardiac arrest in in-custody and arrest-related deaths

We postulate that most atraumatic deaths during police restraint of subjects in the prone position are due to prone restraint cardiac arrest (PRCA), rather than from restraint asphyxia or a stress-induced cardiac condition, such as excited delirium. The prone position restricts ventilation and diminishes pulmonary perfusion. In the setting of a police encounter, metabolic demand will be high from anxiety, stress, excitement, physical struggle, and/or stimulant drugs, leading to metabolic acidosis and requiring significant hyperventilation. Although oxygen levels may be maintained, prolonged restraint in the prone position may result in an inability to adequately blow off CO₂ , causing blood pCO₂ levels to rise rapidly. The uncompensated metabolic acidosis (low pH) will eventually result in loss of myocyte contractility. The initial electrocardiogram rhythm will generally be either pulseless electrical activity (PEA) or asystole, indicating a noncardiac etiology, more consistent with PRCA and inconsistent with a primary role of any underlying cardiac pathology or stress-induced cardiac etiology. We point to two animal models: in one model rats unable to breathe deeply due to an external restraint die when their metabolic demand is increased, and in the other model, pressure on the chest of rats results in decreased venous return and cardiac arrest rather than death from asphyxia. We present two cases of subjects restrained in the prone position who went into cardiac arrest and had low pHs and initial PEA cardiac rhythms. Our cases demonstrate the danger of prone restraint and serve as examples of PRCA.

Timing and Identification of the Cause and Treatment of a Cardiac Arrest: A Potential Survival Benefit

OBJECTIVE

The aim of this study was to evaluate how mobile medical teams (MMTs) search for the etiology of a cardiac arrest (CA) and to investigate the association between the discovery of etiology and patient outcome.

SUBJECTS AND METHODS

Resuscitations of all adult patients who experienced an in- or out-of-hospital CA between 2016 and 2018 were video recorded. All video recordings were reviewed. The time to start of "cause analysis" and time to treatment by the MMT were analyzed. Also, investigations performed during etiologic evaluation were examined: heteroanamnesis, medical history-taking, clinical examinations, technical investigations, and the use of the 4Hs and 4Ts method.

RESULTS

Of the 139 CA events included in this study, the MMTs performed etiologic evaluation in only 75% of the resuscitations, and in 20% of the evaluations, they did not use the recommended 4Hs and 4Ts method. Medical history-taking and heteroanamnesis were performed in the large majority, but often without clear cause. A presumptive etiology was found in 46.8% of out-of-hospital CAs and 65.2% of in-hospital CAs. A significant association was found between return of spontaneous circulation and the discovery of presumable etiology for out-of-hospital CAs (p < 0.001). The median time to treatment was 492 s (recommended: 130-250 s) for nonshockable rhythms and 422 s (recommended: 270-390 s) for shockable rhythms, up to twice the time advised according to the guidelines.

CONCLUSION

The current approach for etiologic evaluation is not ideal. Further research is needed to establish a more structured and simplified approach.

A Machine Learning Model for the Prognosis of Pulseless Electrical Activity during Out-of-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.