Brazilian Journal of Anesthesiology
https://bjan-sba.org/article/doi/10.1016/j.bjane.2021.06.016
Brazilian Journal of Anesthesiology
Clinical Research

Predictive model for difficult laryngoscopy using machine learning: retrospective cohort study

Jong Ho Kim, Jun Woo Choi, Young Suk Kwon, Seong Sik Kang

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Abstract

Background
Both predictions and predictors of difficult laryngoscopy are controversial. Machine learning is an excellent alternative method for predicting difficult laryngoscopy. This study aimed to develop and validate practical predictive models for difficult laryngoscopy through machine learning.

Methods
Variables for the prediction of difficult laryngoscopy included age, Mallampati grade, body mass index, sternomental distance, and neck circumference. Difficult laryngoscopy was defined as grade 3 and 4 by the Cormack-Lehane classification. Pre-anesthesia and anesthesia data of 616 patients who had undergone anesthesia at a single center were included. The dataset was divided into a base training set (n = 492) and a base test set (n = 124), with equal distribution of difficult laryngoscopy. Training data sets were trained with six algorithms (multilayer perceptron, logistic regression, supportive vector machine, random forest, extreme gradient boosting, and light gradient boosting machine), and cross-validated. The model with the highest area under the receiver operating characteristic curve (AUROC) was chosen as the final model, which was validated with the test set.

Results
The results of cross-validation were best using the light gradient boosting machine algorithm with Mallampati score x age and sternomental distance as predictive model parameters. The predicted AUROC for the difficult laryngoscopy class was 0.71 (95% confidence interval, 0.59–0.83; p =  0.014), and the recall (sensitivity) was 0.85.

Conclusion
Predicting difficult laryngoscopy is possible with three parameters. Severe damage resulting from failure to predict difficult laryngoscopy with high recall is small with the reported model. The model’s performance can be further enhanced by additional data training.

Keywords

Intratracheal intubation;  Laryngoscopes;  Machine learning
60f083b6a953950fa7049f9c rba Articles
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Braz J Anesthesiol

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