Resuscitation
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A machine-learning model trained to recognize emergency calls regarding Out-of-Hospital Cardiac Arrest (OHCA) was tested in clinical practice at Copenhagen Emergency Medical Services (EMS) from September 2018 to December 2019. We aimed to investigate emergency call characteristics where the machine-learning model failed to recognize OHCA or misinterpreted a call as being OHCA. ⋯ Continuous optimization of the language model is needed to improve the prediction of OHCA and thereby improve sensitivity and specificity of the machine-learning model on recognising OHCA in emergency telephone calls.
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Observational Study
Annual improvement trends in resuscitation outcome of patients defibrillated by laypersons after out-of-hospital cardiac arrests and compression-only resuscitation of laypersons.
We aimed to investigate the effect of compression-only cardiopulmonary resuscitation (CPR) with conventional CPR in patients who were defibrillated by laypersons. ⋯ In Japan, the outcomes of out-of-hospital cardiac arrest patients who were defibrillated by laypersons were considerably better in compression-only resuscitation of laypersons every year.
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Out-of-hospital cardiac arrest (OHCA) is a serious threat to human life and health, characterized by high morbidity and mortality. However, given the limitations of the current emergency medical system (EMS), it is difficult to immediately treat patients who experience OHCA. It is well known that rapid defibrillation after cardiac arrest is essential for improving the survival rate of OHCA, yet automated external defibrillators (AED) are difficult to obtain in a timely manner. ⋯ Drones are promising and innovative tools. Many studies have demonstrated that AED delivery by drones is feasible and cost-effective; however, as a new strategy to improve the survival rate of OHCA patients, there remain problems to be solved. In the future, more in-depth investigations need to be conducted.