Abstract 18: Diffusion Weighted Imaging Lesions in Patients With Acute Intracerebral Hemorrhage: A Pooled Analysis of Individual Patient Data From MISTIE-III, ATACH-II, I-DEF, and ERICH
Bibliographic record
Abstract
Introduction: The etiology and significance of diffusion weighted imaging (DWI) lesions in patients with acute intracerebral hemorrhage (ICH) remain unclear. We evaluated which factors were associated with DWI lesions, whether associated factors differed by ICH location, and whether DWI lesions were associated with functional outcomes. Methods: We pooled individual patient data from the MISTIE III trial, the ATACH-II trial, the i-DEF trial, and the ERICH study. We included only patients who underwent protocolized magnetic resonance imaging (MRI) of the brain. A poor functional outcome was defined as a modified Rankin Scale (mRS) score of 4-6 at 3-6 months. We used mixed effects logistic regression with the study database as a random effect. Results: Among 1,775 ICH patients, there were 621 (35.6%) lobar, 978 (55.9%) deep, and 148 (8.5%) infratentorial ICHs. Median time to MRI scan was 1.5 days (IQR, 1-4). DWIHLs occurred in 559 (31.5%) patients, with 190 (34.3%) in lobar ICH and 320 (57.8%) in deep ICHs. In mixed effects regression models, factors associated with DWIHLs included younger age factors associated with DWIHLs after acute ICH included younger age (OR, 0.98; 95% CI, 0.97-0.99), black race (OR, 1.59; 95% CI, 1.18-2.16), admission systolic blood pressure (SBP per 10 mm Hg, OR, 1.13; 95% CI, 1.05-1.22), cerebral microbleeds (OR, 1.71, 95% CI, 1.24-2.35), and leukoaraiosis (OR, 1.60; 95% CI, 1.14-2.25). Patients with DWIHLs had higher odds of mRS 4-6 (OR, 1.57; 95% CI, 1.24-1.99) compared to those without, after adjustment for demographics and ICH severity. In subgroup analyses, similar factors influenced DWIHLs in deep ICH. However, in lobar ICH, younger age, admission SBP, and leukoaraiosis were associated with DWIHLs. Presence of DWIHLs was independently associated with poor mRS in deep ICH but not in lobar ICH. There was no relationship between acute BP lowering and DWIHLs, regardless of location. Conclusions: In a large, heterogeneous cohort of ICH patients, our results are consistent with the hypothesis that DWIHLs represent the effects of chronic hypertensive vasculopathy and acute blood pressure elevation. Furthermore, DWIHLs portend poor prognosis after ICH, particularly in deep hemorrhages.
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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 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.013 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".