Systematic Review of the Intraoperative Hypothermia Risk Prediction Models in Total Joint Arthroplasty Patients

Machine learning has been widely applied to the prediction of intraoperative hypothermia (IH) risk in surgical patients. Compared to traditional scoring systems, machine learning methods can more comprehensively identify risk factors for hypothermia, providing effective clinical guidance for nursing. The incidence of hypothermia in total joint arthroplasty (TJA) patients remains high, warranting attention. Although prediction models developed by researchers have shown promising results, the quality of these models still requires scrutiny. This review aims to evaluate existing intraoperative hypothermia risk prediction models in TJA patients, focusing on the development quality and predictive performance of these models.