Resuscitation
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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.
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We evaluated whether an artificial intelligence (AI)-driven robot cardiopulmonary resuscitation (CPR) could improve hemodynamic parameters and clinical outcomes. ⋯ This study provides proof of concept that an AI-driven CPR robot in porcine cardiac arrest is feasible. Compared to a LUCAS 3, an AI-driven CPR robot produced comparable hemodynamic and clinical outcomes.
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Editorial Letter
Neuroprognostication after cardiac arrest: Don't forget the good!