Purpose: Collateral circulation plays a pivotal role in the pathophysiology of acute ischemic stroke and is increasingly recognized as a promising biomarker for predicting the clinical outcome. However, there is no single established grading system. We designed a novel machine-learning software that allows non-invasive, objective, and quantitative assessment of collaterals according to their vascular territories. Our goal is to investigate the prognostic and predictive value of this collateral score for the prediction of acute stroke outcome.
Methods: This is a retrospective study of 135 patients with anterior circulation stroke treated with IV TPA. An equation using this collateral score (adjusting for age, baseline NIHSS, and recanalization) was derived to predict the clinical outcome (90-day mRS). The primary analyses focused on determining the prognostic value of our newly developed collateral scores. Secondary analyses examined the interrelationships between the collateral score and other variables.
Results: The collateral score emerged as a statistically significant prognostic biomarker for good clinical outcome (p < 0.033) among recanalized patients, but not among non-recanalized patients (p < 0.497). Our results also showed that collateral score was a predictive biomarker (p < 0.044). These results suggest that (1) patients with good collateral score derive more benefit from successful recanalization than patients with poor collateral score and (2) collateral status is inconsequential if recanalization is not achieved.
Conclusion: Our data results reinforce the importance of careful patient selection for recanalization therapy to avoid futile recanalization. The paucity of collaterals predicts poor clinical outcome despite recanalization. On the other hand, robust collaterals warrant consideration for recanalization therapy given the better odds of good clinical outcome.
Keywords: Biomarker; CT perfusion; Collateral grading system; Quantitative; Stroke.