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Journal of Nursing ›› 2022, Vol. 29 ›› Issue (5): 10-14.doi: 10.16460/j.issn1008-9969.2022.05.010

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Construction and Verification of Predictive Model of Hemorrhage after Intravenous Thrombolysis in Acute Ischemic Stroke

YANG Jie1,2, XIE Xiao-hua1,2, LIAN Wan-cheng1, YANG Mei2, DENG Li-ping2, PAN Lu2   

  1. 1. Clinical College of Shenzhen Second Hospital, Anhui Medical University, Shenzhen 518035, China;
    2. The Second People's Hospital of Shenzhen, the First Affiliated Hospital of Shenzhen University, Shenzhen 518035,China
  • Received:2021-11-20 Online:2022-03-10 Published:2022-04-11

Abstract: Objective To explore the risk factors of bleeding in patients with acute ischemic stroke after receiving intravenous thrombolysis, and construct a predictive model. Methods Totally 462 patients with acute ischemic stroke who underwent intravenous thrombolysis in a tertiary grade-A hospital in Shenzhen from January 2014 to December 2020 were divided into bleeding group (n=264) and non-bleeding group (n=198). The binary logistic regression model was used to analyze the risk factors, and the predictive model was constructed and verified. Results Age, the score of National Institutes of Health Stroke Scale (NIHSS) before thrombolysis, time from onset to thrombolysis, history of hypertension, and leukoaraiosis were independent risk factors for hemorrhage after intravenous thrombolysis in patients with acute ischemic stroke. AUC of the predictive model was 0.786 and the specificity and sensitivity were 81.82% and 62.36% respectively. Decision curve analysis showed that implementing decision interventions in the bleeding risk range of 0.306 to 0.990 had net clinical benefit. And after verification, it had been shown that the model had good discrimination (AUC=0.743,95%CI:0.699~0.788) and Calibration degree (Hosmer-Lemeshow test:χ2=11.559,P=0.172). Conclusion The nomogram model constructed in this study has better predictive performance, clinical application value, and high repeatability.

Key words: acute ischemic stroke, intravenous thrombolysis, hemorrhage, risk factor, prediction model, nomogram

CLC Number: 

  • R473.5
[1] 周田田,黄萍,邹圣强. 急性缺血性脑卒中患者静脉溶栓后出血转化预测模型的研究进展[J].护理学报, 2021, 28(6): 17-21. DOI:10.16460/j.issn1008-9969.2021.06.017.
[2] Dzialowski I, Pexman JH, Barber PA, et al.Asymptomatic Hemorrhage after Thrombolysis May not be Benign: Prognosis by Hemorrhage Type in the Canadian Alteplase for Stroke Effectiveness Study Registry[J]. Stroke, 2007, 38(1): 75-79. DOI: 10.1161/01.STR.0000251644.76546.62.
[3] Lei C,Wu B,Liu M,et al.Asymptomatic Hemorrhagic Transformation after Acute Ischemic Stroke: Is It Clinically Innocuous?[J]. J Stroke Cerebrovasc Dis, 2014, 23(10):2767-2772. DOI:10.1016/j.jstrokecerebrovasdis.2014.06.024.
[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.中国急性缺血性脑卒中诊治指南2018[J]. 中华神经科杂志, 2018, 51(9):666-682. DOI:10.3760/cma. j. issn. 1006-7876.2018.09.004.
[5] Hacke W, Kaste M, Fieschi C, et al.Randomised Double-Blind Placebo-controlled Trial of Thrombolytic Therapy with Intravenous Alteplase in Acute Ischaemic Stroke (Ecass Ii). Second European-australasian Acute Stroke Study Investigators[J]. Lancet, 1998,352(9136):1245-1251.DOI:10.1016/s0140-6736(98)08020-9.
[6] Wen L, Zhang S, Wan K, et al.Risk Factors of Haemorrhagic Transformation for Acute Ischaemic Stroke in Chinese Patients Receiving Intravenous Thrombolysis: A Meta-analysis[J]. Medicine (Baltimore), 2020, 99(7):e18995. DOI: 10.1097/MD.0000000000018995.
[7] 常红,赵洁,王晓娟,等. 急性缺血性脑卒中患者静脉溶栓后出血预警模型的构建[J]. 中华护理杂志, 2019, 54(11): 1648-1652. DOI: 10.3761/j.issn.0254-1769.2019.11.010.
[8] Lyden P.Using the National Institutes of Health Stroke Scale: A Cautionary Tale[J]. Stroke, 2017, 48(2):513-519. DOI: 10.1161/STROKEAHA.116.015434.
[9] Wu Z, Zeng M, Li C, et al.Time-dependence of NIHSS in Predicting Functional Outcome of Patients with Acute Ischemic Stroke Treated with Intravenous Thrombolysis[J]. Postgrad Med J, 2019, 95(1122):181-186.DOI:10.1136/postgradmedj-2019-136398.
[10] Powers WJ, Rabinstein AA, Ackerson T, et al.2018 Guidelines for the Early Management of Patients with Acute Ischemic Stroke: A Guideline for Healthcare Professionals from the American Heart Association/American Stroke Association[J]. Stroke, 2018,49(3):e46-e110.DOI:10.1161/STR.0000000000000158.
[11] Sandercock P, Wardlaw JM, Lindley R I, et al.The Benefits and Harms of Intravenous Thrombolysis with Recombinant Tissue Plasminogen Activator within 6 h of Acute Ischaemic Stroke (The Third International Stroke Trial [Ist-3]): A Randomised Controlled Trial[J]. Lancet, 2012, 379(9834):2352-2363. DOI:10.1016/S0140-6736(12)60768-5.
[12] Niesen WD, Schläger A, Reinhard M, et al.Transcranial Sonography to Differentiate Primary Intracerebral Hemorrhage from Cerebral Infarction with Hemorrhagic Transformation[J]. J Neuroimaging, 2018, 28(4):370-373. DOI:10.1111/jon.12510.
[13] 李莹. 不同严重程度及时间窗的急性缺血性脑卒中患者静脉溶栓的预后研究[D]. 天津:天津医科大学, 2019.
[14] Lei Z, Li S, Hu S, et al.Effects of Baseline Systolic Blood Pressure on Outcome in Ischemic Stroke Patients with Intravenous Thrombolysis Therapy: A Systematic Review and Meta-analysis[J]. Neurologist, 2020,25(3):62-69. DOI:10.1097/NRL.0000000000000267.
[15] 王亚玲,王冰寒,张艳,等. 急性缺血性脑卒中患者溶栓术后预防出血转化的最佳证据总结[J]. 护理学报, 2021, 28(3):46-52. DOI:10.16460/j.issn1008-9969.2021.03.046.
[16] 潘静,陈晓霞,程洁,等. 血压及其变异性与脑白质疏松症的相关性研究进展[J]. 中国临床神经科学, 2017, 25(2): 241-245. DOI:10.3969/j.issn.1008-0678.2017.02.018.
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