Resuscitation
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The Abdominal Aortic and Junctional Tourniquet (AAJT) increased systemic vascular resistance, mean arterial pressure, carotid blood flow and rate of return of spontaneous circulation (ROSC) in animals with hypovolaemic traumatic cardiac arrest (TCA). The objective of this study was to report the first experience of the use of the AAJT as part of a pre-hospital TCA algorithm. ⋯ Physiological changes were demonstrated but there were no survivors. Further research focusing on faster application times may be associated with improved outcomes.
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Review Meta Analysis
Interfaces for non-invasive neonatal resuscitation in the delivery room: A systematic review and meta-analysis.
To perform a systematic review of trials comparing interfaces for delivering non-invasive PPV to a newborn in the delivery room (DR). ⋯ PROSPERO CRD42020151870.
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Review
Acute clinical deterioration and consumer escalation in the hospital setting: A literature review.
Consumer escalation systems that allow patients and/or their family/carers to escalate concerns about clinical deterioration have been proposed as a way of enhancing patient safety. However, evidence to guide implementation or to support system effectiveness remains unclear. ⋯ The ability of consumer escalation processes to achieve their underlying goals is still to be adequately assessed. Further research is required to inform how to best implement, support and optimise consumer escalation systems.
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Out-of-hospital cardiac arrest (OHCA) during COVID-19 has been reported by countries with high case numbers and overwhelmed healthcare services. Imposed restrictions and treatment precautions may have also influenced OHCA processes-of-care. We investigated the impact of the COVID-19 pandemic period on incidence, characteristics, and survival from OHCA in Victoria, Australia. ⋯ The COVID-19 pandemic period did not influence OHCA incidence but appears to have disrupted the system-of-care in Australia. However, this could not completely explain reductions in survival.
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Submersion time is a strong predictor for death in drowning, already 10 min after submersion, survival is poor. Traditional search efforts are time-consuming and demand a large number of rescuers and resources. We aim to investigate the feasibility and effectiveness of using drones combined with an online machine learning (ML) model for automated recognition of simulated drowning victims. ⋯ The use of a drone and a ML model was feasible and showed satisfying effectiveness in identifying a submerged static human simulating drowning in open water and favorable environmental conditions. The ML algorithm and methodology should be further optimized, again tested and validated in a real-life clinical study.