BACKGROUND AND AIMS

An unanticipated difficult airway is one of the greatest challenges for anesthesiologists. Proper preoperative airway assessment is crucial to reducing complications. However, current screening tests based on anthropometric features are of uncertain benefit. Therefore, our study explores using voice analysis with machine learning algorithms to predict a difficult airway.

METHODS

Observational, multicenter study with N = 438 patients initially enrolled at Centro Medico Teknon and Institut Universitari Dexeus (2019-2022) for the research study. After excluding 125 patients, N = 313 were included. Ethics committee approval was obtained. Adults ASA I-III scheduled for elective procedures under general anesthesia with endotracheal intubation were selected. Patient clinical features and traditional predictive tests were collected. Vowels "A, E, I, O, U" were recorded in normal, flexion, and extension positions. Cormack grade was assessed, and data were analyzed using KNIME, resulting in multiple models based on demographics and voice data. ROC curves and other metrics were evaluated for each model.

RESULTS

Among multiple models evaluated, two yielded the best performance to predict a difficult airway both exclusively analyzing Cormack I and IV cases which showed the most distinct differences. The variables included in each model were the following: Model 1; included demographic data, vowel "A" in all positions and harmonics of the voice achieving an AUC of 0.91. Model 2; Included demographic data, vowel "O" in normal positions and voice parameters (Shimmer, Jitter, HNR); achieving in an AUC of 0.90. In contrast, models which focused on analyzing all Cormack grades (I, II, III, IV) cases performed less effectively.

CONCLUSIONS

Acoustic parameters of the voice together with the demographic data of the patients, when introduced into classification algorithms based on machine learning showed promising signs of predicting a difficult airway.