Prediction of moderate-to-severe postoperative thirst in general anesthesia patients based on automated machine learning and clinical nursing translation.

Objective: 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. Methods: This retrospective single-center cohort included 928 patients undergoing general anesthesia (training set: Results: 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. DCA showed greater net benefit across thresholds of 1%-95%. In same-center temporal validation, discrimination remained acceptable (ROC-AUC: 0.8807; PR-AUC: 0.8778), but calibration deteriorated (Brier score: 0.1924; intercept: -1.2250; slope: 0.5811), indicating average risk overestimation and overly extreme probabilities. Six predictors were identified: esmolol use, intraoperative blood loss, operation time, ASA classification, ERAS, and intraoperative fluid volume. Conclusions: The AutoML model showed strong discrimination and transparent feature attribution. The prototype illustrates its potential use at PACU handover to prioritize early assessment and comfort-oriented thirst management. However, temporal miscalibration indicates that individualized probabilities are not ready for clinical decision-making without recalibration and subsequent prospective validation.