Development and validation of a regression model with nomogram for difficult video laryngoscopy in Chinese population: a prospective, single-center, and nested case-control study.

Abstract

Airway management failure is associated with increased perioperative morbidity and mortality. Airway-related complications can be significantly reduced if difficult laryngoscopy is predicted with high accuracy. Currently, there are no large-sample studies on difficult airway assessments in Chinese populations. An airway assessment model based on the Chinese population is urgently needed to guide airway rescue strategy. This prospective nested case-control study took place in a tertiary hospital in Shanghai, China. Information on 10,549 patients was collected, and 8,375 patients were enrolled, including 7,676 patients who underwent successful laryngoscopy and 699 patients who underwent difficult laryngoscopy. The baseline characteristics, medical history, and bedside examinations were included as predictor variables. Laryngoscopy was defined as 'successful laryngoscopy' based on a Cormack-Lehane Grades of 1-2 and as 'difficult laryngoscopy' based on a Cormack-Lehane Grades of 3-4. A model was developed by incorporating risk factors and was presented in the form of a nomogram by univariate logistic regression, least absolute shrinkage and selection operator, and stepwise logistic regression. The main outcome measures were area under the curve (AUC), sensitivity, and specificity of the predictive model. The AUC value of the prediction model was 0.807 (95% confidence interval [CI]: 0.787-0.828), with a sensitivity of 0.730 (95% CI, 0.690-0.769) and a specificity of 0.730 (95% CI, 0.718-0.742) in the training set. The AUC value of the prediction model was 0.829 (95% CI, 0.800-0.857), with a sensitivity of 0.784 (95% CI, 0.73-0.838) and a specificity of 0.722 (95% CI, 0.704-0.740) in the validation set. Our model had accurate predictive performance, good clinical utility, and good robustness for difficult laryngoscopy in the Chinese population.