Leira E C, Adams H P, Rosenthal G E, Torner J C
Department of Neurology, Carver College of Medicine, University of Iowa, Iowa City, Iowa 52242, USA.
Cerebrovasc Dis. 2008;26(6):573-7. doi: 10.1159/000165109. Epub 2008 Oct 23.
Emergency treatment of ischemic stroke should ideally be mechanism specific, but acute subtype diagnosis is problematic. Since different subtypes often are associated with specific patterns of neurological deficits, we hypothesize that scores on baseline NIH stroke scale (NIHSS) items may help emergently stratify patients by their probability of having a particular stroke subtype.
We performed multivariate polytomous logistic regression analyses on 1,281 patients enrolled in the Trial of ORG 10172 in Acute Stroke Treatment (TOAST). We tested the predictive value of individual items to the baseline NIHSS exam, and syndromic combinations of those items, in anticipating the TOAST stroke subtype at 3 months adjusting for atrial fibrillation. We then used the most significant NIHSS items to construct a predictive model.
The NIHSS items that discriminate between stroke subtypes are language, neglect, visual field and brachial predominance of weakness. Among patients without atrial fibrillation, a normal score for these 4 variables conveys a 46% chance of lacunar stroke, 12% of atherothrombotic stroke and 10% of cardioembolism. This pattern gradually reverses with increased numbers of abnormal responses. Those with abnormalities in all 4 items have a 0.1% chance of lacunar stroke, 50% of atherothrombotic stroke and 39% of cardioembolism.
Language, neglect, visual fields and brachial predominance of weakness in the baseline NIHSS help discriminate between subtypes, particularly between lacunar and nonlacunar strokes. Clinical trials testing interventions aimed to particular stroke mechanisms may use these NIHSS items to emergently stratify patients based on their probability of having a particular stroke subtype.
缺血性中风的理想紧急治疗应针对发病机制，但急性亚型诊断存在问题。由于不同亚型通常与特定的神经功能缺损模式相关，我们推测美国国立卫生研究院卒中量表（NIHSS）基线项目得分可能有助于根据患者发生特定中风亚型的概率对其进行紧急分层。
我们对1281例参与急性卒中治疗中ORG 10172试验（TOAST）的患者进行了多变量多分类逻辑回归分析。我们测试了各个项目对NIHSS基线检查的预测价值，以及这些项目的综合征组合在调整房颤因素后预测3个月时TOAST中风亚型的能力。然后，我们使用最具显著性的NIHSS项目构建了一个预测模型。
区分中风亚型的NIHSS项目包括语言、偏侧忽视、视野和上肢为主的无力。在无房颤的患者中，这4个变量得分正常提示腔隙性中风的概率为46%，动脉粥样硬化血栓形成性中风为12%，心源性栓塞为10%。随着异常反应数量的增加，这种模式逐渐逆转。这4项均异常的患者发生腔隙性中风的概率为0.1%，动脉粥样硬化血栓形成性中风为50%，心源性栓塞为39%。
基线NIHSS中的语言、偏侧忽视、视野和上肢为主的无力有助于区分亚型，尤其是腔隙性和非腔隙性中风之间。针对特定中风机制的干预措施的临床试验可使用这些NIHSS项目根据患者发生特定中风亚型的概率对其进行紧急分层。
