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Predictive model for difficult laryngoscopy using machine learning: retrospective cohort study.
Kim, Jong Ho; Choi, Jun Woo; Kwon, Young Suk; Kang, Seong Sik.
Afiliação
  • Kim JH; Chuncheon Sacred Heart Hospital, Department of Anesthesiology and Pain Medicine, Chuncheon, South Korea; Hallym University, Institute of New Frontier Research Team, Chuncheon, South Korea.
  • Choi JW; Chuncheon Sacred Heart Hospital, Department of Anesthesiology and Pain Medicine, Chuncheon, South Korea.
  • Kwon YS; Chuncheon Sacred Heart Hospital, Department of Anesthesiology and Pain Medicine, Chuncheon, South Korea; Hallym University, Institute of New Frontier Research Team, Chuncheon, South Korea. Electronic address: gettys@hallym.or.kr.
  • Kang SS; Kangwon National University, College of Medicine, Department of Anesthesiology and Pain Medicine, Chuncheon, South Korea.
Braz J Anesthesiol ; 72(5): 622-628, 2022.
Article em En | MEDLINE | ID: mdl-34252452
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.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Intubação Intratraqueal / Laringoscopia Tipo de estudo: Etiology_studies / Observational_studies / Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Revista: Braz J Anesthesiol Ano de publicação: 2022 Tipo de documento: Article País de afiliação: Coréia do Sul País de publicação: Brasil
Texto completo
Adicionar na Minha BVS
Imprimir
XML
PubMed Links
Buscar no Google

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Intubação Intratraqueal / Laringoscopia Tipo de estudo: Etiology_studies / Observational_studies / Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Revista: Braz J Anesthesiol Ano de publicação: 2022 Tipo de documento: Article País de afiliação: Coréia do Sul País de publicação: Brasil