Plasma D-dimer may predict poor functional outcomes through systemic complications after aneurysmal subarachnoid hemorrhage
Bibliographic record
Abstract
OBJECTIVE Plasma D-dimer levels elevate during acute stages of aneurysmal subarachnoid hemorrhage (SAH) and are associated with poor functional outcomes. However, the mechanism in which D-dimer elevation on admission affects functional outcomes remains unknown. The aim of this study is to clarify whether D-dimer levels on admission are correlated with systemic complications after aneurysmal SAH, and to investigate their additive predictive value on conventional risk factors for poor functional outcomes. METHODS A total of 187 patients with aneurysmal SAH were retrospectively analyzed from a single-center, observational cohort database. Correlations of plasma D-dimer levels on admission with patient characteristics, initial presentation, neurological complications, and systemic complications were identified. The authors also evaluated the additive value of D-dimer elevation on admission for poor functional outcomes by comparing predictive models with and without D-dimer. RESULTS D-dimer elevation on admission was associated with increasing age, female sex, and severity of SAH. Patients with higher D-dimer levels had increased likelihood of nosocomial infections (OR 1.22 [95% CI 1.07-1.39], p = 0.004), serum sodium disorders (OR 1.11 [95% CI 1.01-1.23], p = 0.033), and cardiopulmonary complications (OR 1.20 [95% CI 1.04-1.37], p = 0.01) on multivariable analysis. D-dimer elevation was an independent risk factor of poor functional outcome (modified Rankin Scale Score 3-6, OR 1.50 [95% CI 1.15-1.95], p = 0.003). A novel prediction model with D-dimer had significantly better discrimination ability for poor outcomes than conventional models without D-dimer. CONCLUSIONS Elevated D-dimer levels on admission were independently correlated with systemic complication, and had an additive value for outcome prediction on conventional risk factors after aneurysmal SAH.
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) | 0.001 | 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) | 0.000 | 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".