A new method for predicting recovery after stroke

Background and Purpose-Several prognostic factors have been identified for outcome after stroke. However, there is a need for empirically derived mode ls that can predict outcome and assist in medical management during rehabil itation. 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 m onths after stroke were collected prospectively for 299 stroke patients at 2 London hospitals. Multilevel models were used to model recovery trajector ies, allowing for day-to-day and between-patient variation. The predictive performance of the model was validated with an independent cohort of 710 st roke patients. Results-Urinary incontinence, sex, prestroke disability, and dysarthria aff ected the level of outcome after stroke; age, dysphasia, and limb deficit a lso 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 w ithin 3 points of the observed Barthel Index on 49% of occasions and improv ed 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 al tered in light of observed recovery. This model is a potentially useful too l for comparing individual patients with average recovery trajectories. Pat ients at elevated risk could be identified and interventions initiated.