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 al...

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Autores principales:	C. Wallisch, S. Zeiner, P. Scholten, C. Dibiasi, O. Kimberger
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/b650991b22b34af68f3657221016bd61
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spelling	oai:doaj.org-article:b650991b22b34af68f3657221016bd612021-11-21T12:16:40ZDevelopment and internal validation of an algorithm to predict intraoperative risk of inadvertent hypothermia based on preoperative data10.1038/s41598-021-01743-z2045-2322https://doaj.org/article/b650991b22b34af68f3657221016bd612021-11-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-01743-zhttps://doaj.org/toc/2045-2322Abstract 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.C. WallischS. ZeinerP. ScholtenC. DibiasiO. KimbergerNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q C. Wallisch S. Zeiner P. Scholten C. Dibiasi O. Kimberger Development and internal validation of an algorithm to predict intraoperative risk of inadvertent hypothermia based on preoperative data
description	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.
format	article
author	C. Wallisch S. Zeiner P. Scholten C. Dibiasi O. Kimberger
author_facet	C. Wallisch S. Zeiner P. Scholten C. Dibiasi O. Kimberger
author_sort	C. Wallisch
title	Development and internal validation of an algorithm to predict intraoperative risk of inadvertent hypothermia based on preoperative data
title_short	Development and internal validation of an algorithm to predict intraoperative risk of inadvertent hypothermia based on preoperative data
title_full	Development and internal validation of an algorithm to predict intraoperative risk of inadvertent hypothermia based on preoperative data
title_fullStr	Development and internal validation of an algorithm to predict intraoperative risk of inadvertent hypothermia based on preoperative data
title_full_unstemmed	Development and internal validation of an algorithm to predict intraoperative risk of inadvertent hypothermia based on preoperative data
title_sort	development and internal validation of an algorithm to predict intraoperative risk of inadvertent hypothermia based on preoperative data
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/b650991b22b34af68f3657221016bd61
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Development and internal validation of an algorithm to predict intraoperative risk of inadvertent hypothermia based on preoperative data

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