Ultraearly assessed reperfusion status after middle cerebral artery recanalization predicting clinical outcome

Abstract

Objectives: Mechanical thrombectomy has high evidence in stroke therapy; however, successful recanalization guarantees not a favorable clinical outcome. We aimed to quantitatively assess the reperfusion status ultraearly after successful middle cerebral artery (MCA) recanalization to identify flow parameters that potentially allow predicting clinical outcome.

Materials and methods: Sixty-seven stroke patients with acute MCA occlusion, undergoing recanalization, were enrolled. Using parametric color coding, a post-processing algorithm, pre-, and post-interventional digital subtraction angiography series were evaluated concerning the following parameters: pre- and post-procedural cortical relative time to peak (rTTP) of MCA territory, reperfusion time, and index. Functional long-term outcome was assessed by the 90-day modified Rankin Scale score (mRS; favorable: 0-2).

Results: Cortical rTTP was significantly shorter before (3.33 ± 1.36 seconds; P = .03) and after intervention (2.05 ± 0.70 seconds; P = .003) in patients with favorable clinical outcome. Additionally, age (P = .005) and initial National Institutes of Health Stroke Scale score (P = .02) were significantly different between the patients, whereas reperfusion index and time as well as initially estimated infarct size were not. In multivariate analysis, only post-procedural rTTP (P = .005) was independently associated with favorable clinical outcome. 2.29 seconds for post-procedural rTTP might be a threshold to predict favorable clinical outcome.

Conclusions: Ultraearly quantitative assessment of reperfusion status after successful MCA recanalization reveals post-procedural cortical rTTP as possible independent prognostic value in predicting favorable clinical outcome, even determining a threshold value might be possible. In consequence, focusing stroke therapy on microcirculatory patency could be valuable to improve outcome.

Keywords: cerebrovascular diseases; flow analysis; image post-processing; mechanical thrombectomy; stroke.