{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,14]],"date-time":"2026-09-14T06:04:18Z","timestamp":1789365858865,"version":"build-2803163510"},"reference-count":26,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,14]],"date-time":"2026-09-14T00:00:00Z","timestamp":1789344000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Med."],"abstract":"<jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats: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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      This retrospective single-center cohort included 928 patients undergoing general anesthesia (training set:\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u2009650; held-out internal test set:\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u2009278). 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.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats: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. DCA showed greater net benefit across thresholds of 1%\u201395%. In same-center temporal validation, discrimination remained acceptable (ROC-AUC: 0.8807; PR-AUC: 0.8778), but calibration deteriorated (Brier score: 0.1924; intercept: \u22121.2250; 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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fmed.2026.1846230","type":"journal-article","created":{"date-parts":[[2026,9,14]],"date-time":"2026-09-14T05:39:40Z","timestamp":1789364380000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Prediction of moderate-to-severe postoperative thirst in general anesthesia patients based on automated machine learning and clinical nursing translation"],"prefix":"10.3389","volume":"13","author":[{"given":"Yi","family":"Zhan","sequence":"first","affiliation":[{"name":"Department of Anesthesiology and Perioperative Medicine, The First Affiliated Hospital with Nanjing Medical University","place":["Nanjing, Jiangsu, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Siting","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Anesthesiology and Perioperative Medicine, The First Affiliated Hospital with Nanjing Medical University","place":["Nanjing, Jiangsu, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haoran","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Anesthesiology and Perioperative Medicine, The First Affiliated Hospital with Nanjing Medical University","place":["Nanjing, Jiangsu, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,9,14]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"104214","DOI":"10.1016\/j.ijnurstu.2022.104214","article-title":"Effects of a spray-based oropharyngeal moisturising programme for patients following endotracheal extubation after cardiac surgery: a randomised, 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