{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,31]],"date-time":"2026-08-31T11:01:02Z","timestamp":1788174062755,"version":"build-2803163510"},"reference-count":40,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,12,12]],"date-time":"2025-12-12T00:00:00Z","timestamp":1765497600000},"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. Endocrinol."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Accurately predicting intraoperative hemodynamic instability (HI) in patients with pheochromocytoma is essential for improving prognosis; however, clinically applicable large-sample, high-precision predictive models remain limited. This study develops and validates an ensemble learning (EL) model to predict HI risk.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This cohort study included a derivation cohort (n = 353) and an external validation cohort (n = 51), from January 2011 to February 2023. General clinical and intraoperative hemodynamic data were collected. Ensemble feature selection was used to identify key predictors. 5-fold cross-validation was repeated 1000 times to develop the EL model. Shapley Additive Explanations was used to analyze feature contributions, and the model was implemented as a web calculator. The primary outcome was the occurrence of intraoperative HI, evaluated by area under the curve (AUC), sensitivity, specificity, and calibration.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Of 45 variables, tumor size, preoperative systolic blood pressure, age, fasting plasma glucose, and body mass index were top predictors. The developed EL model achieved AUC, sensitivity, and specificity values of 0.886, 0.776, and 0.836 and 0.744, 0.733, and 0.667 in Training set and external validations, respectively. Higher SBP (\u2265 125 mmHg), larger tumor size (\u2265 60 mm), older age (\u2265 55 years), higher FPG (\u2265 6 mmol\/L), and BMI &amp;lt;22 or &amp;gt;30 kg\/m\u00b2 increased HI risk. The model demonstrated strong calibration and is accessible at\n                      <jats:ext-link>http:\/\/60.205.91.235\/<\/jats:ext-link>\n                      .\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>This study identified five key predictors of intraoperative HI in patients with pheochromocytoma. The developed EL model provides an accurate, clinically applicable HI risk estimation tool, potentially improving clinical management.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fendo.2025.1670909","type":"journal-article","created":{"date-parts":[[2025,12,12]],"date-time":"2025-12-12T06:34:42Z","timestamp":1765521282000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Ensemble learning prediction model for intraoperative hemodynamic instability in patients with 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