A logistic regression model demonstrated good overall performance for predicting emergence delirium risk in elderly patients undergoing spinal surgery, achieving an area under the curve of 0.832.
Cohort (n=430)
No
Can a machine learning-based model predict emergence delirium risk in elderly patients undergoing spinal surgery?
An interpretable logistic regression model showed acceptable overall performance for predicting emergence delirium risk in elderly patients undergoing spinal surgery, identifying education, age, and midazolam exposure as key predictors.
Effect estimate: AUC 0.832
Emergence delirium (ED) is a common complication in elderly patients undergoing surgery for degenerative spinal disease (DSD) and is associated with adverse postoperative outcomes. Risk prediction may support perioperative risk stratification and individualized monitoring. This study aimed to develop a machine learning based model to predict ED risk in this population. This single-center prospective observational cohort study enrolled 430 elderly patients with DSD undergoing spinal surgery at Shanghai Changzheng Hospital between January and October 2024 using convenience sampling. Eligible patients were screened and consented before surgery, and predictors were collected prospectively across five perioperative domains: patient characteristics, preoperative, intraoperative, anesthesia-related, and early postoperative factors. Emergence delirium was assessed as the outcome in the PACU. Feature selection was performed using least absolute shrinkage and selection operator (LASSO) regression. Six machine learning (ML) models were developed and internally validated, and model interpretability was assessed using SHapley Additive exPlanations (SHAP). Among 38 features examined, LASSO regression identified nine key predictors, including age, education level, postoperative pain, and midazolam exposure, among others. Among six ML models evaluated in the testing set, logistic regression achieved the best overall performance, with an area under the curve of 0.832, AUPRC of 0.693, accuracy of 0.769, specificity of 0.858, sensitivity of 0.610, F1-score of 0.650, and Brier score of 0.163. Global feature importance ranking was determined using the SHAP summary plot, showing that the most influential features were education, age, and Midazolam. Individual feature importance, as depicted by the SHAP beeswarm plot, indicated that intraoperative bleeding, operation duration, and age had the most significant impact on prediction. In addition, a potential synergistic pattern between operation duration and intraoperative blood loss indicated a synergistic increase in emergence delirium risk when both were elevated. An interpretable logistic regression model showed acceptable overall performance for predicting emergence delirium risk in elderly patients undergoing spinal surgery. The model may support anesthesia-informed perioperative risk stratification and individualized monitoring; however, given its moderate sensitivity, it should be used as an adjunct to clinical judgment rather than as a stand-alone screening tool.
Wang et al. (Fri,) conducted a cohort in Degenerative spinal disease (n=430). Logistic regression prediction model was evaluated on Prediction of emergence delirium (AUC 0.832). A logistic regression model demonstrated good overall performance for predicting emergence delirium risk in elderly patients undergoing spinal surgery, achieving an area under the curve of 0.832.