ArticleFrontiers in neurology2022
Automated quantification of atrophy and acute ischemic volume for outcome prediction in endovascular thrombectomy.
Article in Frontiers in neurology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed, 4 citations in OpenAlex.
- Deep Vein Thrombosis Prevention in Acute Ischemic Stroke Patients with Lower Limb Paralysis: A Narrative Review.Journal of clinical medicine · 2026Review
- Automatically quantified follow-up imaging biomarkers predict clinical outcomes after acute ischemic stroke.Frontiers in neurology · 2025Article
- Potential and limitations of computed tomography images as predictors of the outcome of ischemic stroke events: a review.Frontiers in stroke · 2023Review
- Utilizing Quantitative Analysis of CSF Volume from Clinical T1-Weighted MRI to Predict Thrombectomy Outcomes.Journal of neuroimaging : official journal of the American Society of NeuroimagingArticle
Corrections and comments
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Authors and funding
13 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Short- and long-term outcomes from endovascular thrombectomy (EVT) for large vessel occlusion stroke remain variable. Numerous relevant predictors have been identified, including severity of neurological deficits, age, and imaging features. The latter is typically defined as acute changes (most commonly Alberta Stroke Programme Early CT Score, ASPECTS, at presentation), but there is little information on the impact of imaging assessment of premorbid brain health as a determinant of outcome. Aims: To examine the impact of automated measures of stroke severity and underlying brain frailty on short- and long-term outcomes in acute stroke treated with EVT. Methods: In 215 patients with anterior circulation stroke, who subsequently underwent EVT, automated analysis of presenting non-contrast CT scans was used to determine acute ischemic volume (AIV) and e-ASPECTS as markers of stroke severity, and cerebral atrophy as a marker of brain frailty. Univariate and multivariate logistic regression were used to identify significant predictors of NIHSS improvement, modified Rankin scale (mRS) at 90 and 30 days, mortality at 90 days and symptomatic intracranial hemorrhage (sICH) following successful EVT. Results: For long-term outcome, atrophy and presenting NIHSS were significant predictors of mRS 0-2 and death at 90 days, whereas age did not reach significance in multivariate analysis. Conversely, for short-term NIHSS improvement, AIV and age were significant predictors, unlike presenting NIHSS. The interaction between age and NIHSS was similar to the interaction of AIV and atrophy for mRS 0-2 at 90 days. Conclusion: Combinations of automated software-based imaging analysis and clinical data can be useful for predicting short-term neurological outcome and may improve long-term prognostication in EVT. These results provide a basis for future development of predictive tools built into decision-aiding software in stroke.
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Registered trials
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