Related Experiment Video
Updated: Jun 23, 2025
Short-Duration Hypothermia Induction in Rats using Models for Studies examining Clinical Relevance and Mechanisms
Published on: March 3, 2021
Construction of a Risk Prediction Model for Intraoperative Hypothermia in Patients Undergoing Lower Extremity Joint
Ting Li1, Lihong Chen1, Yanting Shi1
1Department of Operating Room, Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan, People's Republic of China.
Purpose:
To investigate the influencing factors of intraoperative hypothermia (IOH) in patients undergoing total joint arthroplasty (TJA) of the lower extremities, establish a risk prediction model, and test the effect of application.
Design:
A prospective, observational study was conducted.
Methods:
Patients who underwent total knee arthroplasty and total hip arthroplasty from June 2020 to December 2021 were prospectively analyzed. According to the occurrence of IOH, patients were divided into the IOH group (temperature less than 36 °C) and non-IOH group (temperature ≥36 °C). We collected demographic, anesthesia, and surgical data for both groups to identify risk factors for IOH and develop a predictive model. The model's goodness of fit was assessed using the Hosmer-Lemeshow test, and its predictive efficacy was evaluated using the receiver operating characteristic curve.
Findings:
A total of 258 patients were included in this study, with 79 patients in the IOH group and 179 patients in the non-IOH group. Logistic regression analysis showed that American Society of Anesthesiologists' grade, blood loss, and duration of surgery were independent risk factors for IOH in lower extremity TJA patients. Hosmer-Lemeshow test P = .803, area under receiver operating characteristic curve was 0.846, Youden index was 0.490, sensitivity was 65.4%, specificity was 83.6%. In the external validation cohort, the application accuracy of the model was 83.3%.
Conclusions:
The prediction model established in this study is suitable for the risk assessment of IOH in TJA patients with good prediction effect, which can provide a tool for clinical medical staff to identify high-risk populations preoperatively.
