A new method for predicting recovery after stroke.
OBJECTIVE
Several prognostic factors have been identified for outcome after stroke. However, there is a need for empirically derived models that can predict outcome and assist in medical management during rehabilitation. To be useful, these models should take into account early changes in recovery and individual patient characteristics. We present such a model and demonstrate its clinical utility.


METHODS
Data on functional recovery (Barthel Index) at 0, 2, 4, 6, and 12 months after stroke were collected prospectively for 299 stroke patients at 2 London hospitals. Multilevel models were used to model recovery trajectories, allowing for day-to-day and between-patient variation. The predictive performance of the model was validated with an independent cohort of 710 stroke patients.


RESULTS
Urinary incontinence, sex, prestroke disability, and dysarthria affected the level of outcome after stroke; age, dysphasia, and limb deficit also affected the rate of recovery. Applying this to the validation cohort, the average difference between predicted and observed Barthel Index was -0.4, with 90% limits of agreement from -7 to 6. Predicted Barthel Index lay within 3 points of the observed Barthel Index on 49% of occasions and improved to 69% when patients' recovery histories were taken into account.


CONCLUSIONS
The model predicts recovery at various stages of rehabilitation in ways that could improve clinical decision making. Predictions can be altered in light of observed recovery. This model is a potentially useful tool for comparing individual patients with average recovery trajectories. Patients at elevated risk could be identified and interventions initiated.
Tilling
K
K
Department of Public Health Sciences, King's College London, London, UK. kate.tilling@kcl.ac.uk
Sterne
J A
JA
Rudd
A G
AG
Glass
T A
TA
Wityk
R J
RJ
Wolfe
C D
CD
D000203:Activities of Daily Living; D017677:Age Distribution; D000368:Aged; D000369:Aged, 80 and over; D001037:Aphasia / Q000453:epidemiology; D015331:Cohort Studies; D015897:Comorbidity; D003661:Decision Support Techniques; D019468:Disease Management; D004401:Dysarthria / Q000453:epidemiology; D005260:Female; D006801:Humans; D008131:London / Q000453:epidemiology; D008297:Male; D015233:Models, Statistical; D011237:Predictive Value of Tests; D011379:Prognosis; D020127:Recovery of Function; D015203:Reproducibility of Results; D018570:Risk Assessment; D012720:Severity of Illness Index; D017678:Sex Distribution; D020521:Stroke / Q000175:diagnosis / Q000453:epidemiology; D000071939:Stroke Rehabilitation; D014549:Urinary Incontinence / Q000453:epidemiology