Construction of a risk prediction model of delirium during general anesthesia recovery based on Bayesian network
10.3760/cma.j.cn211501-20230711-01658
- VernacularTitle:基于贝叶斯网络构建全身麻醉苏醒期谵妄风险预测模型
- Author:
Yanmin LI
1
;
Wenzhu SONG
;
Taohong MA
;
Xiang FENG
;
Yuli LIANG
Author Information
1. 山西医科大学第一医院麻醉科，太原 030001
- Keywords:
Delirium;
Awakening period;
Bayesian network;
Related factors;
Risk Prediction Model
- From:
Chinese Journal of Practical Nursing
2023;39(35):2762-2769
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To construct a Bayesian network risk prediction model for delirium during recovery from general anesthesia. To explore the network relationship between awakening delirium of general anesthesia and its related factors, and to reflect the influence intensity of each factor on awakening delirium of general anesthesia through network reasoning.Methods:This is a cross-sectional study. From February to May 2022, the Chinese version of the four rapid delirium diagnosis protocols for general anesthesia patients admitted to the department of Anesthesia, the First Hospital of Shanxi Medical University were adopted as research subjects through convenience sampling method to carry out the delirium screening program during awakening, and general information and blood sample laboratory test results of the subjects were collected. The single factor analysis was used to screen the correlative factors of awakening delirium and a Bayesian network model based on the maximum minimum climb method (MMHC) was constructed.Results:A total of 480 patients were included in the study, and the delirium rate during the recovery period of general anesthesia was 12.9%(62/480). The Bayesian network of awakening delirium consisted of 11 nodes and 18 directed edges. The Bayesian network showed that age, sodium, cerebral infarction and hypoproteinemia were the direct factors related to awakening delirium, while ASA grade, hematocele and hemoglobin were the indirect factors related to awakening delirium. The area under its ROC curve was 0.80(0.78-0.83).Conclusions:Bayesian networks can well reveal the complex network connections between awakening delirium and its related factors, and then prevent and control awakening delirium accordingly.
