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Predicting independent survival after stroke: a European study for the development and validation of standardised stroke scales and prediction models of outcome

Research output: Contribution to journalArticle

Salma Ahmed Ayis, Bolaji Coker, Anthony G Rudd, Martin S Dennis, Charles D A Wolfe

Original languageEnglish
Pages (from-to)288-296
Number of pages9
JournalJournal of Neurology, Neurosurgery and Psychiatry
Early online date1 Nov 2012
StatePublished - 2013

King's Authors


BACKGROUND: Accurate prediction of stroke outcome is desirable for clinical management and provision of appropriate care, and potentially for stratification of patients into studies.

OBJECTIVES: To investigate the predictive properties of validated scales and severity measures, and their constituent variables, and to compare their prediction in six European populations.

METHODS: We studied 2033 first-ever stroke patients in population-based stroke registers in France, Italy, Lithuania, the UK, Spain and Poland. Logistic models were used to predict independent survival at 3 and 12 months after stroke using a range of measures including the Six Simple Variable (SSV), Barthel index (BI) and the National Institute of Heath Stroke Scale (NIHSS). Predictions were compared within and between populations using receiver operating characteristic curves. A five-variable scale was developed and validated. RESULTS: Comparisons of BI with BI+age, and NIHSS with NIHSS+age, across populations showed that inclusion of age significantly improved prediction. Fairly equal predictions were obtained by three models: five variables, BI+age, and NIHSS+age. Better agreement between predicted and actual outcomes, and more precise estimates were obtained by the five variables model (age, verbal component of the Glasgow Coma Scale, arm power, ability to walk, and pre-stroke dependency).

CONCLUSIONS: Living alone before the stroke was not significantly associated with independent survival after the stroke. Five variables (excluding living alone, from the SSV) provided good prediction for all populations and subgroups. Further external validation for our estimates is recommended before utilisation of the model in practice and research.

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