Abstract WP462: New or Expanding Ventricular Hemorrhage Predicts Poor Outcome After Intracerebral Hemorrhage
Bibliographic record
Abstract
Introduction: Baseline intraventricular hemorrhage (IVH) is a predictor of poor outcome in acute intracerebral hemorrhage (ICH) patients. However, questions remain as to the exact burden that new IVH development, seen on follow-up imaging, or what degree of interval IVH expansion, impacts long term functioning. Objective: To derive and validate a relationship between IVH change and long term outcome. Methods: Fractional polynomial analysis was used to test linear and non-linear models of 24-hour IVH change and clinical outcome using data from the multicenter PREDICT study. The primary outcome was mRS 4-6 at 90 days. Dichotomous thresholds were derived via assessment of the selected model and diagnostic accuracy measures were calculated. Independent predictors of poor outcome were determined via multivariable logistic regression. The developed model and all derived thresholds were validated in an independent single center cohort. Results: Of the 256 patients from PREDICT, 127 (49.6%) had mRS scores of 4-6 at 90 days. 24-hour IVH change and the primary outcome fit a non-linear relationship, where minimal increases in IVH were associated with a high probability of poor outcome (Figure 1). Mean IVH expansion was 8.6 mL. IVH expansion greater than 1 mL (n=53, Sens 33%, Spec 92%, PPV 79%, NPV 58%, aOR 2.77 [95% CI: 1.12-6.89]) and development of any new IVH (n= 74, Sens 43%, Spec 85%, PPV 74%, NPV 60%, aOR 2.17 [95% CI: 1.02-4.63]) strongly predicted mRS 4-6 at 90 days. The model and developed thresholds reproduced well in a validation cohort of 170 patients. Conclusion: IVH expansion as minimal as 1 mL, or any new IVH is strongly predictive of poor outcome. This can aid in prognostication, be incorporated into definitions of hematoma expansion for future ICH treatment trials, or even imply that IVH treatment is a therapeutic target that may lead to improved outcomes.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".