Introduction: Failures to translate pre-clinical results have been discouraging. We have contended that stroke is too heterogeneous with respect to factors influencing outcome to expect small studies to be balanced. It is not only difficult to control for biological and methodological variability but efforts to improve homogeneity, such as minimizing physiological variability, may render results less applicable to humans. Here, we report a predictive outcome model in experimental stroke which incorporates baseline variability and provides statistical thresholds a treatment must exceed to be efficacious in a broad population.
Methods: We generated a mathematical model to predict outcome using transient MCA occlusion in 23 unfasted rats. To create baseline variability, we varied occlusion times from 90-120 min, altered baseline glucose with streptozotocin, and assessed neurological outcome 3 days later with a modified Bederson Score (BS; 0-6 functional measure, 7 death). Statistical surfaces in 3 dimensions were generated using Jacobian matrices flanking the model to provide a screening threshold (1 SD) for comparing new therapies against this model.
Results: We successfully generated an outcome model from occlusion time, glucose and BS (Fig; R 2 =.49, p=.0003; middle surface is the model surrounded by SD surfaces). Outcome was sensitive to change in glucose and time, suggesting small imbalances in these factors between groups may influence outcome, and hence the perceived efficacy of a new therapeutic intervention. At normoglycemia and 90 mins, the lower surface overlapped with no deficit, indicating it would be difficult to reliably demonstrate benefit under those conditions.
Conclusions: These results indicate it is feasible to incorporate biological variability to generate more clinically relevant conditions. The method will be tested with other stroke models and modifiers towards a generalized model to screen for therapies worthy of further study.