Impact of leukoaraiosis on the infarct growth rate and clinical outcome in acute large vessel occlusion stroke after endovascular thrombectomy
Bibliographic record
Abstract
Abstract Introduction: As a marker of chronic cerebral small vessel disease, leukoaraiosis (LA) was reported to impact the recruitment of collaterals in acute ischemic stroke (AIS). We intended to explore the impact of LA on the infarct growth rate (IGR) and clinical outcome by impaired collateral development in AIS patients with large vessel occlusion (LVO) who underwent endovascular thrombectomy (EVT). Patients and methods: Two hundred thirty-six AIS patients who underwent EVT were retrospectively reviewed. The severity of LA was graded using the Fazekas scale with non-contrast CT. IGR was calculated by the acute core volume on CT perfusion divided by the time from stroke onset to imaging. The collateral status after LVO was assessed using the ASITN/SIR collateral scale. The clinical outcomes after EVT were evaluated using a modified Rankin Scale (mRS). The Alberta stroke program early CT score (ASPECTS), the National Institutes of Health Stroke Scale (NIHSS) score at admission, and the modified treatment in cerebral infarction (mTICI) score after EVT were also included. Correlations between those factors were analyzed. Results: Patients with severe LA had significantly larger core volume on CTP (p = 0.022) and lower collateral grade (p < 0.001). Faster IGR was significantly associated with higher LA severity (adjusted odds ratio [aOR]: 1.53; 95% CI: 1.02–2.33; p = 0.046), higher NIHSS (aOR: 1.04; 95% CI: 1.00–1.09; p = 0.032) and impaired collaterals (aOR: 2.26; 95% CI: 1.27–4.03; p = 0.005). In mediation analysis, collaterals explained 33% of the effect of LA on fast IGR. There was correlation between the severity of LA and mRS (p = 0.007). Discussion and Conclusion: The increasing severity of LA is associated with impaired collateral status and fast infarct growth. These findings suggest that LA may become a predictive imaging biomarker for the likelihood of progression of tissue injury and clinical outcome after EVT in acute large vessel occlusion stroke.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".