Hypothermia; Anesthesia Clinical Trial
Official title:
Construction and Validation of a Risk Prediction Model for Hypothermia After Laparoscopic Gastrointestinal Tumor Surgery Based on Machine Learning
The incidence of postoperative hypothermia in patients with laparoscopic gastrointestinal tumors is high. Hypothermia increases the risk of postoperative complications and medical costs. Early warning can effectively reduce the incidence of postoperative hypothermia in patients. Multivariate prediction models help identify high-risk patients and reversible factors. At present, there are few reports on the risk factors and prediction models of postoperative hypothermia in patients with laparoscopic gastrointestinal tumors. Therefore, this study aims to clarify the risk factors of postoperative hypothermia in patients with laparoscopic gastrointestinal tumors. Four machine learning algorithms, traditional Logistic regression analysis, decision tree, random forest and naive Bayes, were used to establish risk prediction models. According to the TRIPOD statement, C-index, Hosmer-Lemeshow ( H-L ) test and decision curve analysis ( DCA ) were used to evaluate the prediction and fitting effects of the models in all aspects, and the optimal model was selected and verified. Provide reference for subsequent research.
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