Abstract 414: Association of atrophy, lacunes, and white matter hyperintensities with outcomes after thrombectomy: A Bayesian analysis
Bibliographic record
Abstract
Introduction/Purpose Cerebral small vessel disease (CSVD) markers, including brain atrophy, lacunes, and white matter hyperintensities (WMH), are common in patients with ischemic stroke and may influence recovery after mechanical thrombectomy (MT). Their independent association with post‐MT outcomes remains controversial. We sought to evaluate whether CSVD markers assessed on sensitive pre‐treatment MRI are associated with 90‐day poor outcomes (modified Rankin scale mRS 3‐6), post‐MT parenchymal hematoma (PH), and symptomatic intracerebral hemorrhage (SICH). Materials/Methods We conducted a retrospective multicenter, multinational study of consecutive patients with anterior circulation large vessel occlusion who underwent MT and had pre‐treatment MRI. Brain atrophy was graded using the Pasquier scale, WMH using the Fazekas scale, and lacunes were rated as absent or present according to STRIVE‐2 criteria. The primary outcome was poor outcomes at 90 days. Secondary outcomes included ipsilateral PH and SICH during the acute hospitalization. Associations were assessed using multivariable Bayesian logistic regression, adjusting for age, sex, baseline mRS, acute infarct volume, intravenous thrombolysis, symptom onset time, and mTICI 2b‐3 successful reperfusion. Results are presented as Bayesian odds ratios (ORs) with 95% credible intervals (CrIs) and posterior probabilities of association (Pr[OR>1]). Results Among 479 patients (median age 72 years [IQR, 59‐80]; 46% female; 50% received intravenous thrombolysis; 83% achieved successful reperfusion), lacunes were independently associated with poor outcomes (OR=1.87, 95%CrI=1.26‐3.10, Pr[OR>1]=0.99). Brain atrophy (OR=1.80, 95%CrI=1.00‐3.26, Pr[OR>1]=0.99) and moderate‐to‐severe WMH (OR=1.77, 95%CrI=1.01‐3.13, Pr[OR>1]=0.98) were also associated with higher probabilities of poor outcomes (Table 1). With regard to hemorrhagic transformation, WMH showed posterior probabilities suggestive of associations with PH (OR=1.83, 95%CrI=0.81‐4.17, Pr[OR>1]=0.93) and with SICH (OR=2.61, 95%CrI=0.72‐9.85, Pr[OR>1]=0.93). Atrophy (PH: OR=0.49, 95%CrI=0.21‐1.18, Pr[OR>1]=0.08; SICH: OR=0.81, 95%CrI=0.23‐3.02, Pr[OR>1]=0.38) and lacunes (PH: OR=0.95, 95%CrI=0.45‐1.71, Pr[OR>1]=0.45; SICH: OR=1.15, 95%CrI=0.34‐3.90, Pr[OR>1]=0.52) did not appear to be associated with PH or SICH, with posterior probabilities close to 0.5 and wide CrIs. Conclusion In a large dataset of patients with pre‐MT MRI, lacunes were independently associated with 90‐day poor outcomes; atrophy and WMH were also associated with high posterior probabilities of worse functional outcomes. Furthermore, WMH showed suggestive links to PH and SICH. These SVD markers are not contraindications to thrombectomy, but may refine prognostic stratification to guide clinical counseling and warrant further exploration in future studies to understand microvascular vulnerability of ischemic tissue. image
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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 | 0.030 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".