Resuscitation
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Efficient ventilation is important during cardiopulmonary resuscitation (CPR). Nevertheless, there is insufficient knowledge on how the patient's position affects ventilatory parameters during mechanically assisted CPR. We studied ventilatory parameters at different positive end-expiratory pressure (PEEP) levels and when using an inspiratory impedance valve (ITD) during horizontal and head-up CPR (HUP-CPR). ⋯ When using mechanical ventilation during CPR, it seems that the PEEP level and patient position are important determinants of respiratory parameters. Moreover, tidal volume seems to be lower when the thorax is positioned at 35°.
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The Neonatal Life Support 2020 guidelines emphasize that meconium-stained amniotic fluid (MSAF) remains a significant risk factor for a newborn to receive advanced resuscitation, especially if additional risk factors are present at the time of birth. However, these additional perinatal risk factors are not clearly identified. The purpose of this study was to evaluate the importance of additional independent ante- and intrapartum risk factors in the era of no routine endotracheal suctioning that determine the need for resuscitation in newborns born through MSAF. ⋯ Risk stratification of perinatal factors associated with the need for newborn resuscitation and advanced resuscitation in the deliveries associated with MSAF may help neonatal teams and resources to be appropriately prioritized and optimally utilized.
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The time-dependent prognostic role of bystander cardiopulmonary resuscitation (CPR) for out-of-hospital cardiac arrest (OHCA) patients has not been described with great precision, especially for neurologic outcomes. Our objective was to assess the association between bystander CPR, emergency medical service (EMS) response time, and OHCA patients' outcomes. ⋯ Although bystander CPR is associated with an immediate increase in odds of survival and of good neurologic outcome for OHCA patients, it does not influence the negative association between longer EMS response time and survival and good neurologic outcome.
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To evaluate the existing knowledge on the effectiveness of machine learning (ML) algorithms inpredicting defibrillation success during in- and out-of-hospital cardiac arrest. ⋯ Machine learning algorithms, specifically Neural Networks, have been shown to have potential to predict defibrillation success for cardiac arrest with high sensitivity and specificity.Due to heterogeneity, inconsistent reporting, and high risk of bias, it is difficult to conclude which, if any, algorithm is optimal. Further clinical studies with standardized reporting of patient characteristics, outcomes, and appropriate algorithm validation are still required to elucidate this. PROSPERO 2020 CRD42020148912.
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Out-of-hospital cardiac arrest (OHCA) in pediatric patients is associated with high rates of mortality and neurologic injury, with no definitive evidence-based method to predict outcomes available. A prognostic scoring tool for adults, The Brain Death After Cardiac Arrest (BDCA) score, was recently developed and validated. We aimed to validate this score in pediatric patients. ⋯ The BDCA score shows promise in children ≥ 12mo following OHCA and may be considered in conjunction with existing multimodal prognostication approaches.