The neural network model demonstrated superior generalization capabilities in predicting postoperative sore throat compared to XGBoost and random forest models in external validation, achieving an AUROC of 0.81.
Observational (n=834)
Yes
Does a neural network model accurately predict postoperative sore throat in adult patients undergoing general anesthesia with endotracheal intubation?
A neural network model using five clinical variables accurately predicts postoperative sore throat, outperforming other machine learning models and offering a tool for targeted prevention.
Effect estimate: AUROC 0.81 (95% CI 0.74-0.89)
Absolute Event Rate: 0.81% vs 0.78%
Postoperative sore throat (POST) is a prevalent complication after general anesthesia and targeting high-risk patients helps in its prevention. This study developed and validated a machine learning model to predict POST. A total number of 834 patients who underwent general anesthesia with endotracheal intubation were included in this study. Data from a cohort of 685 patients was used for model development and validation, while a cohort of 149 patients served for external validation. The prediction performance of random forest (RF), neural network (NN), and extreme gradient boosting (XGBoost) models was compared using comprehensive performance metrics. The Local Interpretable Model-Agnostic Explanations (LIME) methods elucidated the best-performing model. POST incidences across training, validation, and testing cohorts were 41.7%, 38.4%, and 36.2%, respectively. Five predictors were age, sex, endotracheal tube cuff pressure, endotracheal tube insertion depth, and the time interval between extubation and the first drinking of water after extubation. After incorporating these variables, the NN model demonstrated superior generalization capabilities in predicting POST when compared to the XGBoost and RF models in external validation, achieving an area under the receiver operating characteristic curve (AUROC) of 0.81 (95% CI 0.74–0.89) and a precision–recall curve (AUPRC) of 0.77 (95% CI 0.66–0.86). The model also showed good calibration and clinical usage values. The NN model outperforms the XGBoost and RF models in predicting POST, with potential applications in the healthcare industry for reducing the incidence of this common postoperative complication.
Zhou et al. (2023) conducted an observational in Postoperative sore throat (POST) (n=834). Neural network (NN) prediction model vs. Extreme gradient boosting (XGBoost) and Random forest (RF) models was evaluated on Area under the receiver operating characteristic curve (AUROC) for predicting POST in the external validation cohort (AUROC 0.81, 95% CI 0.74-0.89). The neural network model demonstrated superior generalization capabilities in predicting postoperative sore throat compared to XGBoost and random forest models in external validation, achieving an AUROC of 0.81.