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 leve

SUBMITTER: Wallisch C 

PROVIDER: S-EPMC8595364 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature