To construct and validate an intraoperative hypothermia risk prediction model for elderly patients undergoing total hip arthroplasty (THA).We collected data from 718 patients undergoing THA in a tertiary hospital from January 2021 to December 2022. Of these patients, 512 were assigned to the modeling group from January 2021 to April 2022, and 206 participants were assigned to the validation group from May 2022 to December 2022. A logistic regression analysis was performed to construct the model. The area under the curve (AUC) was used to test the model's predictive ability.The incidence rate of intraoperative hypothermia was 51.67%. The risk factors entered into the risk prediction model were age, preoperative hemoglobin level, intraoperative blood loss, postoperative hemoglobin level, and postoperative systolic blood pressure. The model was constructed as follows: logit (P) = - 10.118 + 0.174 × age + 1.366 × 1 (preoperative hemoglobin level) + 0.555 × 1 (postoperative hemoglobin level) + 0.009 × 1 (intraoperative blood loss) + 0.066 × 1 (postoperative systolic blood pressure). Using the Hosmer-Lemeshow test, the P value was 0.676 (AUC, 0.867). The Youden index, sensitivity, and specificity were 0.602, 0.790, and 0.812, respectively. The incidence rates of intraoperative hypothermia in the modeling and validation groups were 53.15% and 48.06%, respectively. The correct practical application rate was 89.81%. This model had good application potential.This risk prediction model has good predictive value and can accurately predict the occurrence of intraoperative hypothermia in patients who undergo THA, which provides reliable guidance for clinical work and has good clinical application value. 

Construction and validation of a risk prediction model for intraoperative hypothermia in elderly patients undergoing total hip arthroplasty

Prediction models of vaginal birth after cesarean delivery: A systematic review.

The prevalence of unplanned intraoperative hypothermia during thoracoscopic pulmonary tumor resection under general anesthesia is considerable, which may result in numerous adverse reactions.The aim of this study was to develop and validate a nomogram-based prediction model for assessing the risk of intraoperative hypothermia in patients undergoing thoracoscopic pulmonary tumor resection under general anesthesia.This was a retrospective study conducted at a tertiary class A hospital.The study included 506 adult patients who underwent thoracoscopic lung tumor resection under general anesthesia in 2022.The clinical data of 506 patients who underwent thoracoscopic pulmonary tumor surgery from January 1 to December 31, 2022 were collected and randomly divided into the modeling group (n = 356) and the validation group (n = 50). The data of 356 patients were used establish a prediction model for intraoperative hypothermia. A total of 17 factors covering patient demographics, disease characteristics, and surgical conditions were gathered. The least absolute shrinkage and selection operator regression model was utilized to optimize the risk model's features. Multivariate logistic regression analysis was employed to construct the final predictive model.Gender, body mass index, preoperative body temperature and operation time were used as predictors to construct the nomogram. The C-index of the model was 0.831 (95%CI: 0.799-0.863). The C-index of external validation was 0.820, which verified the calibration of the model. Decision curve analysis validated the clinical utility of the nomogram, particularly when using a threshold probability of unplanned intraoperative hypothermia 1 %.-74 %.The nomogram constructed in this study can effectively predict the risk of intraoperative hypothermia in patients undergoing thoracoscopic lung tumor resection under general anesthesia. The nomogram incorporated readily available predictors such as sex, body mass index, preoperative body temperature, and duration of surgery. 

Development and evaluation of a novel predictive nomogram for assessing the risk of intraoperative hypothermia in patients undergoing thoracoscopic pulmonary tumor surgery

Intraoperative hypothermia is one of the most common adverse events related to surgery, and clinical practice has been severely underestimated. In view of this, this study aims to build a practical intraoperative hypothermia prediction model for clinical decision-making assistance. We retrospectively collected clinical data of patients who underwent gynecological laparoscopic surgery from June 2018 to May 2023, and constructed a multimodal algorithm prediction model based on this data. For the construction of the prediction model, all data are randomly divided into a training queue (70%) and a testing queue (30%), and then 3 types of machine learning algorithms are used, namely: random forest, artificial neural network, and generalized linear regression. The effectiveness evaluation of all predictive models relies on the comprehensive evaluation of the net benefit method using the area under the receiver operating characteristic curve, calibration curve, and decision curve analysis. Finally, 1517 screened patients were filtered and 1429 participants were included for the construction of the predictive model. Among these, anesthesia time, pneumoperitoneum time, pneumoperitoneum flow rate, surgical time, intraoperative infusion, and room temperature were independent risk factors for intraoperative hypothermia and were listed as predictive variables. The random forest model algorithm combines 7 candidate variables to achieve optimal predictive performance in 2 queues, with an area under the curve of 0.893 and 0.887 and a 95% confidence interval of 0.835 to 0.951 and 0.829 to 0.945, respectively. The prediction efficiency of other prediction models is 0.783 and 0.821, with a 95% confidence interval of 0.725 to 0.841 and 0.763 to 0.879, respectively. The intraoperative hypothermia prediction model based on machine learning has satisfactory predictive performance, especially in random forests. This interpretable prediction model helps doctors evaluate the risk of intraoperative hypothermia, optimize clinical decision-making, and improve patient prognosis. 

Early prediction of intraoperative hypothermia in patients undergoing gynecological laparoscopic surgery: A retrospective cohort study

In total joint arthroplasty patients, intraoperative hypothermia (IOH) is associated with perioperative complications and an increased economic burden. Previous models have some limitations and mainly focus on regression modeling. Random forest (RF) algorithms and decision tree modeling are effective for eliminating irrelevant features and making predictions that aid in accelerating modeling and reducing application difficulty. 

Cross-Combination Analyses of Random Forest Feature Selection and Decision Tree Model for Predicting Intraoperative Hypothermia in Total Joint Arthroplasty

Risk Prediction Models for Inadvertent Intraoperative Hypothermia: A Systematic Review.

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Risk Prediction Models for Inadvertent Intraoperative Hypothermia: A Systematic Review.