Development and validation of a predictive model for intraoperative hypothermia in elderly patients undergoing craniocerebral tumor resection: a retrospective cohort study.

OBJECTIVE: Timely identification of elderly patients who are at risk of developing intraoperative hypothermia (IH) is imperative to enable appropriate interventions. This study aimed to develop a nomogram for predicting the risk of IH in elderly patients undergoing resection of craniocerebral tumor, and to validate its effectiveness.

METHODS: Elderly patients who underwent craniocerebral tumor resection at a large tertiary hospital in eastern China between January 2019 and December 2022 were included (n=988). The study population was divided into a training set and a validation set by time period. Risk factors identified through the Least Absolute Shrinkage and Selection Operator Method and logistic regression analysis were used to establish the nomogram. The model was validated internally by Bootstrap method and externally by validation set through receiver operating characteristic (ROC) curve analysis, Hosmer-Lemeshow test, and decision curve analysis (DCA).

RESULTS: A total of 273 (27.6%) patients developed IH. Duration of anesthesia (P<0.001), blood loss (P<0.001), preoperative temperature (P<0.001), tumor location (P<0.001), age (P<0.05), and mean arterial pressure (P<0.05) were identified as independent risk factors for IH. A nomogram integrating these six factors was constructed. The area under the ROC curve was 0.773 (95% CI: 0.735-0.811) (70.5% specificity and 75.0% sensitivity), indicating good predictive performance. The DCA demonstrated the clinical benefit of using the nomogram.

CONCLUSIONS: Our model showed good performance in identifying elderly patients who are at high risk of developing IH during craniocerebral tumor resection. The nomogram can help inform timely preventive interventions.