基于贝叶斯网络构建全身麻醉苏醒期谵妄风险预测模型

Construction of a risk prediction model of delirium during general anesthesia recovery based on Bayesian network

目的构建全身麻醉苏醒期谵妄的贝叶斯网络风险预测模型。探索全身麻醉苏醒期谵妄及其相关因素间的网络关系,通过网络推理反映各因素对全身麻醉苏醒期谵妄的影响强度。方法采用横断面研究方法。便利抽样法选取2022年2—5月山西医科大学第一医院麻醉恢复室的全身麻醉患者为研究对象,采用中文版4项谵妄快速诊断方案开展苏醒期谵妄筛查项目,收集研究对象的一般资料和血标本实验室检查结果。采用单因素分析筛选苏醒期谵妄的相关因素并构建基于最大最小爬山法(MMHC)的贝叶斯网络模型。结果共纳入研究对象480例,全身麻醉苏醒期谵妄的发生率为12.9%(62/480)。苏醒期谵妄的贝叶斯网络由11个节点和18条有向边构成。贝叶斯网络显示,年龄、血钠值、合并脑梗死、低蛋白血症为苏醒期谵妄的直接相关因素,ASA分级、红细胞压积、血红蛋白为苏醒期谵妄的间接相关因素。其ROC曲线下面积为0.80(0.78~0.83)。结论贝叶斯网络能很好的揭示苏醒期谵妄及其相关因素之间的复杂网络联系,进而有针对性的对苏醒期谵妄进行预防控制。

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.