Predicting long-term outcome after acute ischemic stroke: a simple index works in patients from controlled clinical trials

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Konig, Inke and Ziegler, Andreas and Bluhmki, Enrich and Hacke, Werner and Bath, Philip M.W. and Sacco, Ralph L. and Diener, Hans-Christoph and Weimar, Christian (2008) Predicting long-term outcome after acute ischemic stroke: a simple index works in patients from controlled clinical trials. Stroke . ISSN 0039-2499

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Abstract

Background and Purpose—An early and reliable prognosis for recovery in stroke patients is important for initiation of

individual treatment and for informing patients and relatives. We recently developed and validated models for predicting survival and functional independence within 3 months after acute stroke, based on age and the National Institutes of Health Stroke Scale score assessed within 6 hours after stroke. Herein we demonstrate the applicability of our models in an independent sample of patients from controlled clinical trials.

Methods—The prognostic models were used to predict survival and functional recovery in 5419 patients from the Virtual

International Stroke Trials Archive (VISTA). Furthermore, we tried to improve the accuracy by adapting intercepts and

estimating new model parameters.

Results—The original models were able to correctly classify 70.4% (survival) and 72.9% (functional recovery) of patients. Because the prediction was slightly pessimistic for patients in the controlled trials, adapting the intercept improved the accuracy to 74.8% (survival) and 74.0% (functional recovery). Novel estimation of parameters, however, yielded no relevant further improvement.

Conclusions—For acute ischemic stroke patients included in controlled trials, our easy-to-apply prognostic models based

on age and National Institutes of Health Stroke Scale score correctly predicted survival and functional recovery after 3

months. Furthermore, a simple adaptation helps to adjust for a different prognosis and is recommended if a large data

set is available. (Stroke. 2008;39:000-000.)

Item Type:

Article

RIS ID:

https://nottingham-repository.worktribe.com/output/1015900

Schools/Departments:

University of Nottingham, UK > Faculty of Medicine and Health Sciences > School of Medicine > Division of Clinical Neuroscience

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