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.
Highlights
Intraoperative hypothermia increases perioperative morbidity and identifying patients at risk preoperatively is challenging
Several patient characteristics have been associated with perioperative hypothermia in smaller-scale studies, including sex, age, body mass index (BMI), diabetes, and hypothyroidism[12,23,24,25], and a mixture of various pre- and intraoperative factors were identified in a larger-scale study[26]
We developed three multivariable prediction models for hypothermia based on different levels of information that are available, since healthcare professionals have different degrees of knowledge about their patients at different timepoints
Summary
Intraoperative hypothermia increases perioperative morbidity and identifying patients at risk preoperatively is challenging. 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. 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. Numerous potential causes of inadvertent perioperative hypothermia have been discussed They can be subdivided into surgical factors, anesthesiologic factors, environmental factors (e.g., operating room temperature), and patient c haracteristics[11,12]. Several patient characteristics have been associated with perioperative hypothermia in smaller-scale studies, including sex, age, body mass index (BMI), diabetes, and hypothyroidism[12,23,24,25], and a mixture of various pre- and intraoperative factors were identified in a larger-scale study[26].
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