Intensive Blood Pressure Reduction is Associated with Reduced Hematoma Growth in Fast Bleeding Intracerebral Hemorrhage
Bibliographic record
Abstract
OBJECTIVE: Patients with spontaneous intracerebral hemorrhage (ICH) at the highest risk of hematoma growth are those with the most potential to benefit from anti-expansion treatment. Large clinical trials have not definitively shown a clear benefit of blood pressure (BP) reduction. We aim to determine whether intensive blood pressure reduction could benefit patients with fast bleeding ICH. METHODS: An exploratory analysis of data from the Antihypertensive Treatment of Acute Cerebral Hemorrhage 2 (ATACH-2) randomized controlled trial was performed. In order to capture not just early bleeding (even if a small amount), but the rate of bleeding (ml/hour), we restricted the study to "Fast bleeding ICH," defined as an ICH volume/onset to computed tomography (CT) time >5 ml/hr. Hematoma growth, as defined as an increase of hematoma volume > 33% between baseline and 24 hours. RESULTS: A total of 940 patients were included (mean age = 62.1 years, 61.5% men), of whom 214 (22.8%) experienced hematoma expansion. Of these, 567 (60.3%) met the definition of "fast bleeding" with baseline ICH volume/time to presentation of at least 5 ml/hr. Intensive BP reduction was associated with a significantly lower rate of hematoma growth in fast bleeding patients (20.6% vs 31.0%, p = 0.005). In a subgroup of 266 (46.9%) fast-bleeding patients who received treatment within 2 hours after symptom onset, intensive BP lowering was associated with improved functional independence (odds ratio [OR] = 1.98, 95% confidence interval [CI] = 1.06-3.69, p = 0.031). INTERPRETATION: Our results suggest that early use of intensive BP reduction may reduce hematoma growth and improve outcome in fast bleeding patients. ANN NEUROL 2023.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".