Prevalence of Difficult Airway Predictors in Cases of Failed Prehospital Endotracheal Intubation

Gaither, Joshua B; Spaite, Daniel W.; Stolz, Uwe; Ennis, Joshua; Mosier, Jarrod; Sakles, John

doi:10.1016/j.jemermed.2014.04.021

Cited by 41 publications

(31 citation statements)

References 23 publications

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“…Blood, secretions, and active vomiting have all been identified as predictors of difficult intubation 8,9,10. Current airway training models use traditional airway mannequins and intubations in the controlled setting of the operating room.…”

Section: Discussionmentioning

confidence: 99%

“…Neither of these methods duplicates the dynamic, challenging conditions surrounding emergency airways, including actively vomiting patients and those with blood and secretions contaminating the glottic view 3. Blood and vomitus in the airway has been identified as a predictor of difficult intubation 4,5,6,7. A training model that could simulate the challenges of an actively vomiting patient or a bloody airway would be ideal to prepare trainees to face these situations in real clinical practice.…”

Section: Introductionmentioning

confidence: 99%

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Novel Airway Training Tool that Simulates Vomiting: Suction-Assisted Laryngoscopy Assisted Decontamination (SALAD) System

DuCanto¹,

Serrano²,

Thompson³

2017

WestJEM

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IntroductionWe present a novel airway simulation tool that recreates the dynamic challenges associated with emergency airways. The Suction-Assisted Laryngoscopy Assisted Decontamination (SALAD) simulation system trains providers to use suction to manage emesis and bleeding complicating intubation.MethodsWe modified a standard difficult-airway mannequin head (Nasco, Ft. Atkinson, WI) with hardware-store equipment to enable simulation of vomiting or hemorrhage during intubation. A pre- and post-survey was used to assess the effectiveness of the SALAD simulator. We used a 1–5 Likert scale to assess confidence in managing the airway of a vomiting patient and comfort with suction techniques before and after the training exercise.ResultsForty learners participated in the simulation, including emergency physicians, anesthesiologists, paramedics, respiratory therapists, and registered nurses. The average Likert score of confidence in managing the airway of a vomiting or hemorrhaging patient pre-session was 3.10±0.49, and post-session 4.13±0.22. The average score of self-perceived skill with suction techniques in the airway scenario pre-session was 3.30±0.43, and post-session 4.03±0.26. The average score for usefulness of the session was 4.68±0.15, and the score for realism of the simulator was 4.65±0.17.ConclusionA training session with the SALAD simulator improved trainee’s confidence in managing the airway of a vomiting or hemorrhaging patient. The SALAD simulation system recreates the dynamic challenges associated with emergency airways and holds promise as an airway training tool.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Novel Airway Training Tool that Simulates Vomiting: Suction-Assisted Laryngoscopy Assisted Decontamination (SALAD) System

DuCanto¹,

Serrano²,

Thompson³

2017

WestJEM

View full text Add to dashboard Cite

show abstract

“…We are unaware of any previous efforts to design such a tool for predicting difficult ETI in the prehospital setting. While there have been several publications describing predictors of difficult intubation, none have been derived and validated as a predictive model for use by prehospital providers 13,25. A tool developed by a group of French anesthesiologists was evaluated in a small prehospital subgroup; however, generalization is limited as intubations were all performed by emergency physicians 26…”

Section: Discussionmentioning

confidence: 99%

“…These include blood in the airway, vomit in the airway, short neck, c-spine immobility, short mandible, obesity, airway edema, facial trauma, large tongue, limited Mallampati score, intra-incisor distance of <3 fingers, and thyromental distance of <2 fingers 9,10,13,27. These studies, examining multiple characteristics, provided the basis for the factors selected in our analysis.…”

Section: Discussionmentioning

confidence: 99%

“…Previous works have identified multiple factors associated with difficult ETI in a variety of acute care settings including the prehospital setting, intensive care unit, and emergency department 9–13. Although predictors and resultant treatment pathways have been identified, we are unaware of any externally validated, simplified identification tools for prehospital providers, identifying those factors most predictive of difficult ETI 9,10,14.…”

Section: Introductionmentioning

confidence: 99%

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Derivation and Validation of The Prehospital Difficult Airway IdentificationTool (PreDAIT): A Predictive Model for Difficult Intubation

Carlson¹,

Hostler²,

Guyette³

et al. 2017

WestJEM

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IntroductionEndotracheal intubation (ETI) in the prehospital setting poses unique challenges where multiple ETI attempts are associated with adverse patient outcomes. Early identification of difficult ETI cases will allow providers to tailor airway-management efforts to minimize complications associated with ETI. We sought to derive and validate a prehospital difficult airway identification tool based on predictors of difficult ETI in other settings.MethodsWe prospectively collected patient and airway data on all airway attempts from 16 Advanced Life Support (ALS) ground emergency medical services (EMS) agencies from January 2011 to October 2014. Cases that required more than two ETI attempts and cases where an alternative airway strategy (e.g. supraglottic airway) was employed after one unsuccessful ETI attempt were categorized as “difficult.” We used a random allocation sequence to split the data into derivation and validation subsets. Using backward elimination, factors with a p<0.1 were included in the multivariable regression for the derivation cohort and then tested in the validation cohort. We used this model to determine the area under the curve (AUC), and the sensitivity and specificity for each cut point in both the derivation and validation cohorts.ResultsWe collected data on 1,102 cases with 568 in the derivation set (155 difficult cases; 27%) and 534 in the validation set (135 difficult cases; 25%). Of the collected variables, five factors were predictive of difficult ETI in the derivation model (adjusted odds ratio, 95% confidence interval [CI]): Glasgow coma score [GCS] >3 (2.15, 1.19–3.88), limited neck movement (2.24, 1.28–3.93), trismus/jaw clenched (2.24, 1.09–4.6), inability to palpate the landmarks of the neck (5.92, 2.77–12.66), and fluid in the airway such as blood or emesis (2.25, 1.51–3.36). This was the most parsimonious model and exhibited good fit (Hosmer-Lemeshow test p = 0.167) with an AUC of 0.68 (95% CI [0.64–0.73]). When applied to the validation set, the model had an AUC of 0.63 (0.58–0.68) with high specificity for identifying difficult ETI if ≥2 factors were present (87.7% (95% CI [84.1–90.8])).ConclusionWe have developed a simple tool using five factors that may aid prehospital providers in the identification of difficult ETI.

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Impact of pathologic body composition assessed by CT-based anthropometric measurements in adult patients with multiple trauma: a retrospective analysis

Poros

Irlbeck

Probst

et al. 2019

Eur J Trauma Emerg Surg

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Prevalence of Difficult Airway Predictors in Cases of Failed Prehospital Endotracheal Intubation

Cited by 41 publications

References 23 publications

Novel Airway Training Tool that Simulates Vomiting: Suction-Assisted Laryngoscopy Assisted Decontamination (SALAD) System

Novel Airway Training Tool that Simulates Vomiting: Suction-Assisted Laryngoscopy Assisted Decontamination (SALAD) System

Derivation and Validation of The Prehospital Difficult Airway IdentificationTool (PreDAIT): A Predictive Model for Difficult Intubation

Impact of pathologic body composition assessed by CT-based anthropometric measurements in adult patients with multiple trauma: a retrospective analysis

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