Association between time and severe hypoperfusion with risk of hemorrhagic transformation in stroke patients
Bibliographic record
Abstract
INTRODUCTION: Perfusion imaging studies show a substantially increased risk of hemorrhagic transformation (HT) in severely hypoperfused tissue. Preclinical evidence indicates that ischemic damage is influenced not only by the degree of hypoperfusion but also by the duration of exposure to that hypoperfused state. We aim to investigate the association of time and severe hypoperfusion with parenchymal hematoma (PH) in ischemic stroke and explore whether there is a combined effect of the two variables on PH. METHODS: Data are from the ESCAPE-NA1 trial, which evaluated the effect of nerinetide in large vessel occlusion patients treated with thrombectomy. This study included patients with some degree of recanalization (expanded Thrombolysis in Cerebral Infarct [eTICI] > 0) and available baseline CT perfusion. Severe hypoperfusion was defined as at least 1 mL volume of relative cerebral blood flow (rCBF) <20%. We assess 24-h imaging for the presence of PH, according to Heidelberg bleeding criteria. Univariable and multivariable logistic regression analyses, including interaction terms, were used to assess the effect of time and severe hypoperfusion on outcomes. RESULTS: Out of 1105 patients from ESCAPE-NA1, 396 (35.8%) were included. The median age was 70 years (IQR = 59.8-79.2), 202 (51%) were females, and 50 (12.6%) experienced PH. Onset-to-imaging time (adjusted OR 1.04 [95% CI = 1.01-1.06] per 15-min increase) and the presence of severe hypoperfusion (adjusted OR 2.87 [95% CI = 1.47-5.63]) were the only variables associated with PH in multivariable analysis. No significant interaction effect of time and severe hypoperfusion on PH was found. The presence of severe hypoperfusion had a negative predictive value of 98% and a positive predictive value of 39.4% for predicting PH in patients presenting within 3 h and after 6 h from symptom onset, respectively. CONCLUSION: Both severe hypoperfusion and time affect the risk of hemorrhagic transformation. However, the interaction between these two variables was not statistically significant, indicating that their effects on hemorrhagic transformation risk are not dependent on each other. Analyzing these variables may help identify patients with a leaky, severely compromised blood-brain barrier in the ischemic core-a "leaky core."
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".