Journal of Shandong University (Health Sciences) ›› 2025, Vol. 63 ›› Issue (8): 94-102.doi: 10.6040/j.issn.1671-7554.0.2024.1217

• Clinical Research • Previous Articles    

A Bayesian network-based risk prediction study of stroke in patients with type 2 diabetes mellitus

CHEN Yingying1,2,3, WANG Lu4, HU Xifeng2, ZHU Gaopei1,2,3, XUE Fuzhong1,2,3   

  1. 1. Department of Medical Dataology, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan 250012, Shandong, China;
    2. National Institute of Health and Medical Big Data, Jinan 250003, Shandong, China;
    3. Qilu Hospital of Shandong University, Jinan 250012, Shandong, China;
    4. National Administration of Health Data, Jinan 250002, Shandong, China
  • Published:2025-08-25

PDF (PC)

68

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

CLC Number: 

  • R730.1
[1] Bellary S, Kyrou I, Brown JE, et al. Type 2 diabetes mellitus in older adults: clinical considerations and management[J]. Nat Rev Endocrinol, 2021, 17(9): 534-548.
[2] 李建辉, 徐波. 老年人Ⅱ型糖尿病合并脑卒中危险因素分析[J]. 中国实用医药, 2007, 2(16): 61-62. LI Jianhui, XU Bo. Analysis of risk factors of type 2 diabetes mellitus complicated with stroke in the elderly[J]. China Practical Medicine, 2007, 2(16): 61-62.
[3] Sun H, Saeedi P, Karuranga S, et al. IDF Diabetes Atlas: Global, regional and country-level diabetes prevalence estimates for 2021 and projections for 2045[J]. Diabetes Res Clin Pract, 2022, 183: 109119. doi:10.1016/j.diabres.2021.109119
[4] Graham H, White PL. Social determinants and lifestyles: integrating environmental and public health perspectives[J]. Public Health, 2016, 141: 270-278. doi:10.1016/j.puhe.2016.09.019
[5] Kolari c V, Svir cevic V, Bijuk R, et al. Chronic complications of diabetes and quality of life[J]. Acta Clin Croat, 2022, 61(3): 520-527.
[6] 王富军, 王文琦. 《中国老年2型糖尿病防治临床指南(2022年版)》解读[J]. 河北医科大学学报, 2022, 43(12): 1365-1370. WANG Fujun, WANG Wenqi. Interpretation of clinical guidelines for the prevention and treatment of type 2 diabetes in the elderly in China(2022 edition)[J]. Journal of Hebei Medical University, 2022, 43(12): 1365-1370.
[7] 朱婧, 罗彩凤, 倪益益, 等. 2型糖尿病病人并发缺血性脑卒中急性期血糖波动趋势及影响因素分析[J]. 安徽医药, 2019, 23(12): 2395-2399. ZHU Jing, LUO Caifeng, NI Yiyi, et al. Analysis of the tendency of blood glucose fluctuation and its influencing factors in type 2 diabetic patients with acute ischemic stroke [J]. Anhui Medical and Pharmaceutical Journal, 2019, 23(12): 2395-2399.
[8] Zhou ZE, Lindley RI, Rådholm K, et al. Canagliflozin and stroke in type 2 diabetes mellitus[J]. Stroke, 2019, 50(2): 396-404.
[9] 许俊杰, 陆霞, 张林燕, 等. 嘉兴市城南社区2型糖尿病患者并发脑卒中患病状况及影响因素调查分析[J]. 实用预防医学, 2022, 29(11): 1358-1361.
[10] 吴云虹, 林雅明, 符薇薇, 等. 糖尿病患者并发急性缺血性脑卒中的影响因素研究[J]. 实用心脑肺血管病杂志, 2021, 29(5): 59-63. WU Yunhong, LIN Yaming, FU Weiwei, et al. Influencing factors of diabetes patients complicated with acute ischemic stroke[J]. Practical Journal of Cardiac Cerebral Pneumal and Vascular Disease, 2021, 29(5): 59-63.
