Data-driven Development of a Core Dataset for Difficult Airway Alerts

Previous difficult airway management is the most accurate predictor of future difficulty. Consistent documentation is paramount for future airway planning, but requires reliable, reproducible and easily accessible information. Currently, anaesthesia alert cards are often based on analogue hard copies while they lack a clinically meaningful core data set allowing structured reproducible documentation and risk estimation. Further, existing alert cards are often inconsistently used and clear triggers for issuing of airway alert cards are widely undefined. The FingAIRprint project aims to develop a justifiable core data set using a data-driven approach in patients undergoing tracheal intubation with videolaryngoscopy or direct laryngoscopy, that is intended to be used for documentation of digital airway alerts.