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

Indication criteria for camera-assisted and awake tracheal intubation are vague. It is unknown if diagnostic and clinical data from multiple sources, such as transnasal videoendoscopy or symptoms for pharyngolaryngeal lesions, might improve preanesthesia airway management planning and decision-making in patients undergoing head and neck surgery.

To develop and validate a new decision-making tool (Evidence-Based Algorithm for the Expected Difficult Intubation [Expect-It]) and show noninferiority to the clinical standard (nonalgorithm-based decision-making).

This single-center study prospectively developed and validated a decision-making tool with a 2-stage design that included anesthetic cases from patients undergoing head and neck surgery between May 1, 2021, and January 29, 2022. Data were analyzed between August 2021 (first stage) and December 2023.

Airway-related risk factors from 4 domains (previous intubation difficulties, physical examination, physician's rating of difficult airway indicators, and pharyngolaryngeal lesions/transnasal videoendoscopy findings) were preoperatively assessed. During airway management planning, physicians proposed a first-line tracheal intubation technique (camera-assisted or direct laryngoscopy) and strategy (awake or asleep tracheal intubation). In the development cohort, these proposals were nonalgorithm-based (clinical standard); in the validation cohort, they relied on the Expect-It decision-making tool.

Regularized regression was used to select potentially predictive airway-related risk factors (covariables). The final decision-making tool is a combined score originating from 2 multivariable logistic regression models that predict 2 different primary outcomes: the most appropriate (1) tracheal intubation technique (camera-assisted or direct laryngoscopy) and (2) strategy (awake or asleep), as determined by the anesthesiologists after tracheal intubation.

Of 1201 patients (mean [SD] age, 50.3 [19.0] years; 695 [58%] male), 1282 anesthetic cases were included in the analysis: 602 in the development and 680 in the validation cohort. The area under the curve of the decision-making tool was 0.86 (95% CI, 0.81-0.90) to predict appropriate camera-assisted and 0.97 (95% CI, 0.96-0.99) to predict appropriate awake tracheal intubation in the development cohort. The sensitivity of the Expect-It tool to predict both appropriate camera-assisted and awake tracheal intubation was superior compared to the clinical standard (camera-assisted: 88% [95% CI, 81%-93%] vs 35% [95% CI, 27%-44%], respectively; awake tracheal intubation: 97% [95% CI, 81%-100%] vs 29% [95% CI, 15%-50%], respectively), and specificity was noninferior to the clinical standard (camera-assisted: 97% [95% CI, 96%-98%] vs 96% [95% CI, 93%-97%], respectively; awake tracheal intubation: 100% [95% CI, 99%-100%] vs 98% [95% CI, 97%-99%], respectively). After tool implementation, the first-attempt success rate increased from the development to validation cohort (437 [73%] vs 557 [82%], respectively; odds ratio, 1.72 [95% CI, 1.32-2.22]), while failed direct laryngoscopy decreased from the development to validation cohort (45 [8%] vs 10 [2%], respectively; odds ratio, 0.18 [95% CI, 0.09-0.37]).

In this study, the Expect-It tool for airway management planning was prospectively developed and validated. The tool was found to support airway management planning accurately and may serve as a precursor for intelligent algorithms.