Resuscitation
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Out-of-hospital cardiac arrest (OHCA) is associated with high mortality. Current methods for predicting mortality post-arrest require data unavailable at the time of initial medical contact. We created and validated a risk prediction model for patients experiencing OHCA who achieved return of spontaneous circulation (ROSC) which relies only on objective information routinely obtained at first medical contact. ⋯ CASS accurately predicts mortality in OHCA patients. The model uses only binary, objective clinical data routinely obtained at first medical contact. Early risk stratification may allow identification of more patients in whom timely and aggressive invasive management may improve outcomes.
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Use of smart devices to provide real-time cardiopulmonary resuscitation (CPR) feedback in the context of out-of-hospital cardiac arrest (OHCA) has considerable potential for improving survival. However, the findings of previous studies evaluating the effectiveness of these devices have been conflicting. Therefore, we conducted a systematic review of the literature to assess the utility of smart devices for improving the quality of CPR during CPR training. ⋯ This review does not find durable evidence for usefulness of smart devices in CPR training. However, the smartwatches may improve the accuracy of chest compression depth. Future studies with larger sample sizes might be necessary before reaching a firm conclusion.
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During resuscitation decisions are made frequently and based on limited information in a stressful environment. ⋯ Human factors contributing to decision-making during resuscitation are identified and can be mitigated by tailored stress training and cognitive aids. Understanding these factors may have implications for clinician education and the development of decision-support tools.