[11] 中华医学会糖尿病学分会. 中国2型糖尿病防治指南(2020年版)(上)[J].中国实用内科杂志,2021,41(8): 668-695.
[12] Nathanson MH, Andrzejowski J, Dinsmore J, et al. Guidelines for safe transfer of the brain-injured patient: trauma and stroke, 2019: guidelines from the Association of Anaesthetists and the Neuro Anaesthesia and Critical Care Society[J]. Anaesthesia, 2020, 75(2): 234-246.
[13] Friedman N, Geiger D, Goldszmidt M. Bayesian network classifiers[J]. Mach Learning, 1997, 29: 131-163. doi: 10.1023/A:1007465528199
[14] Glover F. Artificial intelligence, heuristic frameworks and tabu search[J]. Manage Decis Econ, 1990, 11(5): 365-375.
[15] 陈凯庭, 安艳荣, 董学勤, 等. 新诊断2型糖尿病患者采用短期胰岛素泵强化治疗的效果[J]. 中国老年学杂志, 2021, 41(5): 945-948. CHEN Kaiting, AN Yanrong, DONG Xueqin, et al. Effect of short-term insulin pump intensive treatment on newly diagnosed type 2 diabetes mellitus patients[J]. Chinese Journal of Gerontology, 2021, 41(5): 945-948.
[16] 全会标, 高勇义, 李唐瑛. 老年2型糖尿病合并脑卒中危险因素分析[J]. 中国热带医学, 2012, 12(5): 579-581. QUAN Huibiao, GAO Yongyi, LI Tangying. Risk factors associated with cerebral stroke in seniole type 2 daibetes militus patients[J]. China Tropical Medicine, 2012, 12(5): 579-581.
[17] 张竞文, 陈頔, 赵紫楠, 等. 依达拉奉治疗糖尿病合并急性缺血性脑卒中的有效性和安全性系统评价[J]. 中国药业, 2022, 31(3): 108-114. ZHANG Jingwen, CHEN Di, ZHAO Zinan, et al. Efficacy and safety of edaravone in the treatment of diabetes mellitus complicated with acute ischemic stroke: a systematic review[J]. China Pharmaceuticals, 2022, 31(3): 108-114.
[18] 黄永锋, 罗若佳, 范思铭. 胰岛素泵强化甲巯咪唑治疗2型糖尿病合并甲状腺功能亢进症的疗效及预后分析[J]. 吉林医学, 2021, 42(1): 90-92. HUANG Yongfeng, LUO Ruojia, FAN Siming. Efficacy and prognosis analysis of insulin pump strengthening methimazole in the treatment of type 2 diabetes mellitus complicated with hyperthyroidism[J]. Jilin Medical Journal, 2021, 42(1): 90-92.
[19] 尤艺, 韩迪. 2型糖尿病合并非酒精性脂肪肝与缺血性脑血管病的关系分析[J]. 临床医学进展, 2024, 14(3): 1504-1513. YOU Yi, HAN Di. Analysis of the relationship between type 2 diabetes mellitus with nonalcoholic fatty liver disease and ischemic cerebrovascular disease[J]. Advances in Clinical Medicine, 2024, 14(3): 1504-1513.
[20] 宗铮. T2DM与认知功能的相关危险因素分析[D]. 大连:大连医科大学, 2021.
[21] 周新华, 邱伟, 范改焕. T2DM患者肾脏病变与血脂代谢的相关性[J]. 医药论坛杂志, 2020, 41(11): 88-91. ZHOU Xinhua, QIU Wei, FAN Gaihuan. Correlation between renal lesions and lipid metabolism in patients with T2DM [J]. Journal of Medical Forum, 2020, 41(11): 88-91.
[22] 庞宗然, 苏晓慧, 刘祖涵, 等. 微循环障碍与糖尿病及其并发症关系[J]. 时珍国医国药, 2011, 22(4): 988-989. PANG Zongran, SU Xiaohui, LIU Zuhan, et al. Relationship between microcirculation disturbance and diabetes mellitus and its complication[J]. Lishizhen Medicine and Materia Medica Research, 2011, 22(4): 988-989.
[23] 陈英, 张之福, 陈炜, 等. 高血糖对脑卒中预后的影响分析[J]. 中国医药科学, 2012, 2(21): 70-71. CHEN Ying, ZHANG Zhifu, CHEN Wei, et al. Influence analysis of hyperglycemia and cerebral apoplexy prognosis[J]. China Medicine and Pharmacy, 2012, 2(21): 70-71.
[24] 胡耀凯, 韩雄. 前后循环动脉粥样硬化性脑卒中危险因素的比较[J]. 中国实用医刊, 2014, 41(9): 70-71.
