# Construction and validation of a predictive model for hypothermia complication during endoscopic thyroidectomy for thyroid cancer

## TL;DR

This study creates a model to predict hypothermia risk during endoscopic thyroid cancer surgery, helping nurses identify high-risk patients.

## Contribution

A novel predictive model for intraoperative hypothermia in endoscopic thyroidectomy, validated for clinical utility.

## Key findings

- Infusion volume and irrigation fluid temperature are independent risk factors for intraoperative hypothermia.
- BMI is an independent protective factor against intraoperative hypothermia.
- The model showed excellent predictive accuracy (AUC = 0.945) and maintained strong performance in external validation (AUC = 0.831).

## Abstract

Intraoperative hypothermia frequently occurs during surgery and can negatively impact patient outcomes. The study focuses on establishing a clinical prediction model to identify the risk of intraoperative hypothermia in patients undergoing endoscopic thyroidectomy for thyroid cancer.

Univariate analysis was performed to identify potential indicators associated with intraoperative hypothermia. Multivariable logistic regression analysis was employed to select the independent predictors for model construction. The predictive performance and clinical utility of the model were assessed using receiver operating characteristic (ROC) curve, calibration plots and decision curve analysis (DCA). External validation was conducted to evaluate its generalizability.

Univariate analysis revealed that age, body mass index (BMI), anesthesia duration, duration of surgery, infusion volume, intraoperative irrigation volume, irrigation fluid temperature and intraoperative blood loss were significantly associated with the occurrence of intraoperative hypothermia (all P < 0.05). Multivariate logistic regression analysis identified infusion volume and irrigation fluid temperature were independent risk factors for intraoperative hypothermia in patients undergoing endoscopic radical thyroidectomy for thyroid cancer, whereas BMI was an independent protective factor (P < 0.05). ROC curve indicated excellent predictive accuracy of the model (AUC = 0.945). The calibration plot demonstrated a high degree of concordance between the actual incidence and the predicted probabilities. The results of DCA indicated that this predictive model has high clinical application value. When applied to the validation cohort, the model maintained strong predictive performance and stability, with an AUC of 0.831.

The nomogram model developed in this study exhibits strong predictive performance and high clinical utility in assessing the risk of intraoperative hypothermia among patients undergoing endoscopic thyroid cancer radical surgery, serving as a valuable reference for operating room nurses in identifying high-risk individuals.