Blood–brain barrier opening as a predictor of epilepsy and mortality after subarachnoid haemorrhage
Bibliographic record
Abstract
BACKGROUND: We determined the predictive power of semi-automated blood-brain barrier assessment and other variables collected during neurocritical care for the outcome of 'epilepsy or late death' following aneurysmal subarachnoid haemorrhage. METHODS: This is a secondary analysis of the prospective, non-interventional, prognostic DISCHARGE-1-cohort from six university hospitals in Germany. All patients who underwent at least one contrast-enhanced MRI during neurocritical care were included. Initial clinical scores and Modified Rankin Scale at day 14 were available. Subdural electrocorticography was scored for seizures and spreading depolarisations. Two MRIs, one post-aneurysm occlusion and another post-neuromonitoring, were semi-automatically segmented into cerebrospinal fluid spaces, normal brain tissue, and abnormal brain tissue. Normal and abnormal tissue were further classified into tissue with "intact" or "dysfunctional" blood-brain barrier. Epilepsy and late death were determined at a median of 3.7 years. FINDINGS: Abnormal, barrier-dysfunctional tissue as a percentage of intracranial volume on post-monitoring MRI was the only independent predictor of early death within three weeks among 130 patients. In the 121 early survivors, this variable was also the only independent predictor of 'epilepsy or late death'. This result, obtained by a combination of imputation and the leaving-one-out method, was confirmed in two sensitivity analyses within smaller populations and with fewer missing values. INTERPRETATION: The study substantiates previous experimental evidence that blood-brain barrier dysfunction plays a key role in epileptogenesis after brain injuries. Contrast-enhanced MRI, a minimally invasive technique, highlighted abnormal, barrier-dysfunctional tissue as a stand-alone independent predictor, underscoring its potential as a 'precision medicine' tool in early diagnosis and intervention. FUNDING: JPD and AF report a grant from the Era-Net Neuron EBio2 with funds from BMBF 01EW2004 and CIHR Award No. NDD 168164. JPD reports a grant from DFG DR 323/10-2 (project number: 413848220) and EU Horizon MSCA-DN 101119916-SOPRANI. AF reports grants from the Canadian Institutes of Health Research (CIHR) PJT 148896 and Israel Science Foundation (ISF) 2254/20. NH is Berlin Institute of Health Clinical Fellow, funded by Stiftung Charité.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.001 | 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 teacher head, 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".