Resuscitation
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Observational Study
Machine learning can support dispatchers to better and faster recognize out-of-hospital cardiac arrest during emergency calls: A retrospective study.
Fast recognition of out-of-hospital cardiac arrest (OHCA) by dispatchers might increase survival. The aim of this observational study of emergency calls was to (1) examine whether a machine learning framework (ML) can increase the proportion of calls recognizing OHCA within the first minute compared with dispatchers, (2) present the performance of ML with different false positive rate (FPR) settings, (3) examine call characteristics influencing OHCA recognition. ⋯ ML recognized a higher proportion of OHCA within the first minute compared with dispatchers and has the potential to be a supportive tool during emergency calls. The optimal FPR settings need to be evaluated in a prospective study.
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To investigate how the publication of the targeted temperature management (TTM) trial in December 2013 affected the trends in temperature management and outcome following admission to UK intensive care units (ICUs) after out-of-hospital cardiac arrest (OHCA). ⋯ The lowest temperature recorded in the first-24 h of admission in OHCA patients was higher in the post-TTM cohort compared with the pre-TTM cohort. There has been an increase in the proportion of patients with fever (>38 °C) in the first 24 h. Although crude mortality was slightly higher in the post-TTM cohort, an analysis accounting for time trend and variation between critical care units, found no significant change associated with the TTM publication.
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In this study, we examine the impact of a trauma-focused resuscitation protocol on survival outcomes following adult traumatic out-of-hospital cardiac arrest (OHCA). ⋯ Despite an increase in trauma-based interventions and a reduction in the time to their administration, our study did not find a survival benefit from a trauma-focused resuscitation protocol over initial conventional CPR. However, survival was low with both approaches.