ABSTRACT
Objective:To construct a prediction model of inadvertent perioperative hypothermia in patients under general anesthesia , and to apply to clinic to verify its performance.Methods:The data of 19 068 surgical patients in a Grade Ⅲ Class A hospital in Zhejiang Province from January 2016 to September 2020 were included. The model was constructed by using artificial intelligence technology based on deep learning, and the prediction effect of the model was tested by using the area under the subject operating characteristic curve and decision curve. Totally 2 157 surgical patients were included from October 2020 to March 2021 to test the prediction accuracy of the model.Results:The incidence of hypothermia was 13.89% (2 649/19 068) in the modeling group and 14.18% (306/2 157) in the validation group. The area under the subject operating characteristic curve of the prediction model was 0.724 (95% CI: 0.707-0.741), the sensitivity was 0.516, the specificity was 0.823, the cut-off value was 0.175, and the accuracy of practical application was 79.54%. Conclusions:This model can stably predict the incidence of perioperative inadvertent hypothermia in patients under general anesthesia, and provide reference for clinical prevention of inadvertent perioperative hypothermia.
ABSTRACT
The business process reengineering is accomplished by setting up at the operation room a pre-op ready room,an anesthesia induction room,and an anesthetic recovery room.With the aid of the computerized management system,a system platform is built to connect anesthetists,blood bank and pathology lab.This can optimize operation room management,shorten turn-over time before operations,improve efficiency,and cut back hospital costs for an all-win outcome.