Wolf Creek XVII Part 6: Physiology-Guided CPR.

Introduction: Physiology-guided cardiopulmonary resuscitation (CPR) offers the potential to optimize resuscitation and enable early prognosis.

Methods: Physiology-Guided CPR was one of six focus topic for the Wolf Creek XVII Conference held on June 14-17, 2023 in Ann Arbor, Michigan, USA. International thought leaders and scientists in the field of cardiac arrest resuscitation from academia and industry were invited.

Amplitude spectral area of ventricular fibrillation can discriminate survival of patients with out-of-hospital cardiac arrest.

Background: Evidence of the association between AMplitude Spectral Area (AMSA) of ventricular fibrillation and outcome after out-of-hospital cardiac arrest (OHCA) is limited to short-term follow-up. In this study, we assess whether AMSA can stratify the risk of death or poor neurological outcome at 30 days and 1 year after OHCA in patients with an initial shockable rhythm or with an initial non-shockable rhythm converted to a shockable one.

Methods: This is a multicentre retrospective study of prospectively collected data in two European Utstein-based OHCA registries.

Cardiac output estimation using ballistocardiography: a feasibility study in healthy subjects.

There is no reliable automated non-invasive solution for monitoring circulation and guiding treatment in prehospital emergency medicine. Cardiac output (CO) monitoring might provide a solution, but CO monitors are not feasible/practical in the prehospital setting. Non-invasive ballistocardiography (BCG) measures heart contractility and tracks CO changes.

Lung tissue injury and hemodynamic effects of ventilations synchronized or unsynchronized to continuous chest compressions in a porcine cardiac arrest model.

Aim: Compare lung injury and hemodynamic effects in synchronized ventilations (between two chest compressions) vs. unsynchronized ventilations during cardiopulmonary resuscitation (CPR) in a porcine model of cardiac arrest.

Methods: Twenty pigs were randomized to either synchronized or unsynchronized group.

Amplitude spectral area of ventricular fibrillation and defibrillation success at low energy in out-of-hospital cardiac arrest.

The optimal energy for defibrillation has not yet been identified and very often the maximum energy is delivered. We sought to assess whether amplitude spectral area (AMSA) of ventricular fibrillation (VF) could predict low energy level defibrillation success in out-of-hospital cardiac arrest (OHCA) patients. This is a multicentre international study based on retrospective analysis of prospectively collected data.

aMplitude spectral area of ventricular fibrillation and amiOdarone Study in patients with out-of-hospital cArdIaC arrest. The MOSAIC study.

Objective: Antiarrhythmic drugs are recommended for out of hospital cardiac arrest (OHCA) with shock-refractory ventricular fibrillation (VF). Amplitude Spectral Area (AMSA) of VF is a quantitative waveform measure that describes the amplitude-weighted mean frequency of VF, it correlates with intramyocardial adenosine triphosphate (ATP) concentration, it is a predictor of shock efficacy and an emerging indicator to guide defibrillation and resuscitation efforts. How AMSA might be influenced by amiodarone administration is unknown.

Amplitude Spectrum Area of ventricular fibrillation to guide defibrillation: a small open-label, pseudo-randomized controlled multicenter trial.

Background: Ventricular fibrillation (VF) waveform analysis has been proposed as a potential non-invasive guide to optimize timing of defibrillation.

Methods: The AMplitude Spectrum Area (AMSA) trial is an open-label, multicenter randomized controlled study reporting the first in-human use of AMSA analysis in out-of-hospital cardiac arrest (OHCA). The primary efficacy endpoint was the termination of VF for an AMSA ≥ 15.

Injuries associated with mechanical chest compressions and active decompressions after out-of-hospital cardiac arrest: A subgroup analysis of non-survivors from a randomized study.

Background: Both skeletal and visceral injuries are reported after cardiopulmonary resuscitation (CPR). This subgroup analysis of a randomized clinical study describes/compares autopsy documented injury patterns caused by two mechanical, piston-based chest compression devices: standard LUCAS® 2 (control) and LUCAS® 2 with active decompression (AD, intervention) in non-survivors with out-of-hospital cardiac arrest (CA).

Method: We compared injuries documented by autopsies (medical/forensic) after control and intervention CPR based on written relatives consent to use patients' data.

Efficacy of defibrillator pads placement during ventricular arrhythmias, a before and after analysis.

Background: European resuscitation guidelines describe several acceptable placements of defibrillator pads during resuscitation of cardiac arrest. However, no clinical trial has compared defibrillation efficacy between any of the different pad placements. Houston Fire Department emergency medical system (EMS) used anterior-posterior (AP) defibrillator pad placement before becoming a study site in the circulation improving resuscitation care trial (CIRC).

Physiological effects of providing supplemental air for avalanche victims. A randomised trial.

Background: Survival from avalanche burial is dependent on time to extraction, breathing ability, air pocket oxygen content, and avoiding rebreathing of carbon dioxide (CO). Mortality from asphyxia increases rapidly after burial. Rescue services often arrive too late.

Proximity Extension Assay in Combination with Next-Generation Sequencing for High-throughput Proteome-wide Analysis.

Understanding the dynamics of the human proteome is crucial for developing biomarkers to be used as measurable indicators for disease severity and progression, patient stratification, and drug development. The Proximity Extension Assay (PEA) is a technology that translates protein information into actionable knowledge by linking protein-specific antibodies to DNA-encoded tags. In this report we demonstrate how we have combined the unique PEA technology with an innovative and automated sample preparation and high-throughput sequencing readout enabling parallel measurement of nearly 1500 proteins in 96 samples generating close to 150,000 data points per run.

