Data Sheet 2_Prediction of moderate-to-severe postoperative thirst in general anesthesia patients based on automated machine learning and clinical nursing translation.docx

Objective<p>To develop an Automated Machine Learning (AutoML)-based model for screening Moderate-to-Severe Postoperative Thirst (MSPOT) risk at surgery-to-PACU handover and a prototype clinical decision support system.</p>Methods<p>This retrospective single-center cohort included 928 patients undergoing general anesthesia (training set: n = 650; held-out internal test set: n = 278). An Improved Wave Optics Optimizer (IWOO) framework integrated feature selection and hyperparameter optimization using demographic, preoperative, and intraoperative variables.

Prediction was performed at anesthesia-to-PACU handover, after final intraoperative data became available and before routine postoperative NRS thirst assessment. Performance was evaluated using ROC-AUC, PR-AUC, calibration analysis, Brier score, and decision curve analysis (DCA). SHAP was used to summarize feature contributions.</p>Results<p>In the held-out internal test set, the AutoML model achieved a ROC-AUC of 0.9053, PR-AUC of 0.9080, and Brier score of 0.129, outperforming conventional models.