Preparing for Emergency: A Valid, Reliable Assessment Tool for Emergency Cricothyroidotomy Skills. (10th November 2014)

• Record Type: Journal Article
• Authors: Melchiors, Jacob; Todsen, Tobias; Nilsson, Philip; Wennervaldt, Kasper; Charabi, Birgitte; Bøttger, Morten; Konge, Lars; von Buchwald, Christian
• Is Part Of: Otolaryngology--head and neck surgery. Volume 152:Number 2(2015:Feb.)
• Page Start: 260
• Page End: 265
• Publication Date: 2014-11-10
• Subjects: assessment -- simulation -- cricothyroidotomy -- surgical skills -- surgical education -- objective surgical assessment tool -- airway skills -- reliability -- validity; Head -- Surgery -- Periodicals; Neck -- Surgery -- Periodicals; Otolaryngology -- Periodicals; 617.51
• DOI: 10.1177/0194599814556722
• Languages: English
• ISSNs: 0194-5998

Abstract

Abstract : Objective: Emergency cricothyrodotomy (EC) is a lifesaving procedure. Evidence‐based assessment of training effects and competency levels is relevant to all departments involved in emergency airway management. As most training uses low‐fidelity models, the predictive value of good performance on such a model becomes relevant with regard to performance on a high‐fidelity model (cadaver). This requires a valid assessment tool for EC performance. Study Design: Psychometric study on low‐fidelity models and human cadavers. Setting: University hospital. Subjects and Methods: An assessment tool was created. Sixteen physicians (7 experienced surgeons and 9 novice physicians) performed an EC on a low‐fidelity model and a cadaver. Two blinded raters assessed video recordings of performances using the assessment tool. Results: We found a high interrater reliability, based on a Pearson's r (0.81), and good evidence for validity, based on successfully distinguishing the 2 groups, using an independent samples t test ( P <. 001). We found a good correlation between performance on a low‐fidelity model and the cadaver with an R 2 = 0.78. Conclusion: The tool for assessing EC competence proved reliable and valid. Performance on a low‐fidelity model measured this way is a good predictor of performance in a more lifelike situation.