以质量求发展，以服务铸品牌

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

    Next Articles

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

PDF (PC)

126

Like

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.
[1] ZHOU Meng-juan, ZHU Xiao-li, ZHANG Tai, DUAN Jian-feng, LUO Yu-mei, MA Wei-li, LU Han, SHI Ting-ting, YANG Yi-lin, LI Ting, CHA Yao-lei, ZHAO Yuan. Construction and verification of predictive model for prolonged length of stay in patients with non-severe acute pancreatitis [J]. Journal of Nursing, 2023, 30(9): 7-12.
[2] YE Lei, ZHANG Ai-qin, RONG Yun, XIA Guang-hui. Risk prediction models for postoperative delirium in elderly patients with hip fracture: a systematic review [J]. Journal of Nursing, 2023, 30(7): 48-52.
[3] CAI Wen-wen, ZHANG Xing-xing, YE Min-shan, LUO Yan-xia, RUAN Wei-qing, Li Jia. Construction of nursing sensitive quality indicator system for patients undergoing colonoscopy based on structure-process-outcome model [J]. Journal of Nursing, 2023, 30(6): 1-6.
[4] LIU Sha, CHEN Ling, LI Tian-tian, ZHANG Dan-li, CAI Wen-zhi. Developmental trajectory of pelvic floor muscle training self-efficacy in antenatal and postnatal women and its impact on postpartum urinary incontinence [J]. Journal of Nursing, 2023, 30(3): 1-5.
[5] ZHANG Hong-xia, YANG Qiao-qiao, DANG Chen-po, ZHANG Wen-fang, ZHANG Xiao-min, SHAO Zhuan-lan. Fracture risk prediction tools in elderly patients with osteoporosis: a scoping review [J]. Journal of Nursing, 2023, 30(3): 57-62.
[6] MIAO Kang-kang,CEHN Qin. Translation and psychometric validation of Children’s Environmental Health Questionnaires [J]. Journal of Nursing, 2023, 30(2): 17-22.
[7] WANG Jing-rong, WEN Xiu-lin, QIAO Li-na, JIN Xian-zhen, FAN Hui, LIU Xuan-xuan, LUO Li-qun, XIAO Qian. Construction of core care skill index system for family caregivers of patients with enterostomy in early stage [J]. Journal of Nursing, 2023, 30(12): 1-6.
[8] LIU Ang, ZHANG Can-ling, SUN Yan-jun, TAO Jun-rong, GONG Kai-xue, MENG Li-hong. Effect of Kiken Yochi training combined with Kirkpatrick model in training of emergency comprehensive ability of nursing undergraduates [J]. Journal of Nursing, 2023, 30(12): 27-30.
[9] ZHANG Xiao-lan, SU Xiao-hua, ZHANG Jing. Construction of risk prediction model for cognitive dysfunction in pregnant women with cesarean section [J]. Journal of Nursing, 2023, 30(12): 73-78.
[10] ZHANG Cheng-jie, JING Kun-juan, LIU Dong-xue. Efficacy comparison of nomogram and classification and regression tree in predicting stigma in elderly stroke patients [J]. Journal of Nursing, 2023, 30(11): 7-12.
[11] TU Ying, DU Dan-yang, SHU Yan, ZENG Tao. Best evidence summary of prevention of recurrent ischemic stroke [J]. Journal of Nursing, 2023, 30(11): 41-46.
[12] LI Dan-dan, QIAO Yuan-jing, LIU Jia-xin, WANG Xu, XIA Yu-xue, LIANG Wen-na, QIN Guang-ya, FENG Meng-xuan. Construction of practice training effect evaluation system of intravenous therapy nurse specialists based on Offset model [J]. Journal of Nursing, 2023, 30(10): 73-78.
[13] LI Meng-na, LIU Xiao-xia,CHEN Mei-wen, ZHAO Rui, GE Li-na. Comparison of random forest and Logistic regression models for predicting early discharge after enhanced recovery after surgery for patients with endometrial cancer [J]. Journal of Nursing, 2023, 30(1): 17-21.
[14] CHU Wen-qiang, PENG Jun-xiang, LI Dan-ling. Dynamic changes of inflammatory indicators after cranial surgery and diagnostic value for intracranial infection: a Logistic regression analysis [J]. Journal of Nursing, 2023, 30(1): 22-27.
[15] HUANG Pan-pan, CHENG Hong, ZHANG Ying-hong, WANG Yan, ZHANG Lian-sheng. Construction of "Internet + Nursing Service" Risk Management Evaluation Index System Based on 4R-model Crisis Management Theory [J]. Journal of Nursing, 2022, 29(9): 16-20.
Viewed
Full text


Abstract

Cited
  Shared   
[1] ZHANG Cheng-jie, JING Kun-juan, LIU Dong-xue. Efficacy comparison of nomogram and classification and regression tree in predicting stigma in elderly stroke patients[J]. Journal of Nursing, 2023, 30(11): 7 -12 .
[2] LIU Guan-lin, CHEN Jian-peng, SONG Kai-lan, Luan Yi , ZHANG Di, WANG Xin-yuan, ZHOU Ting, KONG Ying. Effect of three-level linkage combined with family empowerment model on control of sudden symptoms in patients with hospice care at home[J]. Journal of Nursing, 2023, 30(11): 13 -16 .
[3] GONG Shu-ping, WANG Xiao-min, JIANG Yu-xiao, SUN Xiao-ying, ZHAO Xiao-min. Latent profile and influencing factors of empathy of nursing students at early stage of internship[J]. Journal of Nursing, 2023, 30(11): 17 -21 .
[4] . [J]. Journal of Nursing, 2023, 30(11): 22 -26 .
[5] . [J]. Journal of Nursing, 2023, 30(11): 27 -30 .
[6] . [J]. Journal of Nursing, 2023, 30(11): 31 -35 .
[7] . [J]. Journal of Nursing, 2023, 30(11): 36 -40 .
[8] TU Ying, DU Dan-yang, SHU Yan, ZENG Tao. Best evidence summary of prevention of recurrent ischemic stroke[J]. Journal of Nursing, 2023, 30(11): 41 -46 .
[9] . [J]. Journal of Nursing, 2023, 30(11): 47 -52 .
[10] . [J]. Journal of Nursing, 2023, 30(11): 53 -58 .