[25] 孔伟. 前循环与后循环缺血性卒中的危险因素比较[D]. 青岛: 青岛大学, 2012.
[26] 朱秋荣, 徐惠庆, 骆田斌, 等. 2型糖尿病患者慢性并发症与脑卒中发病的关系[J]. 预防医学, 2017, 29(4): 351-354. ZHU Qiurong, XU Huiqing, LUO Tianbin, et al. A study on the relationship between chronic complications of type 2 diabetic and stroke incidence[J]. Preventive Medicine, 2017, 29(4): 351-354.
[27] 高尚艳. 血糖波动对急性脑卒合并糖尿病患者神经功能预后影响[J]. 糖尿病新世界, 2018, 21(10): 41-42. GAO Shangyan. Effect of blood glucose fluctuation on that prognosis of neurological function in patient with acute stroke complicated with diabetes mellitus[J]. Diabetes New World, 2018, 21(10): 41-42.
[28] Huang YS, Lee CC, Chang TS, et al. Increased risk of stroke in young head and neck cancer patients treated with radiotherapy or chemotherapy[J]. Oral Oncol, 2011, 47(11): 1092-1097.
[29] Guo YJ, Chang MH, Chen PL, et al. Predictive value of plasma(D)-dimer levels for cancer-related stroke: a 3-year retrospective study[J]. J Stroke Cerebrovasc Dis, 2014, 23(4): e249-54.
[30] Kim JM, Jung KH, Park KH, et al. Clinical manifestation of cancer related stroke: retrospective case-control study[J]. J Neurooncol, 2013, 111(3): 295-301.
[31] Lanterna LA, Galliani S, Zangari R, et al. Thyroid autoantibodies and the clinical presentation of moyamoya disease: a prospective study[J]. J Stroke Cerebrovasc Dis, 2018, 27(5): 1194-1199.
[32] 潘建丹, 林芝, 赵秋, 等. 中老年急性出血性脑卒中并发认知功能障碍调查及相关因素分析[J]. 实用预防医学, 2022, 29(2): 245-248. PAN Jiandan, LIN Zhi, ZHAO Qiu, et al. Investigation on cognitive dysfunction and analysis of its correlative factors in middle-aged and elderly patients with acute hemorrhagic stroke[J]. Practical Preventive Medicine, 2022, 29(2): 245-248.
[33] Bergman M, Buysschaert M, Medina JL, et al. Remission of T2DM requires early diagnosis and substantial weight reduction[J]. Nat Rev Endocrinol, 2022, 18(6): 329-330.
[1] SHEN Lujia, LU Tianwei, GONG Weiming, ZHAO Yansong, WANG Shukang, YUAN Zhongshang. Application of metabolomic risk score in predicting cardiovascular outcomes in patients with type 2 diabetes mellitus [J]. Journal of Shandong University (Health Sciences), 2025, 63(8): 69-78.
[2] LI Qian, YANG Fan, XUE Fuzhong. Multi-cancer risk prediction model based on multi-modal data fusion [J]. Journal of Shandong University (Health Sciences), 2025, 63(8): 79-85.
[3] WANG Liyun, GAO Tianqin, LIU Yujia, CHEN Qing, CHEN Liu, SHA Kaihui. Development and validation of a postpartum stress urinary incontinence risk prediction model based on machine learning [J]. Journal of Shandong University (Health Sciences), 2025, 63(6): 55-66.
[4] SONG Sihao, CHENG Chuanlong, LI Shufen, XI Rui, LIANG Kemeng, NI Zhisong, CUI Feng, LI Xiujun. Short-term effects of air pollution on the year of life lost due to ischemic stroke and the modifying effects of extreme temperature events in Zibo City [J]. Journal of Shandong University (Health Sciences), 2025, 63(2): 84-94.
