{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T06:11:04Z","timestamp":1783923064489,"version":"3.55.0"},"reference-count":35,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T00:00:00Z","timestamp":1783900800000},"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 is common in video-assisted thoracic surgery (VATS) and can impair postoperative recovery. Most existing studies rely on static cross-sectional analyses, lacking dynamic descriptions of temperature changes and interactive risk factors.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>To investigate intraoperative hypothermia trajectories in VATS patients and provide evidence for predictive temperature management.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      A retrospective analysis was performed on the medical records of 571 patients who underwent thoracoscopic surgeries and satisfied the inclusion criteria at a tertiary hospital between January 2022 and December 2023. The patients were categorized into three distinct temperature groups based on their intraoperative hypothermia status: the normothermic group (\n                      <jats:italic>n<\/jats:italic>\n                      = 138), the hypothermia recovery group (\n                      <jats:italic>n<\/jats:italic>\n                      = 136), and the hypothermia non-recovery group (\n                      <jats:italic>n<\/jats:italic>\n                      = 297). Univariate analysis and logistic regression were employed to identify factors influencing temperature variations. Local weighted regression analysis was conducted on continuous intraoperative temperature monitoring data using Python 3.13, and Matplotlib was utilized to plot the fitted mean curves representing the three temperature trajectories.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Result<\/jats:title>\n                    <jats:p>\n                      No significant differences were found in gender, age, surgery duration, anesthesia duration, Intraoperative blood loss, preoperative body temperature (\n                      <jats:italic>P<\/jats:italic>\n                      &amp;gt; 0.05). Multinomial logistic regression showed that each 500 mL increase in intraoperative infusion volume significantly increased the risk of being in the hypothermia-recovered group (OR = 1.570,\n                      <jats:italic>P<\/jats:italic>\n                      &amp;lt; 0.001) and the hypothermia-nonrecovered group (\n                      <jats:italic>OR<\/jats:italic>\n                      = 1.305,\n                      <jats:italic>P<\/jats:italic>\n                      = 0.007). Lower BMI was a risk factor only for the nonrecovered group (\n                      <jats:italic>OR<\/jats:italic>\n                      = 0.891,\n                      <jats:italic>P<\/jats:italic>\n                      = 0.003). BSA and urine volume were not significant. Three distinct trajectories were identified: Horizontal \u201c\u222b\u201d (normothermic), \u201c\u221a\u201d (hypothermia recovery), and \u201cL\u201d (hypothermia non-recovery).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>\n                      Individualized perioperative temperature management for VATS patients should be guided by dynamic intraoperative temperature trajectories, infusion requirements, and BMI stratification. Patients with low BMI (&amp;lt; 18.5 kg\/m\n                      <jats:sup>2<\/jats:sup>\n                      ) may have better rewarming capacity once hypothermia develops, while overweight or obese patients (BMI &amp;gt;23.9 kg\/m\n                      <jats:sup>2<\/jats:sup>\n                      ) are at higher risk of entering a non-recovery trajectory. These findings provide a practical basis for stratified hypothermia prevention and lay the groundwork for further precision management research.\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fmed.2026.1860218","type":"journal-article","created":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T05:35:02Z","timestamp":1783920902000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Intraoperative hypothermia risk trajectories in patients undergoing video-assisted thoracoscopic surgery: a retrospective study"],"prefix":"10.3389","volume":"13","author":[{"given":"Rui","family":"Chen","sequence":"first","affiliation":[{"name":"Operating Room, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology","place":["Wuhan, Hubei, 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