{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,22]],"date-time":"2026-10-22T09:36:37Z","timestamp":1792661797092,"version":"4.5.0"},"reference-count":42,"publisher":"Walter de Gruyter GmbH","issue":"1","license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,1,23]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec id=\"j_med-2026-1381_abs_001\">\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>To develop and validate machine learning (ML) models for identifying key predictors and estimating the risk of intraoperative hypotension (IOH) in elderly patients undergoing general anesthesia.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"j_med-2026-1381_abs_002\">\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This secondary analysis included 1,720 elderly surgical patients from a randomized controlled trial. Data were split chronologically into training sets. Feature selection was performed using univariate analysis and the Boruta algorithm. Eight ML models\u00a0\u2013\u00a0logistic regression, Bayesian model, K-nearest neighbor, support vector machine, neural network, classification and regression tree, extreme gradient boosting, and random forest\u00a0\u2013\u00a0were developed with cross-validation, hyperparameter tuning, and random oversampling. Model performance was evaluated using ROC, PRC, calibration, and decision curve analyses, and interpretability was enhanced using SHapley Additive exPlanations (SHAP).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"j_med-2026-1381_abs_003\">\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Key predictors included anesthesia protocol, Charlson comorbidity index, preoperative sodium, creatinine, BUN\/creatinine ratio, intraoperative drug use (e.g., sevoflurane, lidocaine, morphine), preoperative MAP and MHR, surgical and anesthesia duration, and surgical site. The random forest model achieved the best performance (accuracy=0.9917; MCC=0.9832; AUC-ROC=0.9998; AUC-PRC=0.9998).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"j_med-2026-1381_abs_004\">\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>A robust ML-based model was established to accurately predict IOH in elderly patients. These findings may support individualized anesthesia management and targeted preventive strategies to reduce IOH incidence.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1515\/med-2026-1381","type":"journal-article","created":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T21:02:52Z","timestamp":1773781372000},"source":"Crossref","is-referenced-by-count":1,"title":["Development of a machine learning algorithm model to predict intraoperative hypotension in elderly patients undergoing thoracic and abdominal surgeries"],"prefix":"10.1515","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-5720-6817","authenticated-orcid":false,"given":"Yifan","family":"An","sequence":"first","affiliation":[{"name":"Department of Anesthesiology , Beijing Shijitan Hospital, Capital Medical University , Beijing , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8098-8925","authenticated-orcid":false,"given":"Pengfei","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Anesthesiology , Beijing Shijitan Hospital, Capital Medical University , Beijing , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-9704-0043","authenticated-orcid":false,"given":"Lei","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Science and Technology , Beijing Shijitan Hospital, Capital Medical University , Beijing , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-0174-5938","authenticated-orcid":false,"given":"Xiaoyun","family":"Hu","sequence":"additional","affiliation":[{"name":"Department of Anesthesiology , Beijing Shijitan Hospital, Capital Medical University , Beijing , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6966-9261","authenticated-orcid":false,"given":"Hui","family":"Qiao","sequence":"additional","affiliation":[{"name":"Department of Anesthesiology , Beijing Shijitan Hospital, Capital Medical University , Beijing , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5443-9498","authenticated-orcid":false,"given":"Weixuan","family":"Sheng","sequence":"additional","affiliation":[{"name":"Department of Anesthesiology , Beijing Shijitan Hospital, Capital Medical University , Beijing , China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"374","published-online":{"date-parts":[[2026,3,16]]},"reference":[{"key":"2026051920360849618_j_med-2026-1381_ref_001","doi-asserted-by":"crossref","unstructured":"Stenglova, A, Benes, J. 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