基于贝叶斯网络的2型糖尿病患者并发脑卒中风险预测

doi:10.6040/j.issn.1671-7554.0.2024.1217

摘要/Abstract

摘要： 目的构建简便、经济且适用于临床场景的2型糖尿病(type 2 diabetes mellitus,T2DM)并发脑卒中的预测模型,精确预测糖尿病并发脑卒中的发病风险及相关危险因素。方法基于齐鲁全生命周期电子健康研究型数据库(Cheeloo Lifespan Electronic Health Research Data-library, CHeeloo LEAD)数据,采用单因素Cox回归分析筛选与糖尿病并发脑卒中相关的危险因素,应用联合Cox与贝叶斯网络模型构建风险预测模型,并从鉴别和校准两方面评价模型的预测性能。结果本研究共纳入CHeeloo LEAD中2015年1月1日至2017年10月31日的糖尿病患者15 528例,随防至2023年1月1日,期间发生脑卒中2 552例,单因素分析筛选出67个与脑卒中发病有关的潜在危险因素并用以构建贝叶斯网络,多因素Cox回归模型筛选出个4独立危险因素,分别为年龄、短暂和突发性疾病、循环系统疾病、脑血管病后遗症。联合Cox与贝叶斯网络模型构建T2DM并发脑卒中的预测模型,预测个体3年发生脑卒中的风险,训练集AUC为0.814,测试集AUC为0.816,结果基本一致。结论 T2DM患者中,年龄、短暂和突发性疾病、循环系统疾病、脑血管病后遗症是导致脑卒中风险增加的重要危险因素。在临床实践中,应重视T2DM患者脑部病变的发生,识别相关的危险因素,加强监测和管理,以减少脑卒中的发生率并改善患者的预后。

关键词: 2型糖尿病, 脑卒中, 贝叶斯网络, Cox回归模型, 风险预测

Abstract: Objective To construct a simple, economical and clinically applicable prediction model for T2DM complicated stroke, so as to accurately predict the risk of diabetes mellitus and stroke and obtain relevant risk factors. Methods Based on the data of Cheeloo Lifespan Electronic Health Research Data-library(CHeeloo LEAD), univariate Cox regression analysis was used to screen the risk factors associated with diabetes mellitus complicated with stroke, and the combined Cox and Bayesian network models were used to construct a risk prediction model, then the prediction performance of the model was evaluated from two aspects of identification and calibration. Results A total of 15,528 diabetic patients in CHeeloo LEAD database from January 1, 2015 to October 31, 2017 were included in this study, and 2,552 cases of stroke occurred from January 1, 2015 to January 1, 2023. Sixty-seven potential risk factors related to stroke were screened out by univariate analysis and used to construct Bayesian networks, and 4 independent risk factors, including age, transient and sudden-onset diseases, circulatory system diseases, and sequelae of cerebrovascular diseases, were screened by multivariate Cox regression model. Combined with the Cox model and Bayesian network model, the prediction model of T2DM complicated stroke was constructed to predict the risk of stroke in individuals in 3 years, and the AUC of the training set and test set were 0.814 and 0.816, respectively, exhibiting the basically consistent results. Conclusion Age, transient and sudden-onset diseases, circulatory diseases, and sequelae of cerebrovascular diseases are important risk factors for the increased risk of stroke in patients with T2DM. In clinical practice, attention should be paid to the occurrence of brain lesions in patients with T2DM. The identification of relevant risk factors and the strengthening of monitoring and management should be carried out to reduce the incidence of stroke and improve the prognosis of patients.

Key words: Type 2 diabetes mellitus, Stroke, Bayesian networks, Cox regression model, Risk prediction

中图分类号:

R730.1

陈莹莹,王鲁,胡锡峰,朱高培,薛付忠. 基于贝叶斯网络的2型糖尿病患者并发脑卒中风险预测[J]. 山东大学学报 (医学版), 2025, 63(8): 94-102.

CHEN Yingying, WANG Lu, HU Xifeng, ZHU Gaopei, XUE Fuzhong. A Bayesian network-based risk prediction study of stroke in patients with type 2 diabetes mellitus[J]. Journal of Shandong University (Health Sciences), 2025, 63(8): 94-102.

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