Construction and application of inadvertent perioperative hypothermia prediction model for patients under general anesthesia based on deep learning
10.3760/cma.j.issn.1671-0282.2022.08.017
- VernacularTitle:基于深度学习的全身麻醉患者围术期非计划低体温预测模型的构建与应用
- Author:
Haiyan XIANG
1
;
Lifeng HUANG
;
Weiming QIAN
;
Fengjie ZHU
;
Hao ZHANG
;
Zhangli LU
Author Information
1. 浙江大学医学院附属第二医院护理部，杭州 310009
- Keywords:
Deep learning;
Artificial intelligence;
Back propagation;
Operation;
Hypothermia;
Prediction model;
Anaesthesia;
Anesthetic resuscitation
- From:
Chinese Journal of Emergency Medicine
2022;31(8):1116-1120
- CountryChina
- Language:Chinese
-
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.
