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Describing
recovery after stroke: an application of multilevel models
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Funded
by:
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Charitable Foundation
of Guys & St Thomas'
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Study
team:
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Dr Kate Tilling*
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Dr Jonathan Sterne
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Prof Charles Wolfe
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Anna Cox
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Background:
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Several
prognostic factors have been identified for outcome after stroke. However,
most models are based on outcome at one time-point only, and do not take
into account the changing nature of outcome after stroke.
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Aims:
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To develop methods to quantify and predict
recovery after stroke
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Design:
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Data on
functional recovery (Barthel Index) at 0, 2, 4, 6 and 12 months after
stroke were collected prospectively for 299 stroke patients at two London
Hospitals. Multilevel models were used to model recovery trajectories,
allowing for day-to-day and between patient variation. Using the model
coefficients, predictions could be modified in the light of observed
recovery. The predictive performance of the model was validated using an
independent cohort of 710 stroke patients.
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Results:
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Average pattern of
recovery was initial improvement, then a gradual decline. Urinary
incontinence, sex, pre-stroke handicap and dysarthria affected only the
level of outcome after stroke; age, dysphasia and limb deficit also
affected rate of recovery. A rapid decline in Barthel Index was seen among
30 subjects who died before the end of the study. For an independent
cohort, outcome predicted by the model lay within 3 points of the measured
Barthel Index on 48% of occasions, improved to 69% when based additionally
on patients’ recovery history (conditional prediction). A Barthel value
more than 1 point below the conditional prediction predicted subsequent
death with a sensitivity of 65% and a specificity of 79%.
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Conclusion:
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The pattern of recovery
after stroke depends on the patient’s age, presence of dysphasia and limb
deficit immediately after stroke. Graphs of predicted and actual recovery
over time should be evaluated to monitor recovery of patients after stroke.
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References:
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Tilling K, Sterne JAC, Wolfe CDA. Multilevel
growth curve models with covariate effects: application to recovery after
stroke. Statistic in Medicine 2001; 20(5): 685-704
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Tilling K, Sterne JAC, Rudd AG, Glass TA, Wityk
RJ, Wolfe CDA. A New Methods for Predicting recovery after stroke. Stroke
(in press).
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