Pre-hospital advanced airway management by anaesthetist and nurse anaesthetist critical care teams: a prospective observational study of 2028 pre-hospital tracheal intubations.

BACKGROUND: Pre-hospital tracheal intubation success and complication rates vary considerably among provider categories. The purpose of this study was to estimate the success and complication rates of pre-hospital tracheal intubation performed by physician anaesthetist or nurse anaesthetist pre-hospital critical care teams. METHODS: Data were prospectively collected from critical care teams staffed with a physician anaesthetist or a nurse anaesthetist according to the Utstein template for pre-hospital advanced airway management. The patients served by six ambulance helicopters and six rapid response vehicles in Denmark, Finland, Norway, and Sweden from May 2015 to November 2016 were included. RESULTS: The critical care teams attended to 32 007 patients; 2028 (6.3%) required pre-hospital tracheal intubation. The overall success rate of pre-hospital tracheal intubation was 98.7% with a median intubation time of 25 s and an on-scene time of 25 min. The majority (67.0%) of the patients' tracheas were intubated by providers who had performed >2500 tracheal intubations. The success rate of tracheal intubation on the first attempt was 84.5%, and 95.9% of intubations were completed after two attempts. Complications related to pre-hospital tracheal intubation were recorded in 10.9% of the patients. Intubations after rapid sequence induction had a higher success rate compared with intubations without rapid sequence induction (99.4% vs 98.1%; P=0.02). Physicians had a higher tracheal intubation success rate than nurses (99.0% vs 97.6%; P=0.03). CONCLUSIONS: When performed by experienced physician anaesthetists and nurse anaesthetists, pre-hospital tracheal intubation was completed rapidly with high success rates and a low incidence of complications. CLINICAL TRIAL NUMBER: NCT 02450071.

Brain-Derived Microparticles in Patients with Severe Isolated TBI.

PRIMARY OBJECTIVE: to investigate the presence of circulating microparticles (MPs) of brain tissue origin in the systemic and cerebrovenous blood of patients with severe traumatic brain injury (TBI). RESEARCH DESIGN: Prospective observational study in 15 consecutive patients with severe isolated TBI. METHODS AND PROCEDURES: We repeatedly measured concentrations of MPs expressing glial fibrillary acidic protein (GFAP), neuron-specific enolase (NSE) and aquaporin-4 (AQP4), in arterial and cerebrovenous blood at admittance to hospital and up to 72 hours after the injury. MAIN OUTCOMES AND RESULTS: Concentrations of MPs expressing GFAP and AQP4 were significantly higher in the TBI group compared with healthy controls: GFAP 2.0 [1.1-7.9] vs. 1.3 [1-2.1] × 106/mL, p < 0.001; AQP4 0.1 [0.07-0.22] vs. 0.08 [0.06-0.11] × 106/mL, p < 0.001 (median, range). No transcranial gradients were found. Levels of NSE-expressing MPs were also higher in the TBI group compared with healthy controls: 0.4 [0.25-2.1] vs. 0.26 [0.13-0.98] × 106/mL, p < 0.05; however, regarding NSE-positive non-platelet MPs, there were no differences between patients and controls. CONCLUSIONS: Patients with TBI have higher numbers of brain-derived MPs. Further studies are needed, however, to identify specific and sensitive MP markers of brain injury.

Assessment of hospital surge capacity using the MACSIM simulation system: a pilot study.

AIM: The aim of this study was to use a simulation model developed for the scientific evaluation of methodology in disaster medicine to test surge capacity (SC) in a major hospital responding to a simulated major incident with a scenario copied from a real incident. METHODS: The tested hospital was illustrated on a system of magnetic boards, where available resources, staff, and patients treated in the hospital at the time of the test were illustrated. Casualties were illustrated with simulation cards supplying all data required to determine procedures for diagnosis and treatment, which all were connected to real consumption of time and resources. RESULTS: The first capacity-limiting factor was the number of resuscitation teams that could work parallel in the emergency department (ED). This made it necessary to refer severely injured to other hospitals. At this time, surgery (OR) and intensive care (ICU) had considerable remaining capacity. Thus, the reception of casualties could be restarted when the ED had been cleared. The next limiting factor was lack of ventilators in the ICU, which permanently set the limit for SC. At this time, there was still residual OR capacity. With access to more ventilators, the full surgical capacity of the hospital could have been utilized. CONCLUSIONS: The tested model was evaluated as an accurate tool to determine SC. The results illustrate that SC cannot be determined by testing one single function in the hospital, since all functions interact with each other and different functions can be identified as limiting factors at different times during the response.