Background: We investigate the association of imaging biomarkers extracted from fully automated body composition analysis (BCA) of computed tomography (CT) angiography images of endovascularly treated acute ischemic stroke (AIS) patients regarding angiographic and clinical outcome.
Methods: Retrospective analysis of AIS patients treated with mechanical thrombectomy (MT) at three tertiary care-centers between March 2019-January 2022. Baseline demographics, angiographic outcome and clinical outcome evaluated by the modified Rankin Scale (mRS) at discharge were noted. Multiple tissues, such as muscle, bone, and adipose tissue were acquired with a deep-learning-based, fully automated BCA from CT images of the supra-aortic angiography.
Results: A total of 290 stroke patients who underwent MT due to cerebral vessel occlusion in the anterior circulation were included in the study. In the univariate analyses, among all BCA markers, only the lower sarcopenia marker was associated with a poor outcome (P=0.007). It remained an independent predictor for an unfavorable outcome in a logistic regression analysis (OR 0.6, 95% CI 0.3 to 0.9, P=0.044). Fat index (total adipose tissue/bone) and myosteatosis index (inter- and intramuscular adipose tissue/total adipose tissue*100) did not affect clinical outcomes.
Conclusion: Acute ischemic stroke patients with a lower sarcopenia marker are at risk for an unfavorable outcome. Imaging biomarkers extracted from BCA can be easily obtained from existing CT images, making it readily available at the beginning of treatment. However, further research is necessary to determine whether sarcopenia provides additional value beyond established outcome predictors. Understanding its role could lead to optimized, individualized treatment plans for post-stroke patients, potentially improving recovery outcomes.
Keywords: CT Angiography; Stroke; Thrombectomy.
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