Resuscitation
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Observational Study
Drones can be used to provide dispatch centres with on-site photos before arrival of EMS in time critical incidents.
Drones are able to deliver automated external defibrillators in cases of out-of-hospital cardiac arrest (OHCA) but can be deployed for other purposes. Our aim was to evaluate the feasibility of sending live photos to dispatch centres before arrival of other units during time-critical incidents. ⋯ In a newly implemented drone dispatch service, drones were dispatched to 13% of relevant EMS calls. When drones were dispatched, they arrived at scene earlier than EMS services in 90% of cases. Drones were able to relay photos to the dispatch centre in all cases. Although severely affected by closed airspace and weather conditions, this novel method may facilitate additional decision-making information during time-critical incidents.
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Hypoxic ischemic brain injury (HIBI) induced by cardiac arrest (CA) seems to predominate in cortical areas and to a lesser extent in the brainstem. These regions play key roles in modulating the activity of the autonomic nervous system (ANS), that can be assessed through analyses of heart rate variability (HRV). The objective was to evaluate the prognostic value of various HRV parameters to predict neurological outcome after CA. ⋯ In comatose patients after CA, some HRV markers appear to be associated with unfavorable outcome, EEG severity and PLR abolition, although the sensitivity of these HRV markers remains limited.
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The incidence of sudden cardiac arrest (SCA) during acute coronary syndrome is somewhat unclear, since often subjects dying before the first healthcare contact are not included in the estimates. We aimed to investigate the complete incidence of SCA during ACS. ⋯ The inclusion of ACS-SCA subjects dying before the first emergency medical service (EMS) contact results in a higher and likely more accurate estimation of SCA during ACS. The incidence of SCA was higher among subjects without prior CAD diagnosis. The high mortality rate highlights the importance of early ACS detection to reduce the burden of CAD-related premature deaths.
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This study aimed to predict blood pressure during CPR using chest compression waveform information obtained from a CPR feedback device. ⋯ Blood pressure generated by chest compressions can be predicted with high accuracy by a machine learning method using chest compression waveform information obtained from a CPR feedback device and the patient's demographic characteristics. Real-time provision of the predicted blood pressure can be used to monitor the quality and efficacy of CPR.
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Out-of-hospital cardiac arrest (OHCA) is a critical condition with low survival rates. In patients with a return of spontaneous circulation, brain injury is a leading cause of death. In this study, we propose an interpretable machine learning approach for predicting neurologic outcome after OHCA, using information available at the time of hospital admission. ⋯ The XGBoost machine learning model with 10 features available at the time of hospital admission showed good performance for predicting neurologic outcome after OHCA, with no apparent signs of overfitting.