Unanticipated difficult airway in obstetric patients: Development of a new algorithm for formative assessment in high-fidelity simulation
Background: The objective of this study was to develop a consensus-based algorithm for the management of the unanticipated difficult airway in obstetrics, and to use this algorithm for the assessment of anesthesia residents' performance during high-fidelity simulation. Methods: An algorithm for unanticipated difficult airway in obstetrics, outlining the management of six generic clinical situations of "can and cannot ventilate" possibilities in three clinical contexts: elective cesarean section, emergency cesarean section for fetal distress, and emergency cesarean section for maternal distress, was used to create a critical skills checklist. The authors used four of these scenarios for high-fidelity simulation for residents. Their critical and crisis resource management skills were assessed independently by three raters using their checklist and the Ottawa Global rating scale. Results: Sixteen residents participated. The checklist scores ranged from 64-80% and improved from scenario 1 to 4. Overall Global rating scale scores were marginal and not significantly different between scenarios. The intraclass correlation coefficient of 0.69 (95% CI: 0.58, 0.78) represents a good interrater reliability for the checklist. Multiple critical errors were identified, the most common being not calling for help or a difficult airway cart. Conclusions: Aside from identifying common critical errors, the authors noted that the residents' performance was poorest in two of our scenarios: "fetal distress and cannot intubate, cannot ventilate" and "maternal distress and cannot intubate, but can ventilate." More teaching emphasis may be warranted to avoid commonly identified critical errors and to improve overall management. Our study also suggests a potential for experiential learning with successive simulations. Copyright © 2012, the American Society of Anesthesiologists, Inc. Lippincott Williams & Wilkins.