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开发中风后预后良好和预后不良的预测模型。

Developing predictive models of excellent and devastating outcome after stroke.

机构信息

Department of Neurology, Aberdeen Royal Infirmary, Ward 40 Aberdeen Royal Infirmary Foresterhill, Aberdeen AB252ZN, UK.

出版信息

Age Ageing. 2012 Jul;41(4):560-4. doi: 10.1093/ageing/afs034. Epub 2012 Mar 22.

Abstract

BACKGROUND

models to predict functional status post-stroke have utility in balancing groups in randomised trials, for outcome comparison between stroke centres and may assist in outcome prediction. This study aimed to develop models of both excellent [modified Rankin score (mRS) 0-1] and devastating outcomes (mRS of 5-6).

METHODS

patients admitted with ischaemic or haemorrhagic stroke in 2001-02 to the Halifax Infirmary, Canada, were enrolled. Sixteen clinical variables from the first neurological assessment and six radiological variables from the acute CT scan were used to the model outcome at 6 months.

RESULTS

five hundred and thirty-eight stroke patients were enrolled. Thirty per cent had an excellent outcome and 30% had a devastating outcome. Three models of the excellent outcome were developed [area under the receiver operator curve (AUC) 0.866-882] including the variables age, pre-stroke functional status, stroke severity, ability to lift both arms, walk independently, normal verbal Glasgow Coma Scale and leukoaraiosis. Predictive models of the devastating outcome (AUC of 0.859-0.874) included additional variables living alone pre-stroke and total anterior circulation stroke. The simplest models of both outcomes were externally validated (AUC of 0.856-0.885).

CONCLUSION

this study demonstrates new externally validated predictive models of both excellent and devastating outcomes. Leukoaraiosis was the only independent radiological predictor of both outcomes. Living alone pre-stroke predicted devastating outcome post-stroke.

摘要

背景

预测卒中后功能状态的模型在随机试验中具有平衡组别的效用,可用于卒中中心之间的结果比较,并可能有助于预测结果。本研究旨在建立优秀(改良 Rankin 量表 [mRS] 0-1)和严重结局(mRS 为 5-6)的模型。

方法

纳入了 2001-02 年在加拿大哈利法克斯综合医院住院的缺血性或出血性卒中患者。使用首次神经评估的 16 个临床变量和急性 CT 扫描的 6 个影像学变量来预测 6 个月时的结局。

结果

共纳入 538 例卒中患者。30%的患者结局良好(优秀),30%的患者结局严重(严重)。建立了 3 种优秀结局模型(AUC 为 0.866-0.882),包括年龄、卒中前功能状态、卒中严重程度、双臂上举能力、独立行走能力、言语格拉斯哥昏迷量表正常和脑白质疏松。严重结局的预测模型(AUC 为 0.859-0.874)包括了卒中前独居和全前循环卒中这两个额外的变量。两种结局的最简模型均经外部验证(AUC 为 0.856-0.885)。

结论

本研究建立了新的优秀和严重结局的外部验证预测模型。脑白质疏松是两种结局的唯一独立影像学预测因素。卒中前独居可预测卒中后的严重结局。

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