Resuscitation
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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.
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To determine whether out-of-hospital cardiac arrest (OHCA) post-resuscitation management and outcomes differ between four Detroit hospitals. ⋯ Differing rates of DNR and coronary angiography was associated with observed disparities in favorable neurologic outcome, but not death, between four Detroit hospitals.