中国现代神经疾病杂志 ›› 2020, Vol. 20 ›› Issue (8): 727-732. doi: 10.3969/j.issn.1672-6731.2020.08.013

• 临床研究 • 上一篇    下一篇

2 Padua血栓评估模型对脑卒中患者静脉血栓栓塞症的评估价值

李巍, 王莉莉   

  1. 100038 首都医科大学附属北京世纪坛医院神经内科
  • 收稿日期:2020-07-20 出版日期:2020-08-25 发布日期:2020-09-21
  • 通讯作者: 王莉莉,Email:bitljq2012@163.com

Value of Padua risk assessment model for evaluating venous thromboembolism of stroke patients

LI Wei, WANG Li-li   

  1. Department of Neurology, Beijing Shijitan Hospital, Capital Medical University, Beijing 100038, China
  • Received:2020-07-20 Online:2020-08-25 Published:2020-09-21

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摘要：

目的 探究Padua血栓评估模型对脑卒中患者并发静脉血栓栓塞症（VTE）风险的预测价值。方法 选择2018年1月至2019年6月共169例脑卒中患者，其中并发VTE者（血栓组）56例，未并发VTE者（对照组）113例。比较两组患者Padua血栓评估模型中危险因素的分布差异，采用单因素和多因素前进法Logistic回归分析筛查脑卒中患者并发VTE的危险因素；采用Padua血栓评估模型对患者进行VTE风险分层，以不同分值作为风险分层的界值拟合受试者工作特征（ROC）曲线，评价Padua血栓评估模型预测脑卒中并发VTE的效能。结果 血栓组患者房颤（P=0.024）、高龄（P=0.000）、心力衰竭和（或）呼吸衰竭（P=0.000）、急性感染性和（或）风湿性疾病（P=0.000）、活动性恶性肿瘤/化疗（P=0.016）、既往VTE史（P=0.007）、活动减少（P=0.009）比例均高于对照组。Logistic回归分析显示，房颤（OR=3.203，95% CI：1.172~8.751；P=0.023）、高龄（OR=3.469，95% CI：1.063~7.580；P=0.002）、心力衰竭和（或）呼吸衰竭（OR=4.017，95% CI：1.315~12.274；P=0.015）、急性感染性和（或）风湿性疾病（OR=3.472，95% CI：1.457~8.271；P=0.005）、既往VTE史（OR=5.884，95% CI：1.068~32.408；P=0.042）是脑卒中并发VTE的危险因素。ROC曲线显示，Padua血栓评估模型风险分层界值为4分时，曲线下面积为0.762、标准误为0.040（95% CI：0.689~0.854，P=0.000），其灵敏度、特异度、阳性预测值、阴性预测值分别为71.43%、69.02%、53.34%、83.08%，Youden指数为0.404，Padua血栓评估模型风险分层界值为4分对脑卒中并发VTE的预测价值最优。结论 高龄、既往VTE史和危重型脑卒中是VTE的高危因素，基于Padua血栓评估模型能够有效预测脑卒中并发VTE的风险。

关键词: 卒中, 静脉血栓形成, 危险因素, Logistic模型, ROC曲线

Abstract:

Objective To explore the value of Padua risk assessment model for evaluating venous thromboembolism (VTE) of stroke patients. Methods VTE screening was performed in 169 patients with stroke who were hospitalized in Beijing Shijitan Hospital between January 2018 and June 2019. Fifty-six patients were identified in VTE group and 113 patients in non-VTE group. The difference of risk factors distribution in Padua risk assessment model was compared between 2 groups. Univariate and multivariate Logistic regression were used to analyze the risk factors for VTE in stroke patients. The risk stratification of VTE was calculated by Padua risk assessment model and different scores were used as the boundary value of risk stratification to fit the receiver operating characteristic curve (ROC), which evaluated the validity of Padua risk assessment model in predicting VTE risk patients. Results The proportions of atrial fibrillation (P=0.024), elderly age (P=0.000), heart and/or respiratory failure (P=0.000), acute infection and/or rheumatologic disorder (P=0.000), active cancer (P=0.016), previous VTE (P=0.007), reduced mobility (P=0.009) in VTE group were significantly higher than those in non-VTE group. Univariate and multivariate Logistics regression analysis were used to evaluate the risk factors of VTE. In VET group, atrial fibrillation (OR=3.203, 95% CI:1.172-8.751; P=0.023), elderly age (OR=3.469, 95% CI:1.603-7.508; P=0.002), heart and/or respiratory failure (OR=4.017, 95% CI:1.315-12.274; P=0.015), acute infection and/or rheumatologic disorder (OR=3.472, 95% CI:1.457-8.271; P=0.005), previous VTE (OR=5.884, 95% CI:1.068-32.408; P=0.042) were significantly associated with VTE. For Padua risk assessment model, the ROC yielded an area under the curve (AUC) of 0.762 and the standard error of the area is 0.040 (95% CI:0.689-0.854, P=0.000). At the cutoff point of 4, Youden index got the value of 0.404 and its sensitivity and specificity was 58.93% and 85.83% and corresponding positive and negative predictive value was 53.34% and 83.08%. Padua risk assessment model can effectively assess the risk of VTE at the cutoff point 4. Conclusions Elderly age, previous VTE and critical and severe stroke are VTE high-risk factors. Padua risk assessment model can effectively assess the risk of VTE among patients after stroke.

Key words: Stroke, Venous thrombosis, Risk factors, Logistic models, ROC curve

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