Intracranial Atherosclerotic Disease in Acute Ischemic Stroke: Clinical Predictors, Imaging Profiles, and Treatment Outcomes: An Analysis from RITE Registry
Bibliographic record
Abstract
Abstract Background Intracranial atherosclerotic disease (ICAD) is a leading cause of ischemic stroke worldwide, with a significant prevalence in specific populations. This study aims to compare the clinical characteristics, imaging profiles, and outcomes of stroke patients with large vessel occlusion (LVO) caused by ICAD versus non-ICAD etiologies, particularly in those transferred for endovascular treatment (EVT). Methods A retrospective analysis was conducted using data from the Riyadh Thrombectomy Registry, including 219 patients with acute ischemic stroke who underwent EVT between January 2019 and March 2024. Clinical, demographic, and imaging data were collected and compared between ICAD and non-ICAD groups. Imaging modalities included computed tomography perfusion (CTP) and angiography, with key metrics such as the hypoperfusion intensity ratio (HIR) and cerebral blood volume (CBV) index evaluated for prognostic utility. Results Patients with ICAD were older and demonstrated a higher prevalence of vascular risk factors. ICAD patients presented with better ASPECTS scores, smaller ischemic core volumes, and higher mismatch ratios, reflecting better collateral circulation. However, despite favorable imaging profiles, ICAD patients exhibited higher mortality rates, particularly in cases involving basilar artery occlusion. HIR and CBV indices demonstrated modest diagnostic performance in distinguishing ICAD from non-ICAD, with an area under the curve (AUC) of 0.62 for HIR and 0.57 for CBV. Conclusions ICAD patients undergoing EVT showed distinct clinical and imaging characteristics compared to non-ICAD patients. Despite better collateral circulation, ICAD is associated with higher mortality, especially in posterior circulation strokes. Further research is required to optimize EVT strategies and explore adjunctive therapies tailored to this challenging patient population.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".