[5] LIU Lin, WANG Xiaonan, YANG Yaxi, WANG Jiangteng, LI Xu, ZHOU Xinli, GUAN Qingbo, ZHANG Xu. Correlation of triglycride-glucose index and atherosclerotic stenosis in intracranial arteries [J]. Journal of Shandong University (Health Sciences), 2024, 62(8): 93-100.
[6] ZHANG Botao, ZHANG Shuaijie, SUN Shuangshuang, YUAN Ying, HU Xifeng, JIA Xiaofeng, YU Yuanyuan, XUE Fuzhong. Development of the Bayesian network-based screening model for ischemic stroke [J]. Journal of Shandong University (Health Sciences), 2024, 62(11): 73-84.
[7] LI Chenshu, WANG Ruihua, LU Xinwu. Research progress in neurological complications of non-A non-B aortic dissection treated with different endovascular techniques [J]. Journal of Shandong University (Health Sciences), 2024, 62(11): 1-7.
[8] LIU Jing, CHEN Chen, WANG Yanwen, CUI Liangliang, HAN Dandan, LI Tiantian. Evaluation on heat-health risk warning in Jinan based on Baidu heat stroke search index [J]. Journal of Shandong University (Health Sciences), 2023, 61(6): 103-108.
[9] LI Jinquan, GAO Meifang, YAN Fei, DONG Ming. Frequency and risk factors of muscle cramp in 136 cases of type 2 diabetes mellitus [J]. Journal of Shandong University (Health Sciences), 2023, 61(5): 20-24.
[10] ZHONG Lu, XUE Fuzhong. A Lung cancer risk prediction model based on Bayesian network uncertainty inference [J]. Journal of Shandong University (Health Sciences), 2023, 61(4): 86-94.
[11] LI Xi, WANG Bingxiang, LI Na, CAO Lina, LI Aihua, GUAN Xiao, ZHANG Zhimian. Effects of lower limb exoskeleton robot rehabilitation training on lower limb motion of hemiplegic patients after stroke [J]. Journal of Shandong University (Health Sciences), 2023, 61(3): 121-126.
[12] HAN Mei, MENG Weijing, TAO Zikun, YANG Xi, XU Yaqi, MU Huaxia, BU Weixiao, WANG Suzhen, SHI Fuyan. Causal mediation analysis with multiple-mediator of hypertension and depression between type 2 diabetes mellitus and cognitive function based on G-computation [J]. Journal of Shandong University (Health Sciences), 2023, 61(10): 101-108.
[13] FANG Qidi, YANG Shuxia, QI Chang, CHENG Chuanlong, HAN Chuang, LIU Ying,CUI Feng, LI Xiujun. Spatio-temporal distribution of stroke in Zibo City in 2019 based on township scale [J]. Journal of Shandong University (Health Sciences), 2022, 60(2): 81-88.
[14] ZHAO Xuan, LI Xiaopeng, LI Jian, TIAN Bin, WANG Guangjun. Therapeutic effects of auricular points sticking with magnetic beads combined with repeated transcranial magnetic stimulation on post-stroke depression [J]. Journal of Shandong University (Health Sciences), 2022, 60(1): 65-70.
[15] LYU Li, JIANG Lu, CHEN Shihong, ZHUANG Xianghua, SONG Yuwen, WANG Dianhui, AN Wenjuan, LI Qian, PAN Zhe. Related factors of osteoporosis in 210 postmenopausal women with type 2 diabetes mellitus [J]. Journal of Shandong University (Health Sciences), 2021, 59(7): 19-25.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
Copyright 2018 © Editorial office of Journal of Shandong University (Health Sciences)
Supported by Beijing Magtech Co.Ltd