{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T05:57:17Z","timestamp":1790575037840,"version":"4.1.0"},"reference-count":31,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T00:00:00Z","timestamp":1790553600000},"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 hypothermia (IOH, temperature &amp;lt;36.0\u2005\u00b0C) is a common complication in gynecological laparoscopic surgery that leads to significant adverse outcomes that would benefit from preoperative risk prediction.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      We developed and compared two predictive models\u2014a conventional multivariable logistic regression (LR) model and a gradient boosting machine (XGBoost) model\u2014to preoperatively predict IOH based on 20 objectively measurable preoperative clinical variables that could be directly extracted from electronic health records (EHRs). The derivation cohort consisted of 460 cases from a tertiary care center in 2024, which were randomly divided into a training set (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u2009322) and a test set (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u2009138) at a 7:3 ratio. For external validation, we used an independent cohort of 183 cases from a second medical institution during the same period. Model performance was evaluated using metrics including the area under the receiver operating characteristic curve (AUROC) and area under the precision-recall curve (AUPRC), with SHapley Additive exPlanations (SHAP) method applied for feature importance analysis.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      We initially analyzed 20 preoperative variables. The final model incorporated eight key indicators: body mass index (BMI), ASA physical status, fasting period, basal body temperature, estimated surgical duration, hypertension status, serum albumin level, and alanine aminotransferase (ALT). The derivation cohort consisted of 460 patients, split into a training set (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u2009322) and a test set (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u2009138). An independent external validation cohort included 183 patients. By AUROC comparison, the XGBoost model outperformed the LR model in both the training set (0.933 vs. 0.891) and the external validation set (0.870 vs. 0.838), while performing slightly lower in the test set (0.821 vs. 0.865).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>We developed and validated an interpretable XGBoost model using eight readily available preoperative clinical indicators. The model demonstrated good performance for the preoperative prediction of IOH in gynecological laparoscopic surgery, which may facilitate early identification of high-risk patients and timely intervention.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fmed.2026.1839255","type":"journal-article","created":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T05:45:07Z","timestamp":1790574307000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Development and validation of a machine learning model for preoperative prediction of intraoperative hypothermia in gynecological laparoscopic surgery: a two-center cohort study"],"prefix":"10.3389","volume":"13","author":[{"given":"Chunping","family":"Li","sequence":"first","affiliation":[{"name":"Department of Anesthesiology, Sichuan Jinxin Xinan Women & Children\u2019s Hospital","place":["Chengdu, Sichuan, 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