Development and internal validation of an algorithm to predict intraoperative risk of inadvertent hypothermia based on preoperative data

Abstract

Intraoperative hypothermia increases perioperative morbidity and identifying patients at risk preoperatively is challenging. The aim of this study was to develop and internally validate prediction models for intraoperative hypothermia occurring despite active warming and to implement the algorithm in an online risk estimation tool. The final dataset included 36,371 surgery cases between September 2013 and May 2019 at the Vienna General Hospital. The primary outcome was minimum temperature measured during surgery. Preoperative data, initial vital signs measured before induction of anesthesia, and known comorbidities recorded in the preanesthetic clinic (PAC) were available, and the final predictors were selected by forward selection and backward elimination. Three models with different levels of information were developed and their predictive performance for minimum temperature below 36 °C and 35.5 °C was assessed using discrimination and calibration. Moderate hypothermia (below 35.5 °C) was observed in 18.2% of cases. The algorithm to predict inadvertent intraoperative hypothermia performed well with concordance statistics of 0.71 (36 °C) and 0.70 (35.5 °C) for the model including data from the preanesthetic clinic. All models were well-calibrated for 36 °C and 35.5 °C. Finally, a web-based implementation of the algorithm was programmed to facilitate the calculation of the probabilistic prediction of a patient’s core temperature to fall below 35.5 °C during surgery. The results indicate that inadvertent intraoperative hypothermia still occurs frequently despite active warming. Additional thermoregulatory measures may be needed to increase the rate of perioperative normothermia. The developed prediction models can support clinical decision-makers in identifying the patients at risk for intraoperative hypothermia and help optimize allocation of additional thermoregulatory interventions.

Construction and validation of postoperative hypothermia prediction model for patients undergoing joint replacement surgery

“…At present, most medical staff has recognised the harm of perioperative hypothermia, and they gradually started to use the predictive model and take early preventive measures to prevent perioperative hypothermia. Studies have shown that the intraoperative prediction model of patients undergoing general anaesthesia surgery can effectively predict and prevent the occurrence of intraoperative hypothermia in such patients (Wallisch et al, 2021; Yi & Zhan et al, 2017). For example, the prediction model of hypothermia during caesarean section constructed by Desgranges (Desgranges et al, 2017) addressed the prediction and intervention of hypothermia during caesarean section.…”

“…Hosmer–Lemeshow can test the model's goodness of fit, and the AUC value (Area under the ROC curve) is used to evaluate the pros and cons of the model (Benhalima et al, 2020; Damen et al, 2019). In recent years, intraoperative hypothermia risk prediction models have been explored in laparoscopic surgery, caesarean section, general anaesthesia and other surgical fields (Desgranges et al, 2017; Wallisch et al, 2021; Yi & Zhan et al, 2017). However, patients undergoing joint replacement surgery are in their old age and receive mainly spinal anaesthesia; additionally, the surgery needs a large incision.…”

Predicting Intraoperative Hypothermia Burden during Non-Cardiac Surgery: A Retrospective Study Comparing Regression to Six Machine Learning Algorithms

“…Recently, it has been demonstrated that intraoperative body temperatures even as low as 35.5 °C were not associated with adverse outcomes [ 13 ]. We recently showed that hypothermia defined by body temperature falling below thresholds of 36 °C and 35.5 °C at least at a single time point can be predicted with acceptable performance using logistic regression [ 14 ]. However, the definition of hypothermia used in this study may be imprecise when applied to our dataset, as nearly half of all patients in our cohort crossed the <36 °C threshold, even though average body temperature during anesthesia was above >36°.…”

“…Estimating the risk of inadvertent hypothermia could enable the targeted use of thermal care interventions and thereby optimize the use of these potentially limited resources. We recently developed several regression models to predict intraoperative hypothermia of less than 36 °C and less than 35.5 °C with sufficient accuracy [ 14 ]. However, in this study, hypothermia was defined as point incidence of body temperature below two fixed and arbitrary thresholds.…”

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

“…Low preoperative baseline body temperature is the most related factor for intraoperative hypothermia. After general anesthesia, patients with low preoperative baseline body temperature are more likely to develop hypothermia due to redistribution of body temperature [ [19] , [20] , [21] , [22] , [23] ].…”