Resuscitation
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Meta Analysis
The impact of COVID-19 pandemic on out-of-hospital cardiac arrest: an individual patient data meta-analysis.
Prior studies have reported increased out-of-hospital cardiac arrests (OHCA) incidence and lower survival during the COVID-19 pandemic. We evaluated how the COVID-19 pandemic affected OHCA incidence, bystander CPR rate and patients' outcomes, accounting for regional COVID-19 incidence and OHCA characteristics. ⋯ During the first COVID-19 pandemic wave, there was higher OHCA incidence and lower bystander CPR rate in regions with a high-burden of COVID-19. COVID-19 was also associated with a change in patient characteristics and lower survival independently of COVID-19 incidence in the region where OHCA occurred.
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Multicenter Study Observational Study
Accredited Cardiac Arrest Centers Facilitate eCPR and Improve Neurological Outcome.
Out-of-hospital cardiac arrest (OHCA) remains a frequent medical emergency with low survival rates even after a return of spontaneous circulation (ROSC). Growing evidence supports formation of dedicated teams in scenarios like cardiogenic shock to improve prognosis. Thus, the European Resuscitation Council (ERC) recommended introduction of Cardiac Arrest Centers (CAC) in their 2015 guidelines. Here, we aimed to elucidate the effects of newly introduced CACs in Germany regarding survival rate and neurological outcome. ⋯ CAC accreditation is linked to higher rates of favorable neurological outcome and unchanged overall survival.
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The primary aim of this systematic review was to investigate the most common electroencephalogram (EEG)-based machine learning (ML) model with the highest Area Under Receiver Operating Characteristic Curve (AUC) in two ML categories, conventional ML and Deep Neural Network (DNN), to predict the neurologic outcomes after cardiac arrest; the secondary aim was to investigate common EEG features applied to ML models. ⋯ RF and CNN were the two most common ML models with the highest AUCs for predicting the neurologic outcomes after cardiac arrest. Using a multimodal model that combines EEG features and electronic health record data may further improve prognostic performance.