{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T05:00:56Z","timestamp":1790744456388,"version":"4.1.0"},"reference-count":29,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T00:00:00Z","timestamp":1790726400000},"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>Background<\/jats:title>\n                    <jats:p>Intraoperative hypotension (IOH) is a common and potentially preventable complication strongly associated with postoperative acute kidney injury, myocardial injury, and mortality. Current approaches to IOH management remain largely reactive, and existing prediction tools have variable clinical utility. This study aimed to develop and externally validate a dynamic machine-learning model using routinely collected perioperative data to perform one-step-ahead prediction of intraoperative hypotension on a regularized 5-minute intraoperative grid.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This retrospective multicenter study included 702 adults undergoing non-cardiac surgery under general anesthesia for model development and internal validation and an independent external cohort of 248 patients. A dynamic prediction framework was constructed using routinely collected preoperative variables and intraoperative 5-minute time-series data. Individualized baseline features and temporal predictors were derived from rolling observation windows. Four machine-learning algorithms were compared using a fully nested grouped cross-validation framework with external validation. Model performance was assessed by discrimination, calibration, decision-curve analysis, and SHapley Additive exPlanations (SHAP)-based interpretability.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>All four machine-learning models demonstrated good discrimination in the primary fully nested case-grouped internal validation, with AUROC values ranging from 0.842 to 0.857. Random forest achieved the highest AUROC (0.857, 95% CI 0.841\u20130.868), the highest AUPRC (0.413, 95% CI 0.367\u20130.452), and extra trees achieved the lowest Brier score (0.093, 95% CI 0.088\u20130.098). In the independent external validation cohort, random forest remained the best-performing model, yielding an AUROC of 0.894 (95% CI 0.875\u20130.907), an AUPRC of 0.540 (95% CI 0.457\u20130.596), and a Brier score of 0.092 (95% CI 0.083\u20130.102). SHAP analysis consistently identified MAP-derived features, including current MAP, recent hypotensive burden, recent MAP measurements, relative MAP reduction, and MAP variability, as the most influential predictors across all four models.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>A dynamic machine-learning model using routinely collected perioperative time-series data accurately predicted MAP &amp;lt;65 mmHg at the next scheduled 5-minute intraoperative time point in an independent external cohort. These findings support further evaluation of this short-horizon risk stratification model in prospective clinical workflow studies.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fmed.2026.1884174","type":"journal-article","created":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T04:48:20Z","timestamp":1790743700000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Dynamic machine-learning model for early prediction of intraoperative hypotension using 5-minute perioperative time-series data: a multicenter retrospective study"],"prefix":"10.3389","volume":"13","author":[{"given":"Bo","family":"Long","sequence":"first","affiliation":[{"name":"Department of Anesthesiology, The Second Affiliated Hospital of Fujian University of Traditional Chinese Medicine","place":["Fuzhou, Fujian, China"]},{"name":"Department of Anesthesiology, Fujian Maternity and Child Health Hospital College of Clinical Medicine for Obstetrics & Gynecology and Pediatrics, Fujian Medical University","place":["Fuzhou, Fujian, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yifeng","family":"Ye","sequence":"additional","affiliation":[{"name":"Department of Anesthesiology, The First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University","place":["Xiamen, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hang","family":"Xing","sequence":"additional","affiliation":[{"name":"Department of Pediatrics, Women & Infants Hospital of Rhode Island, Warren Alpert Medical School of Brown University","place":["Providence, RI, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lijun","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Anesthesiology, Fujian Maternity and Child Health Hospital College of Clinical Medicine for Obstetrics & Gynecology and Pediatrics, Fujian Medical 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