title: Risk assessment of pre-hospital trauma airway management by anaesthesiologists using the predictive Bayesian approach.
abstract: BACKGROUND
  Endotracheal intubation (ETI) has been considered an essential part of pre-hospital advanced life support. Pre-hospital ETI, however, is a complex intervention also for airway specialist like anaesthesiologists working as pre-hospital emergency physicians. We therefore wanted to investigate the quality of pre-hospital airway management by anaesthesiologists in severely traumatised patients and identify possible areas for improvement.
  
  
  METHODS
  We performed a risk assessment according to the predictive Bayesian approach, in a typical anaesthesiologist-manned Norwegian helicopter emergency medical service (HEMS). The main focus of the risk assessment was the event where a patient arrives in the emergency department without ETI despite a pre-hospital indication for it.
  
  
  RESULTS
  In the risk assessment, we assigned a high probability (29%) for the event assessed, that a patient arrives without ETI despite a pre-hospital indication. However, several uncertainty factors in the risk assessment were identified related to data quality, indications for use of ETI, patient outcome and need for special training of ETI providers.
  
  
  CONCLUSIONS
  Our risk assessment indicated a high probability for trauma patients with an indication for pre-hospital ETI not receiving it in the studied HEMS. The uncertainty factors identified in the assessment should be further investigated to better understand the problem assessed and consequences for the patients. Better quality of pre-hospital airway management data could contribute to a reduction of these uncertainties.
authors.affiliation: Department of Research and Development, Norwegian Air Ambulance Foundation, Drøbak, Norway. solste@snla.no
authors.forename: Stephen J M
authors.initials: SJ
authors.lastname: Sollid
authors.forename: Hans Morten
authors.initials: HM
authors.lastname: Lossius
authors.forename: Anders R
authors.initials: AR
authors.lastname: Nakstad
authors.forename: Terje
authors.initials: T
authors.lastname: Aven
authors.forename: Eldar
authors.initials: E
authors.lastname: Søreide
mesh_terms: D017732:Air Ambulances; D000776:Anesthesiology; D001499:Bayes Theorem; D004632:Emergency Medical Services; D006801:Humans; D007442:Intubation, Intratracheal / Q000009:adverse effects; D018570:Risk Assessment; D014947:Wounds and Injuries
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