Resuscitation
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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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Observational Study
Calcium Use during Paediatric In-hospital Cardiac Arrest is Associated with Worse Outcomes.
To evaluate associations between calcium administration and outcomes among children with in-hospital cardiac arrest and among specific subgroups in which calcium use is hypothesized to provide clinical benefit. ⋯ Calcium use was common during paediatric in-hospital cardiac arrest and associated with worse outcomes at hospital discharge.
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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.
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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.