Development and Validation of a Predictive Model for Intraoperative Hypothermia in Neonates and Young Children Undergoing General Anesthesia.

Abstract

Purpose: Intraoperative hypothermia increases perioperative morbidity. However, preoperative identification of patients at risk remains challenging. The aim of this study was to develop and validate a predictive model for intraoperative hypothermia in neonates and young children.

Design: This was a retrospective cohort study.

Methods: We collected data from 2,070 participants aged 1 day to 3 years who underwent general anesthesia at a hospital between January 1, 2021, and June 30, 2023. The training set comprised 1,449 cases, while the validation set included 621 cases. Logistic regression was used to construct the model, and the area under the curve (AUC) was used to evaluate the predictive effect of the model. The transparent reporting of a multivariable prediction model for individual prognosis or diagnosis checklist was used to guide the reporting of this study.

Findings: The incidence of intraoperative hypothermia was 42.79% in the training group and 41.87% in the verification group. The prediction model included six key predictors: age, preoperative weight, basal temperature, respiratory rate, duration of anesthesia, and infusion and transfusion volume grouping (mL/kg). The model is presented as a nomogram. The AUC of the training set was 0.767, with a Joden index of 0.411, sensitivity of 0.687, and specificity of 0.724, and the AUC of the validation set was 0.775. The calibration curve exhibited a high level of consistency between the predicted and observed probabilities.

Conclusions: This study developed a predictive model and constructed a nomogram for assessing the risk of intraoperative hypothermia in neonatal and pediatric patients.