Development and validation of a difficult laryngoscopy prediction model using machine learning of neck circumference and thyromental height

Abstract

Background Predicting difficult airway is challengeable in patients with limited airway evaluation. The aim of this study is to develop and validate a model that predicts difficult laryngoscopy by machine learning of neck circumference and thyromental height as predictors that can be used even for patients with limited airway evaluation. Methods Variables for prediction of difficulty laryngoscopy included age, sex, height, weight, body mass index, neck circumference, and thyromental distance. Difficult laryngoscopy was defined as Grade 3 and 4 by the Cormack-Lehane classification. The preanesthesia and anesthesia data of 1677 patients who had undergone general anesthesia at a single center were collected. The data set was randomly stratified into a training set (80%) and a test set (20%), with equal distribution of difficulty laryngoscopy. The training data sets were trained with five algorithms (logistic regression, multilayer perceptron, random forest, extreme gradient boosting, and light gradient boosting machine). The prediction models were validated through a test set. Results The model’s performance using random forest was best (area under receiver operating characteristic curve = 0.79 [95% confidence interval: 0.72–0.86], area under precision-recall curve = 0.32 [95% confidence interval: 0.27–0.37]). Conclusions Machine learning can predict difficult laryngoscopy through a combination of several predictors including neck circumference and thyromental height. The performance of the model can be improved with more data, a new variable and combination of models.

Artificial intelligence and anesthesia: a narrative review

“…In addition, the model can work at high sensitivity and low specificity (0.9079 and 0.4474) or low sensitivity and high specificity (0.3684 and 0.9605), exceeding the limits of low sensitivity of current tests. Kim et al ( 14 ) proposed a predictive model of difficult laryngoscopy, defined as Grade 3 and 4 by the Cormack-Lehane classification. In this monocentric study, Balanced Random Forest (BRF) algorithm showed the best performance with area under the receiver operating characteristics (AUROC) of 0.79 (0.72–0.86).…”

“…In addition, the model can work at high sensitivity and low specificity (0.9079 and 0.4474) or low sensitivity and high specificity (0.3684 and 0.9605), exceeding the limits of low sensitivity of current tests. Kim et al (14) proposed a predictive model of difficult laryngoscopy,…”

Deep‐learning model associating lateral cervical radiographic features withCormack–Lehanegrade 3 or 4 glottic view

“…Previous studies using machine learning algorithms to predict difficult laryngoscopy, difficult airway or difficult intubation have reported high predictive power [27][28][29], but they all required indicators that should be evaluated by clinicians. In contrast, our method does not require any human evaluation.…”

Automated location of orofacial landmarks to characterize airway morphology in anaesthesia via deep convolutional neural networks

“…TMD, SMD, MO-IIG or neck circumference) -these latter magnitudes having been measured manually, as in the bedside screenings from current clinical practise. Works in this category include Yan et al [11] (who trained a Support Vector Machine), Langeron et al [12] (who employed Tree Boosting), Kim et al [13] (5 ML algorithms, among which Random Forests performed best), Yamanaka et al [14] (with an ensemble of 7 ML algorithms), or Zhou et al [15] (10 algorithms, with Gradient Boosting as their best).…”