Cerebral infarction evolves at different rates depending on available blood flow suggesting that treatment time windows vary depending on the degree of pial collateral recruitment. This work sought to mathematically model infarct growth and determine whether infarct volume growth can be predicted by angiographic assessment of pial collateral recruitment in an experimental MCA occlusion animal model.

Pial collateral recruitment was quantified by using DSA, acquired 15 minutes following permanent MCA occlusion in 6 canines based on a scoring system (average pial collateral score) and arterial arrival time. MR imaging–based infarct volumes were measured 60, 90, 120, 180, 240 and 1440 minutes following MCA occlusion and were parameterized in terms of the growth rate index and final infarct volume (V_Final) as V(t) = V_Final [1 − e^{(−G × t)}] (t = time). Correlations of the growth rate index and final infarct volume to the average pial collateral score and arterial arrival time were assessed by linear bivariate analysis. Correlations were used to generate asymptotic models of infarct growth for average pial collateral score or arterial arrival time values. Average pial collateral score– and arterial arrival time–based models were assessed by F tests and residual errors.

Evaluation of pial collateral recruitment at 15 minutes postocclusion was strongly correlated with 24-hour infarct volumes (average pial collateral score: r² = 0.96, P < .003; arterial arrival time: r² = 0.86, P < .008). Infarct growth and the growth rate index had strong and moderate linear relationships to the average pial collateral score (r² = 0.89; P < .0033) and arterial arrival time (r² = 0.69; P < .0419), respectively. Final infarct volume and the growth rate index were algebraically replaced by angiographically based collateral assessments to model infarct growth. The F test demonstrated no statistical advantage to using the average pial collateral score– over arterial arrival time–based predictive models, despite lower residual errors in the average pial collateral score–based model (P < .03).

In an experimental permanent MCA occlusion model, assessment of pial collaterals correlates with the infarct growth rate index and has the potential to predict asymptotic infarct volume growth.