Evaluating non-linear thresholds and synergistic interactions in the comorbidity of preoperative insomnia and emergence agitation: a SHAP-based machine learning study.

Background: Preoperative insomnia disorder (PID) and emergence agitation (EA) often present as a complex perioperative comorbidity. Traditional linear models may not adequately capture their underlying interactive mechanisms. Methods: This retrospective, single-center study analyzed 542 adult surgical patients. Machine learning algorithms, particularly XGBoost, were developed using stratified random splitting (70% training, 30% testing) to predict the PID-EA co-occurrence. The Shapley Additive exPlanations (SHAP) framework was utilized to interpret statistical associations and non-linear interactions among clinical variables. Results: The XGBoost model demonstrated superior discriminative performance (AUC = 0.874 [95% CI: 0.812-0.936], AUPRC = 0.883, Brier score = 0.150) compared to logistic regression (AUC = 0.749 [95% CI: 0.675-0.823]). SHAP analysis indicated that preoperative cortisol and melatonin were the primary features associated with the model's predictions. Partial dependence plots suggested potential non-linear risk thresholds for neuroendocrine markers. Furthermore, interaction analyses illustrated that severe postoperative pain may synergistically amplify the risk associated with preoperative insomnia. Conclusion: The integration of XGBoost and SHAP offers an interpretable approach to evaluating the PID-EA comorbidity network. These findings highlight potential clinical thresholds and interaction patterns, warranting further validation through multi-center prospective studies.