脑卒中带溶栓后出血的出血转化(HT)和症状性脑出血(siich)风险预测模型

ISRN stroke Pub Date : 2013-01-01 DOI:10.1155/2013/681673
James E Siegler, Muhammad Alvi, Amelia K Boehme, Michael J Lyerly, Karen C Albright, Reza Bavarsad Shahripour, Pawan V Rawal, Niren Kapoor, April Sisson, J Thomas Houston, Anne W Alexandrov, Sheryl Martin-Schild, Andrei V Alexandrov
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引用次数: 6

摘要

背景:症状性脑出血(siich)仍然是静脉注射组织型纤溶酶原激活剂(IV tPA)治疗中最可怕的并发症。我们的目的是调查先前验证的评分方法如何在美国两个卒中带州的治疗患者中发挥作用。方法和结果:我们回顾性回顾了来自两个卒中带州的两个中心的连续接受静脉注射tPA的患者(2008-2011)。我们评估了三种模型预测sICH的能力。sICH定义为美国国立卫生研究院卒中量表(NIHSS)评分≥4分的2型实质出血恶化或死亡。在457例静脉注射tpa治疗的患者中,19例(4.2%)患有sICH(平均年龄68岁,黑人26.3%,女性63.2%)。Cucchiara模型在整个队列中最能预测sICH (AUC: 0.6528),在黑人中最能预测sICH (OR = 6.03, 95% CI 1.07-34.1, P = 0.0422)。结论:在我们来自种族异质性美国卒中带的小样本中,Cucchiara模型在预测siich方面优于其他模型。虽然预测模型不应该用来证明不进行溶栓治疗是合理的,但那些对了解siich的贡献者感兴趣的人可以选择使用Cucchiara模型,直到卒中带模型在该地区开发出来。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Hemorrhagic Transformation (HT) and Symptomatic Intracerebral Hemorrhage (sICH) Risk Prediction Models for Postthrombolytic Hemorrhage in the Stroke Belt.

Hemorrhagic Transformation (HT) and Symptomatic Intracerebral Hemorrhage (sICH) Risk Prediction Models for Postthrombolytic Hemorrhage in the Stroke Belt.

Hemorrhagic Transformation (HT) and Symptomatic Intracerebral Hemorrhage (sICH) Risk Prediction Models for Postthrombolytic Hemorrhage in the Stroke Belt.

Hemorrhagic Transformation (HT) and Symptomatic Intracerebral Hemorrhage (sICH) Risk Prediction Models for Postthrombolytic Hemorrhage in the Stroke Belt.

Background: Symptomatic intracerebral hemorrhage (sICH) remains the most feared complication of intravenous tissue plasminogen activator (IV tPA) treatment. We aimed to investigate how previously validated scoring methodologies would perform in treated patients in two US Stroke Belt states.

Methods and results: We retrospectively reviewed consecutive patients from two centers in two Stroke Belt states who received IV tPA (2008-2011). We assessed the ability of three models to predict sICH. sICH was defined as a type 2 parenchymal hemorrhage with deterioration in National Institutes of Health Stroke Scale (NIHSS) score of ≥4 points or death. Among 457 IV tPA-treated patients, 19 (4.2%) had sICH (mean age 68, 26.3% Black, 63.2% female). The Cucchiara model was most predictive of sICH in the entire cohort (AUC: 0.6528) and most predictive of sICH among Blacks (OR = 6.03, 95% CI 1.07-34.1, P = 0.0422) when patients were dichotomized by score.

Conclusions: In our small sample from the racially heterogeneous US Stroke Belt, the Cucchiara model outperformed the other models at predicting sICH. While predictive models should not be used to justify nontreatment with thrombolytics, those interested in understanding contributors to sICH may choose to use the Cucchiara model until a Stroke Belt model is developed for this region.

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