Development and validation of a difficult laryngoscopy prediction model using machine learning of neck circumference and thyromental height.
BMC Anesthesiol
; 21(1): 125, 2021 04 21.
Article
em En
| MEDLINE
| ID: mdl-33882838
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.Palavras-chave
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Cartilagem Tireóidea
/
Aprendizado de Máquina
/
Laringoscopia
/
Pescoço
Tipo de estudo:
Diagnostic_studies
/
Prognostic_studies
/
Risk_factors_studies
Limite:
Humans
Idioma:
En
Ano de publicação:
2021
Tipo de documento:
Article