Clinical Research Directory

Pre-operative Characteristics for Prediction of Supraglottic Airway Failure Using Machine Learning (ERICA)

Sponsor: University Hospital Ulm

Summary

Supraglottic airway devices (SGA) are a safe and well-established technique for airway management. Nowadays, up to 60% of general anaesthetics performed in European countries use SGA. In 0.2-4.7% SGA fail and require conversion to tracheal tubes. The ERICA study will use artificial intelligence methods to develop a model that can predict the risk of an unplanned SGA conversion based on pre-operative characteristics available during the premedication visit.

Official title: Can Pre-operative Characteristics Predict Failure of Supraglottic Airway to Tracheal Tube? A Machine Learning Algorithm (ERICA)

Key Details

Conditions

Interventions

Locations (2)