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Journal of Nursing ›› 2023, Vol. 30 ›› Issue (11): 1-6.doi: 10.16460/j.issn1008-9969.2023.11.001

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Construction of prognostic model of acute ischemic stroke in elderly individuals based on inflammatory and nutritional indicators

ZHAO Xu, ZHAO Ya-ning, LI Jian-min, LIU Yao, ZHAO Da-ye, CHEN Chang-xiang   

  1. School of Nursing and Rehabilitation, North China University of Science and Technology, Tangshan 063000, China
  • Received:2022-11-02 Online:2023-06-10 Published:2023-07-10

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Abstract: Objective To construct and validate a predictive model for the prognosis of acute ischemic stroke in the elderly based on inflammatory and nutritional indicators. Methods A total of 586 elderly patients with acute ischemic stroke from November 2021 to May 2022 in a tertiary grade-A hospital in Tangshan City were divided into good prognosis group (n=438) and poor prognosis group (n=148). Risk factors were analyzed by LASSO regression with binary logistic regression, and a nomogram was developed and validated. Results Multifactorial analysis showed that advanced age, concurrent chronic diseases, homocysteine >15 mmol/L, lymphocyte to monocyte ratio <3.713, albumin to globulin ratio <1.527, and geriatric nutritional risk index <99.837 were risk factors for poor prognosis in elderly patients with acute ischemic stroke. AUC of the nomogram was 0.774 (95%CI:0.730~0.818); mean absolute error of conformity between the true and predicted value was 0.011 by bootstrap internal validation method; Brier score was 0.152; Hosmer-Lemeshow test P=0.733, and decision curve showed high risk probability value ranged between 0.03 and 0.65. Conclusion The constructed nomogram based on inflammatory and nutritional indicators have high application value and could be used as a tool to predict the poor prognosis of acute ischemic stroke in the elderly.

Key words: ischemic stroke, inflammation, nutrition assessment, model, statistics

CLC Number: 

  • R743
[1] 《中国脑卒中防治报告》编写组.《中国脑卒中防治报告2020》概要[J].中国脑血管病杂志,2022,19(2):136-144.DOI:10.3969/j.issn.1672-5921.2022.02.011.
[2] 吕苏,宋小静,高伟波,等.血小板相关指数与溶栓后急性脑卒中患者预后的关系[J].中华急诊医学杂志, 2021,30(3):301-306.DOI:10.3760/cma.j.issn.1671-0282.2021.03.008.
[3] 崔玉环,颜进项,王海燕,等.预后营养指数可作为老年急性脑梗死患者临床预后的指标[J].中国老年学杂志,2022,42(6):1302-1306.DOI:10.3969/j.issn.1005-9202.2022.06.007.
[4] 中华医学会神经病学分会,中华医学会神经病学分会脑血管病学组.中国各类主要脑血管病诊断要点2019[J].中华神经科杂志, 2019, 52(9):710-715.DOI:10.3760/cma.j.issn.1006-7876.2019.09.003.
[5] 中华医学会神经病学分会,中华医学会神经病学分会脑血管病学组.中国急性缺血性脑卒中诊治指南2018[J].中华神经科杂志, 2018(9):666-682.DOI:10.3760/cma.j.issn.1006-7876.2018.09.004.
[6] 姜逍瑶,赵旭东,鲍磊,等.老年营养风险指数对急性缺血性脑卒中患者预后的预测价值[J].中华老年心脑血管病杂志,2023,25(1):35-38.DOI:10.3969/j.issn.1009-0126.2023.01.010.
[7] 邓朝苹,李丽,代源,等.大面积急性缺血性脑卒中患者血管内治疗短中期预后的影响因素[J].中华老年心脑血管病杂志,2023, 25(2):167-170.DOI:10.3969/j.issn.1009-0126.2023.02.014.
[8] 胆迎,刘晓智,李晨.天津市老年轻型脑卒中与非轻型脑卒中相关危险因素对比及预后[J].中国老年学杂志,2019,39:4899-4902.DOI:10.3969/j.issn.1005-9202.2019.20.007.
[9] Leng YP, Ma YS, Li XG, et al.l-Homocysteine-induced cathepsin V mediates the vascular endothelial inflammation in hyperhomocysteinaemia[J].Br J Pharmacol, 2018,175(8):1157-1172.DOI:10.1111/bph.13920.
[10] Gong P,Liu Y,Gong Y,et al.The association of neutrophil to lymphocyte ratio,platelet to lymphocyte ratio,and lymphocyte to monocyte ratio with post-thrombolysis early neurological outcomes in patients with acute ischemic stroke[J].J Neuroinflammation, 2021, 18(1):51. DOI:10.1186/s12974-021-02090-6.
[11] Li G,Hao Y,Wang C,et al.Association between neutrophil-to-lymphocyte ratio/Lymphocyte-to-monocyte ratio and in-hospital clinical outcomes in ischemic stroke treated with intravenous thrombolysis[J]. J Inflamm Res,2022,15:5567-5578.DOI:10.2147/JIR.S382876.
[12] Kim J Song Tj, Park Jh, et al.Different prognostic value of white blood cell subtypes in patients with acute cerebral infarction[J].Atherosclerosis,2012, 222(2):464-467.DOI:10.1016/j.atherosclerosis.2012.02.042.
[13] 姚辉,马煜盛,吕萍,等.白蛋白球蛋白比值与老年糖尿病患者下肢动脉疾病的关系[J].中华老年心脑血管病杂志,2022, 24(3):233-236.DOI:10.3969/j.issn.1009-0126.2022.03.003.
[14] 苏小平,徐静娟,赵亚东,等.危重患者早期肠内营养喂养不耐受风险预测模型的构建[J].护理学报,2022,29(17): 47-51.DOI:10.16460/j.issn1008-9969.2022.17.047.
[15] 尹克金,栾丽芹,化晓莉,等.营养不良与急性缺血性卒中患者静脉溶栓后早期神经功能恶化的相关性[J].国际脑血管病杂志,2022,30(5):327-332.DOI:10.3760/cma.j.issn.1673-4165.2022.05.002.
[16] Hu J, Chen T, Wang ZY, et al.Geriatric nutritional risk index and the prognosis of patients with stroke: a Meta-analysis[J]. Horm Metab Res ,2022,54(11):736-746.DOI:10.1055/a-1886-4276.
[17] Al Mamun A, Chauhan A,Qi S,et al.Microglial IRF5-IRF4 regulatory axis regulates neuroinflammation after cerebral ischemia and impacts stroke outcomes[J]. Proc Natl Acad Sci USA,2020,117(3):1742-1752.DOI:10.1073/pnas.1914742117.
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