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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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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Videolaryngoscopy (VL) is a promising tool to provide a safe airway during cardiopulmonary resuscitation (CPR) and to ensure early reoxygenation. Using data from the German Resuscitation Registry, we investigated the outcome of non-traumatic out-of-hospital cardiac arrest (OHCA) patients treated with VL versus direct laryngoscopy (DL) for airway management. ⋯ VL for endotracheal intubation (ETI) at OHCA was associated with better neurological outcome in patients with ROSC. Therefore, the use of VL for OHCA offers a promising perspective. Further prospective studies are required.
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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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Cardiac arrest is a leading cause of mortality prior to discharge for children admitted to the pediatric intensive care unit. To address this problem, we used machine learning to predict cardiac arrest up to three hours in advance. ⋯ We have created high-performing models that identify signatures of in-hospital cardiac arrest (IHCA) that may not be evident to clinicians. These signatures include a combination of heart rate variability metrics, vital signs data, and therapeutic drug classes. These machine learning models can predict IHCA up to three hours prior to onset with high performance, allowing clinicians to intervene earlier, improving patient outcomes.