Resuscitation
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Multicenter Study
Text message alert system and resuscitation outcomes after out-of-hospital cardiac arrest: A before-and-after population-based study.
This study aimed to investigate the association of a resuscitation bundle intervention including text message (TM) alert system and bystander cardiopulmonary resuscitation (CPR) and outcomes of out-of-hospital cardiac arrest (OHCA). ⋯ The bundle intervention including TM alert service for OHCA was associated with better survival outcomes through an increase in bystander CPR. Clinical trials registration; NCT02010151.
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Multicenter Study
Electromyographic reactivity measured with scalp-EEG contributes to prognostication after cardiac arrest.
To assess whether stimulus-induced modifications of electromyographic activity observed on scalp EEG have a prognostic value in comatose patients after cardiac arrest. ⋯ Taking EMG into account when assessing reactivity of EEG seems to reduce false negative predictions for identifying patients with favorable outcome after cardiac arrest.
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Multicenter Study
Patient and hospital factors predict use of coronary angiography in out-of-hospital cardiac arrest patients.
To describe the association between patient- and hospital-level factors and coronary angiography among patients who suffer out-of-hospital cardiac arrest (OHCA). ⋯ We identified patient- and hospital-level factors that explain some of the variability in the use of coronary angiography for OHCA. Future work should determine which post arrest patients will benefit most from urgent coronary angiography and evaluate knowledge translation strategies to ensure consistent delivery of best practices.
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Emergency medical dispatchers fail to identify approximately 25% of cases of out of hospital cardiac arrest, thus lose the opportunity to provide the caller instructions in cardiopulmonary resuscitation. We examined whether a machine learning framework could recognize out-of-hospital cardiac arrest from audio files of calls to the emergency medical dispatch center. ⋯ A machine learning framework performed better than emergency medical dispatchers for identifying out-of-hospital cardiac arrest in emergency phone calls. Machine learning may play an important role as a decision support tool for emergency medical dispatchers.
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Despite a consistent association with improved outcomes, public automated external defibrillators (AEDs) are rarely used in out-of-hospital cardiac arrest. One of the barriers towards increased use might be cost-effectiveness. ⋯ Public AEDs are a cost-effective public health intervention in the United States. These findings support widespread dissemination of public AEDs.