Risk factors for intraoperative hypothermia in patients undergoing thoracic spinal surgery: A retrospective cohort study.

ObjectiveThe present study aimed to develop a clinical prediction model for intraoperative hypothermia in patients undergoing thoracic spinal surgery, with the objective of supporting decision-making in clinical care and anesthesia management.MethodsA total of 600 patients were enrolled in this retrospective cohort study. The dataset was randomly divided into training (70% of all data) and validation (30% of all data) sets using sample function in R language. Based on the results of the multivariable binary logistic regression analysis, a nomogram was developed and evaluated using R language. Calibration was performed by comparing the predicted and actual probability curves using the 'rms' package. The 'ggDCA' package was used to construct decision analysis curves for evaluating clinical utility.ResultsA least absolute shrinkage and selection operator binary logistic regression model was constructed to identify 5 potential risk factors from the 14 variables previously identified. These five risk factors, including duration of anesthesia, preoperative body temperature, body mass index, number of diseased vertebrae, and reason for surgery, were then validated using logistic regression models. The concordance index was calculated as 0.919.ConclusionsTumor-related spinal surgery, multilevel procedures, prolonged surgical duration, low preoperative temperature, and low body mass index were associated with a higher risk of intraoperative hypothermia. These factors may help guide targeted warming strategies in patients undergoing thoracic spinal surgery.