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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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.
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Observational Study
Factors associated with the arrival of smartphone-activated First Responders before the Emergency Medical Services in Out-of-Hospital Cardiac Arrest Dispatch.
First responder programs were developed to speed up access to cardiopulmonary resuscitation and defibrillation for out-of-hospital cardiac arrest (OHCA) victims. Little is known about the factors influencing the efficiency of the first responders arriving before the EMS and, therefore, effectively contributing to the chain of survival. ⋯ When dispatched to OHCA scenes, responders already carrying defibrillators could more probably reach the scene before EMS. Special first responder categories are more competitive and should be further investigated.