Classification and Prediction of Difficult Awake Tracheal Intubation With Flexible Bronchoscopes

Airway management problems are key drivers for anesthesia-related adverse events. Awake tracheal intubation using flexible bronchoscopes with preserved spontaneous breathing (ATI:FB) is a recommended technique to manage difficult tracheal intubation in anesthesia, intensive care and emergency medicine. However, a prospective developed classification for this type of airway management is lacking. Due to the absence of a specifically tailored, validated classification for awake intubation with flexible bronchoscopes, many airway operators and institutions use classification tools that were originally developed for direct laryngoscopy, such as the percentage of glottic opening (POGO) score or Cormack-Lehane classification, although their diagnostic performance for the classification of ATI:FB is unknown. This prospective model development and validation study aims to develop two multivariable prediction models: a diagnostic prediction model to classify difficult ATI:FB after ATI:FB has been performed and a second prognostic prediction model to predict the risk for difficult ATI:FB before ATI:FB is performed. An additional aim is to develop a machine learning algorithm to evaluate ATI:FB.

NCT Number	NCT06953414
Lead Sponsor	Universitätsklinikum Hamburg-Eppendorf
Conditions	Anesthesia, Airway Management, Intubation, Intratracheal, Bronchoscopy, Endoscopes
Enrollment	313 participants
Start Date	2025-04-30
Primary Completion	2026-01-31 (estimated)
Study Completion	2026-01-31 (estimated)
Updated on ClinicalTrials.gov	2025-05-06