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Objectives Thrombus computed tomography (CT) characteristics might be used to assess histopathologic thrombus composition in patients treated with endovascular thrombectomy (EVT) for acute ischemic stroke (AIS). We aimed to assess the variability in thrombus composition that could be predicted with combined thrombus CT characteristics. Methods Thrombi of patients enrolled in the MR CLEAN Registry between March 2014 and June 2016 were histologically analyzed with hematoxylin-eosin staining and quantified for percentages of red blood cells (RBCs) and fibrin/platelets. We estimated the association between general qualitative characteristics (hyperdense artery sign [HAS], occlusion location, clot burden score [CBS]) and thrombus composition with linear regression, and quantified RBC variability that could be explained with individual and combined characteristics with R2. For patients with available thin-slice (≤ 2.5 mm) imaging, we performed similar analyses for general and quantitative characteristics (HAS, occlusion location, CBS, [relative] thrombus density, thrombus length, perviousness, distance from ICA-terminus). Results In 332 included patients, the presence of HAS (aβ 7.8 [95% CI 3.9–11.7]) and shift towards a more proximal occlusion location (aβ 3.9 [95% CI 0.6–7.1]) were independently associated with increased RBC and decreased fibrin/platelet content. With general characteristics, 12% of RBC variability could be explained; HAS was the strongest predictor. In 94 patients with available thin-slice imaging, 30% of RBC variability could be explained; thrombus density and thrombus length were the strongest predictors. Conclusions Quantitative thrombus CT characteristics on thin-slice admission CT improve prediction of thrombus composition and might be used to further guide clinical decision-making in patients treated with EVT for AIS in the future. Key Points • With hyperdense artery sign and occlusion location, 12% of variability in thrombus RBC content can be explained. • With hyperdense artery sign, occlusion location, and quantitative thrombus characteristics on thin-slice (≤ 2.5 mm) non-contrast CT and CTA, 30% of variability in thrombus RBC content can be explained. • Absolute thrombus density and thrombus length were the strongest predictors for thrombus composition.

Introduction Vascular anatomy might affect endovascular treatment success in acute ischemic stroke patients with large vessel occlusion. We investigated the prognostic value of extracranial vascular characteristics on procedural time and revascularization success in patients with large vessel occlusion in the anterior cerebral circulation. Patients and methods We included 828 patients endovascularly treated within 6.5 hours of symptom onset from the Dutch MR CLEAN-Registry. We evaluated aortic arch configuration, stenosis and tortuosity of supra-aortic arteries, and internal carotid arteries (ICAs) on pre-intervention CTA. We constructed logistic prediction models for outcome variables procedural duration (≥60 minutes) and non-successful revascularization (extended thrombolysis in cerebral infarction (eTICI) of 0–2A) using baseline characteristics and assessed the effect of extracranial vascular characteristics on model performance. Results Cervical ICA tortuosity and stenosis ≥99% improved prediction of long procedural duration compared with baseline characteristics from area under the curve of 0.61 (95% CI: 0.57–0.65) to 0.66 (95% CI: 0.62–0.70) ( P < 0.001). Cervical ICA tortuosity was significantly associated with non-successful recanalization. Prediction of non-successful revascularization did not improve after including aortic arch elongation, acute take-off angle, aortic variant, origin stenosis of supra-aortic arteries, and cervical ICA tortuosity, with an area under the curve of 0.63 (95% CI: 0.59–0.67) compared with 0.59 (95% CI: 0.55–0.63) ( P = 0.11). Conclusion Extracranial vascular characteristics have additional prognostic value for procedural duration, but not for revascularization success, compared with baseline characteristics. Performance of both prediction models is limited in patients treated for large vessel occlusion.