Clot-based time attenuation curve as a novel imaging predictor of mechanical thrombectomy functional outcome in acute ischemia stroke

Objectives To investigate whether a novel assessment of thrombus permeability obtained from perfusion computed tomography (CTP) can act as a more accurate predictor of clinical response to mechanical thrombectomy (MT) in acute ischemic stroke (AIS).Materials and methods We performed a study including two cohorts of AIS patients who underwent MT admitted to a single-center between April 2018 and February 2022: a retrospective development cohort (n = 71) and a prospective independent validation cohort (n = 96). Thrombus permeability was determined in terms of entire thrombus time-attenuation curve (TAC) on CTP. Association between thrombus TAC distributions and histopathological results was analyzed in the development cohort. Logistic regression was used to assess the performance of the TAC for predicting 90-day modified Rankin Scale (mRS) score, and good outcome was defined as a mRS score of = 2. Basic clinical characteristics was used to build a routine clinical model. A combined model gathered TAC and basic clinical characteristics was also developed. The performance of the three models is compared on the independent validation set.Results Two TAC distributions were observed-unimodal (uTAC) and linear (lTAC). TAC distributions achieved strong correlations (|r|= 0.627, p < 0.001) with histopathological results, in which uTAC associated with fibrin- and platelet-rich clot while lTAC associated with red blood cell-rich clot. The uTAC was independently associated with poor outcome (odds ratio, 0.08 [95% confidence interval (CI), 0.02-0.31]; p < 0.001). TAC distributions yielded an AUC of 0.78 (95% CI, 0.70-0.87) for predicting clinical outcome. When combined clinical characteristics, the performance was significantly improved (AUC, 0.85 [95% CI, 0.76-0.93]; p < 0.001) and higher than routine clinical model (AUC, 0.69 [95% CI, 0.59-0.83]; p < 0.001).Conclusions Thrombus TAC on CTP were found to be a promising new imaging biomarker to predict the outcomes of MT in AIS.