DSA-based perfusion parameters versus TICI score after mechanical thrombectomy in acute ischaemic stroke patients: a congruence analysis.

Background: Several factors are frequently considered for outcome prediction rin stroke patients. We assessed the value of digital subtraction angiography (DSA)-based brain perfusion measurements after mechanical thrombectomy (MT) for outcome prediction in acute ischaemic stroke.

Methods: From DSA image data (n = 90; 38 females; age 73.3 ± 13.1 years [mean ± standard deviation]), time-contrast agent (CA) concentration curves were acquired, and maximum slope (MS), time to peak (TTP), and maximum CA concentration (CA) were calculated using an arterial input function. This data was used to predict neurological deficits at 24 h and upon discharge by using multiple regression analysis; the predictive capability was compared with the predictive power of the "Thrombolysis in cerebral infarction" (TICI) score. Intraclass correlation coefficients (ICC) of the NIHSS values were analysed.

Results: The comparison of means revealed a linear trend after stratification into TICI classes for CA (TICI 0: 0.07 ± 0.02 a.u. to TICI 3: 0.22 ± 0.07 a.u.; p < 0.001), and for MS (TICI 0: 0.04 ± 0.01 a.u./s to TICI 3: 0.12 ± 0.0  a.u./s; p < 0.001). Regression analyses demonstrated equivalent capabilities for estimating neurological deficits after 24 h and at discharge using both the TICI score and DSA-based perfusion parameters (ΔR² ~ 0.03). Compared to the actual NIHSS, the ICC ranged from 0.55 to 0.84 for DSA-based models and from 0.6 to 0.82 for TICI-based models.

Conclusion: Semi-quantitative evaluation of DSA-based perfusion parameters prior to and after MT is feasible and could enhance the objectivity and comparability of MT outcome prediction. This technique may offer novel approaches in acute ischaemic stroke management and data comparability.

Relevance Statement: DSA-based brain perfusion measurements following interventional stroke therapy could allow for an experience-independent assessment of reperfusion success. It demonstrates predictive power at least equivalent to the established methods. This could support a future automated DSA-based brain perfusion measurement method.

Key Points: Currently, the evaluation of stroke therapy success is based on the treating physician's experience. The present study introduces an objective semi-quantitative evaluation method. In predicting clinical outcomes, the traditional expert-based and semi-quantitative methods are equivalent.