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护理学报 ›› 2022, Vol. 29 ›› Issue (5): 10-14.doi: 10.16460/j.issn1008-9969.2022.05.010

• 研究生园地 • 上一篇    下一篇

急性缺血性脑卒中静脉溶栓后出血预测模型的构建与验证

杨洁1,2, 谢小华1,2, 连万成1, 杨梅2, 邓丽萍2, 潘璐2   

  1. 1.安徽医科大学 深圳二院临床学院,广东 深圳 518035;
    2.深圳市第二人民医院 深圳大学第一附属医院,广东 深圳 518035
  • 收稿日期:2021-11-20 出版日期:2022-03-10 发布日期:2022-04-11
  • 通讯作者: 谢小华（1964-）,女,广西合浦人,硕士,主任护师,护理部主任,硕士研究生导师。E-mail：13560779836@163.com
  • 作者简介:杨洁（1995-）,女,安徽宣城人,本科学历,硕士研究生在读。
  • 基金资助:
    广东省科技厅资助项目（2017A020215121）; 广东省深圳市科技创新委员会科技计划资助项目（JCYJ20190806162803481）; 广东省深圳市科技创新委员会科技计划资助项目（JCYJ20180228163026995）; 广东省深圳市第二人民医院临床研究项目（20203357004）

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

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摘要： 目的 探讨急性缺血性脑卒中患者接受静脉溶栓治疗后发生出血的危险因素,构建预测模型。方法 纳入2014年1月—2020年12月于深圳市某三级甲等医院进行静脉溶栓治疗的急性缺血性脑卒中患者462例,将其分为出血组（n=264）和未出血组（n=198）。使用二分类Logistic回归模型分析相关危险因素,构建预测模型并进行验证。结果 年龄、溶栓前美国国立卫生研究院卒中量表评分、发病至溶栓时间、高血压史、脑白质疏松是急性缺血性脑卒中患者静脉溶栓后出血的独立危险因素。预测模型AUC为0.786,特异度为81.82%,敏感度为62.36%。决策曲线分析结果显示在出血风险0.306~0.990区间实施决策干预可获得临床净效益,并经验证后显示该模型具有较好的区分度（AUC=0.743,95%CI:0.699~0.788）和校准度（Hosmer-Lemeshow检验：χ2=11.559,P=0.172）。结论 本研究构建的列线图模型具有较佳的预测效能和临床应用价值,可重复性较强。

关键词: 急性缺血性脑卒中, 静脉溶栓, 出血, 危险因素, 预测模型, 列线图

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

中图分类号: 

  • R473.5
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