Association of White Matter Disease With Functional Recovery and 90‐Day Outcome After EVT: Beyond Chronological Age
Bibliographic record
Abstract
Background: Patients with white matter disease (WMD) - a key marker of cerebral small vessel disease - may have less brain reserve to cope with ischemic injury. The relationship of WMD to functional recovery after endovascular thrombectomy is uncertain. We aim to explore the association between WMD and functional outcome, assessed at multiple time-points postendovascular thrombectomy. Methods: In this post hoc analysis, we analyzed noncontrast computed tomography-imaging from the ESCAPE-NA1 (Safety and Efficacy of Nerinetide [NA-1] in Subjects Undergoing Endovascular Thrombectomy for Stroke) trial and assessed WMD by using the total Fazekas-score (score range: 0-6). The primary outcome was repeated measurements of the modified Rankin scale (mRS) scores (i.e., day-5/discharge, day-30, and day-90). Secondary outcome measures were the ordinal-mRS at 90-days, 90-day-mRS0-2, and 90-day-mortality. Mixed-linear and binary/ordinal logistic regressions were performed, adjusting for age, sex, baseline National Institutes of Health Stroke Scale, cortical atrophy, chronic infarctions, stroke laterality, follow-up infarct volume, and alteplase-nerinetide interaction. Sensitivity analyses were done including only those patients for whom magnetic resonance imaging was available. Results: <0.001). Patients with Fazekas=3-6 fared worse at every time-point after endovascular thrombectomy, compared with patients with Fazekas=0-1. At 30-days, the adjusted difference of the mean mRS=0.47; 95% CI, 0.22-0.72 and at 90-days: adjusted difference=0.60 (95% CI, 0.36-0.85). Higher WMD-burdens were also associated with worse 90-day mRS (adjusted common odds ratio for Fazekas=3-6 versus 0-1: 1.42; 95% CI, 1.03-1.96). Similar results were found in magnetic resonance imaging-only sensitivity analyses. Conclusion: Patients with more WMD showed worse functional recovery after endovascular thrombectomy, compared with patients without WMD, even after adjusting for age and chronic disease markers like atrophy and chronic infarctions. These data may further help inform treatment expectations and recovery-related planning, by using simple visual ratings on routinely acquired noncontrast computed tomography.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".