Early prediction of intraoperative hypothermia in patients undergoing gynecological laparoscopic surgery: A retrospective cohort study.

Intraoperative hypothermia is one of the most common adverse events related to surgery, and clinical practice has been severely underestimated. In view of this, this study aims to build a practical intraoperative hypothermia prediction model for clinical decision-making assistance. We retrospectively collected clinical data of patients who underwent gynecological laparoscopic surgery from June 2018 to May 2023, and constructed a multimodal algorithm prediction model based on this data. For the construction of the prediction model, all data are randomly divided into a training queue (70%) and a testing queue (30%), and then 3 types of machine learning algorithms are used, namely: random forest, artificial neural network, and generalized linear regression. The effectiveness evaluation of all predictive models relies on the comprehensive evaluation of the net benefit method using the area under the receiver operating characteristic curve, calibration curve, and decision curve analysis. Finally, 1517 screened patients were filtered and 1429 participants were included for the construction of the predictive model. Among these, anesthesia time, pneumoperitoneum time, pneumoperitoneum flow rate, surgical time, intraoperative infusion, and room temperature were independent risk factors for intraoperative hypothermia and were listed as predictive variables. The random forest model algorithm combines 7 candidate variables to achieve optimal predictive performance in 2 queues, with an area under the curve of 0.893 and 0.887 and a 95% confidence interval of 0.835 to 0.951 and 0.829 to 0.945, respectively. The prediction efficiency of other prediction models is 0.783 and 0.821, with a 95% confidence interval of 0.725 to 0.841 and 0.763 to 0.879, respectively. The intraoperative hypothermia prediction model based on machine learning has satisfactory predictive performance, especially in random forests. This interpretable prediction model helps doctors evaluate the risk of intraoperative hypothermia, optimize clinical decision-making, and improve patient prognosis.