{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,28]],"date-time":"2026-03-28T05:29:38Z","timestamp":1774675778159,"version":"3.50.1"},"reference-count":45,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,6,20]],"date-time":"2025-06-20T00:00:00Z","timestamp":1750377600000},"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. Physiol."],"abstract":"<jats:sec><jats:title>Introduction<\/jats:title><jats:p>Perioperative stroke is a rare but severe complication that significantly impacts postoperative recovery and survival. This study aimed to develop a machine learning-based predictive model for perioperative stroke risk in patients undergoing noncardiac, nonvascular, and nonneurosurgical procedures.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>This retrospective cohort study was conducted using electronic medical records from 106,328 patients at Henan Provincial People\u2019s Hospital, with data from 2,986 patients analyzed. Nine machine learning models were developed to predict perioperative stroke risk, incorporating key variables such as age, history of stroke, comorbidities, surgical factors, and intraoperative data. The models\u2019 performance was evaluated using standard metrics, including area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, and F1 score.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Among the nine models, the gradient boosting machine (GBM) demonstrated the best performance. In the training set, GBM achieved an AUC of 0.966 (95% CI: 0.957\u20130.975), with accuracy, sensitivity, specificity, and an F1 score of 90.4%, 90.4%, 81.8%, and 79.0%, respectively. In the validation set, the model maintained strong performance, with an AUC of 0.936 (95% CI: 0.917\u20130.954), accuracy of 82.6%, sensitivity of 88.8%, specificity of 81.0%, and an F1 score of 67.1%. In comparison, other models, such as logistic regression, support vector machine (SVM), and neural networks, exhibited lower AUC and less favorable performance metrics. Overall, GBM outperformed all models, demonstrating the best balance across accuracy, sensitivity, specificity, and F1 score.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>The GBM model demonstrated strong predictive performance and generalizability for perioperative stroke risk in noncardiac, nonvascular, and nonneurosurgical patients. The integration of this model into a real-time clinical decision support system enhances clinical decision-making by enabling the early identification of high-risk patients and facilitating personalized interventions.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fphys.2025.1624898","type":"journal-article","created":{"date-parts":[[2025,6,20]],"date-time":"2025-06-20T04:10:25Z","timestamp":1750392625000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Development and validation of a machine learning-based model for perioperative stroke prediction in noncardiac, nonvascular, and nonneurosurgical patients"],"prefix":"10.3389","volume":"16","author":[{"given":"Xuhui","family":"Cong","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xuli","family":"Zou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ruilou","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yubao","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lu","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiaqiang","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2025,6,20]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"104270","DOI":"10.1016\/j.jbi.2022.104270","article-title":"Integrating machine learning predictions for perioperative risk management: towards an empirical design of a flexible-standardized risk assessment tool","volume":"137","author":"Abraham","year":"2023","journal-title":"J. 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