Decision-Making Tool for Planning Camera-Assisted and Awake Intubation in Head and Neck Surgery

Area of Science:

• Anesthesiology
• Airway Management
• Surgical Planning

Background:

• Current criteria for camera-assisted and awake tracheal intubation are unclear.
• Diagnostic and clinical data integration may enhance preanesthesia airway management planning for head and neck surgery patients.

Purpose of the Study:

• Develop and validate the Evidence-Based Algorithm for the Expected Difficult Intubation (Expect-It) tool.
• Demonstrate noninferiority of the Expect-It tool compared to standard non-algorithm-based decision-making.

Main Methods:

• Prospectively developed and validated a decision-making tool using a 2-stage design in head and neck surgery patients.
• Assessed airway-related risk factors from four domains: intubation history, physical exam, physician assessment, and pharyngolaryngeal findings.
• Used regularized regression and multivariable logistic regression to create predictive models for intubation technique and strategy.

Main Results:

• The Expect-It tool demonstrated high accuracy (AUC 0.86-0.97) in predicting appropriate camera-assisted and awake tracheal intubation.
• Sensitivity for predicting appropriate techniques was superior to the clinical standard (88% vs 35% for camera-assisted; 97% vs 29% for awake).
• First-attempt success rates increased (73% to 82%), and failed direct laryngoscopy decreased (8% to 2%) after tool implementation.

Conclusions:

• The prospectively developed and validated Expect-It tool accurately supports airway management planning.
• The tool shows potential as a precursor for developing intelligent algorithms in airway management.
• Expect-It offers a data-driven approach to optimize tracheal intubation strategies in complex surgical cases.