Mechanical active compression-decompression versus standard mechanical cardiopulmonary resuscitation: A randomised haemodynamic out-of-hospital cardiac arrest study.

Background: Active compression-decompression cardiopulmonary resuscitation (ACD-CPR) utilises a suction cup to lift the chest-wall actively during the decompression phase (AD). We hypothesised that mechanical ACD-CPR (Intervention), with AD up to 30 mm above the sternal resting position, would generate better haemodynamic results than standard mechanical CPR (Control).

Methods: This out-of-hospital adult non-traumatic cardiac arrest trial was prospective, block-randomised and non-blinded.

Tidal volume measurements via transthoracic impedance waveform characteristics: The effect of age, body mass index and gender. A single centre interventional study.

Background And Aim: Measuring tidal volumes (TV) during bag-valve ventilation is challenging in the clinical setting. The ventilation waveform amplitude of the transthoracic impedance (TTI-amplitude) correlates well with TV for an individual, but poorer between patients. We hypothesized that TV to TTI-amplitude relations could be improved when adjusted for morphometric variables like body mass index (BMI), gender or age, and that TTI-amplitude cut-offs for ventilations with adequate TV (>400ml) could be established.

Shock decision algorithm for use during load distributing band cardiopulmonary resuscitation.

Aim: Chest compressions delivered by a load distributing band (LDB) induce artefacts in the electrocardiogram. These artefacts alter shock decisions in defibrillators. The aim of this study was to demonstrate the first reliable shock decision algorithm during LDB compressions.

Machine learning for cell classification and neighborhood analysis in glioma tissue.

Multiplexed and spatially resolved single-cell analyses that intend to study tissue heterogeneity and cell organization invariably face as a first step the challenge of cell classification. Accuracy and reproducibility are important for the downstream process of counting cells, quantifying cell-cell interactions, and extracting information on disease-specific localized cell niches. Novel staining techniques make it possible to visualize and quantify large numbers of cell-specific molecular markers in parallel.

Rhythm Analysis during Cardiopulmonary Resuscitation Using Convolutional Neural Networks.

Chest compressions during cardiopulmonary resuscitation (CPR) induce artifacts in the ECG that may provoque inaccurate rhythm classification by the algorithm of the defibrillator. The objective of this study was to design an algorithm to produce reliable shock/no-shock decisions during CPR using convolutional neural networks (CNN). A total of 3319 ECG segments of 9 s extracted during chest compressions were used, whereof 586 were shockable and 2733 nonshockable.

Multimodal Algorithms for the Classification of Circulation States During Out-of-Hospital Cardiac Arrest.

Goal: Identifying the circulation state during out-of-hospital cardiac arrest (OHCA) is essential to determine what life-saving therapies to apply. Currently algorithms discriminate circulation (pulsed rhythms, PR) from no circulation (pulseless electrical activity, PEA), but PEA can be classified into true (TPEA) and pseudo (PPEA) depending on cardiac contractility. This study introduces multi-class algorithms to automatically determine circulation states during OHCA using the signals available in defibrillators.

A Robust Machine Learning Architecture for a Reliable ECG Rhythm Analysis during CPR.

Chest compressions delivered during cardiopulmonary resuscitation (CPR) induce artifacts in the ECG that may make the shock advice algorithms (SAA) of defibrillators inaccurate. There is evidence that methods consisting of adaptive filters that remove the CPR artifact followed by machine learning (ML) based algorithms are able to make reliable shock/no-shock decisions during compressions. However, there is room for improvement in the performance of these methods.

Diagnostic performance of the basic and advanced life support termination of resuscitation rules: A systematic review and diagnostic meta-analysis.

Aim: To minimize termination of resuscitation (TOR) in potential survivors, the desired positive predictive value (PPV) for mortality and specificity of universal TOR-rules are ≥99%. In lack of a quantitative summary of the collective evidence, we performed a diagnostic meta-analysis to provide an overall estimate of the performance of the basic and advanced life support (BLS and ALS) termination rules.

Data Sources: We searched PubMed/EMBASE/Web-of-Science/CINAHL and Cochrane (until September 2019) for studies on either or both TOR-rules in non-traumatic, adult cardiac arrest.

Profiling surface proteins on individual exosomes using a proximity barcoding assay.

Exosomes have been implicated in numerous biological processes, and they may serve as important disease markers. Surface proteins on exosomes carry information about their tissues of origin. Because of the heterogeneity of exosomes it is desirable to investigate them individually, but this has so far remained impractical.

Transthoracic Impedance Measured with Defibrillator Pads-New Interpretations of Signal Change Induced by Ventilations.

Compressions during the insufflation phase of ventilations may cause severe pulmonary injury during cardiopulmonary resuscitation (CPR). Transthoracic impedance (TTI) could be used to evaluate how chest compressions are aligned with ventilations if the insufflation phase could be identified in the TTI waveform without chest compression artifacts. Therefore, the aim of this study was to determine whether and how the insufflation phase could be precisely identified during TTI.

Mixed convolutional and long short-term memory network for the detection of lethal ventricular arrhythmia.

Early defibrillation by an automated external defibrillator (AED) is key for the survival of out-of-hospital cardiac arrest (OHCA) patients. ECG feature extraction and machine learning have been successfully used to detect ventricular fibrillation (VF) in AED shock decision algorithms. Recently, deep learning architectures based on 1D Convolutional Neural Networks (CNN) have been proposed for this task.