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

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.

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

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.

Endovascular repair of subclavian artery injury secondary to internal jugular vein catheterization: case report / Correção endovascular de lesão de artéria subclávia secundária à cateterismo de veia jugular interna: relato de caso

Abstract Background and objectives Ultrasound-guided internal jugular vein catheterization is a common and generally safe procedure in the operating room. However, inadvertent puncture of a noncompressible artery such as the subclavian artery, though rare, may be associated with life-threatening sequelae, including hemomediastinum, hemothorax, and pseudoaneurysm. Case report We describe a case of the successful endovascular repair of right subclavian artery injury in a 75-year-old woman. Subclavian artery was injured secondary to ultrasound-guided right internal jugular vein catheterization under general anesthesia for orthopedic surgery. Conclusion Under general anesthesia several factors such as hypotension can mask the signs of subclavian artery injury. This case report indicates that clinicians should be aware of the complications of central venous catheterization and take prompt action.

Resumo Justificativa e objetivos A cateterização da veia jugular interna guiada por ultrassom é um procedimento comum e geralmente seguro em sala cirúrgica. No entanto, a punção inadvertida de uma artéria não compressível, como a artéria subclávia, embora rara, pode estar associada a sequelas e risco para vida, incluindo hemomediastino, hemotórax e pseudoaneurisma. Relato de caso Descrevemos um caso bem-sucedido da correção endovascular de lesão da artéria subclávia direita em uma paciente de 75 anos. A artéria subclávia foi lesionada após cateterização guiada por ultrassom da veia jugular interna direita sob anestesia geral para cirurgia ortopédica. Conclusão Sob anestesia geral, vários fatores, como a hipotensão, podem mascarar os sinais de lesão da artéria subclávia. Este relato de caso indica que os médicos devem estar cientes das complicações da cateterização venosa central e tomar medidas imediatas.

[Endovascular repair of subclavian artery injury secondary to internal jugular vein catheterization: case report]. / Correção endovascular de lesão de artéria subclávia secundária à cateterismo de veia jugular interna: relato de caso.

BACKGROUND AND OBJECTIVES: Ultrasound-guided internal jugular vein catheterization is a common and generally safe procedure in the operating room. However, inadvertent puncture of a noncompressible artery such as the subclavian artery, though rare, may be associated with life-threatening sequelae, including hemomediastinum, hemothorax, and pseudoaneurysm. CASE REPORT: We describe a case of the successful endovascular repair of right subclavian artery injury in a 75-year-old woman. Subclavian artery was injured secondary to ultrasound-guided right internal jugular vein catheterization under general anesthesia for orthopedic surgery. CONCLUSION: Under general anesthesia several factors such as hypotension can mask the signs of subclavian artery injury. This case report indicates that clinicians should be aware of the complications of central venous catheterization and take prompt action.