Resuscitation
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Review Meta Analysis
The incidence and outcomes of out-of-hospital cardiac arrest in metropolitan versus rural locations: A systematic review and meta-analysis.
Rurality poses a unique challenge to the management of out-of-hospital cardiac arrest (OHCA) when compared to metropolitan (metro) locations. We conducted a systematic review of published literature to understand how OHCA incidence, management and survival outcomes vary between metro and rural areas. ⋯ Overall, while incidence did not vary, the odds of OHCA survival to hospital discharge were approximately 50% lower in rural areas compared to metro areas. This suggests an opportunity for improvement in the prehospital management of OHCA within rural locations. This review also highlighted major challenges in standardising the definition of rurality in the context of cardiac arrest research.
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Editorial Comment Meta Analysis
The urban-rural divide in cardiac arrest survival.
This paper provides a commentary on the recently published "The incidence and outcomes of out-of-hospital cardiac arrest in metropolitan versus rural locations: A systematic review and meta-analysis". The importance of this work due to the systematic search for the evidence and relative consistency of studies in terms of the direction of effect is highlighted. The commentary includes discussion on the variability between studies and the urban-rural differences in clinical care. Opportunities for future research are described, as well as the need to adequately characterise the local conditions and community engagement so that the applicability of research findings can be determined for local contexts.
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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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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.