Abstract 129: Endovascular Therapy in Cerebral Venous Thrombosis: Nationwide Insights and Implications for Mortality
Bibliographic record
Abstract
Introduction Cerebral Venous Thrombosis (CVT) is a rare cerebrovascular condition causing death or functional dependency in 10‐15%. Although Endovascular Therapy (EVT) is deployed variably across centers for managing CVT, the determinants and consequences of such use remain under‐explored. We examined the demographic and clinical correlates of EVT use in CVT and assessed its impact on patient outcomes utilizing a national database. Methods We identified CVT cases from 2015 to 2020 in the National Inpatient Sample, with EVT as the primary exposure. CVT and other diagnoses, and EVT and other procedures, were identified using standard ICD 10 codes. A backward stepwise regression model (at p<0.05) was used to explore patient demographics, hospital characteristics, and comorbidities significantly associated with EVT use. We examined the association between EVT use and in‐hospital mortality using survey weighted unadjusted and adjusted logistic regression models. Covariates in the model one assessing inpatient mortality included age, insurance status, hospital teaching status, hospital volume size, central nervous system (CNS) infections, CNS cancer, other types of cancer, hematological disorder, traumatic head injuries, seizure, intubation, and transfer from an outside hospital. Model two additionally adjusted for female sex, NIH Stroke Scale (NIHSS), and Elixhauser mortality index. Results We identified 48,835 individuals with CVT, of whom 1,275 (2.6%) underwent EVT. The stepwise regression analysis revealed a higher likelihood of receiving EVT with older age (OR per year: 1.01, 95% CI: 1.01‐1.02, p<0.001), presence of a hematological disorder (OR: 2.06, 95% CI: 1.58‐2.67, p<0.001), seizure (OR: 1.54, 95% CI: 1.09‐2.16, p=0.014), intubation (OR: 3.85, 95% CI: 2.78‐5.34, p<0.001), higher NIHSS (10.5[4‐18] versus 3[1‐8], p<0.001), or admission to a teaching hospital (OR: 4.15, 95% CI: 1.93‐8.92, p<0.001), high‐volume center (OR: 6.30, 95% CI: 2.29‐17.33, p<0.001), or transfer from an outside hospital (OR: 1.89, 95% CI: 1.42‐2.51, p<0.001). Conversely, there was a lower likelihood of EVT in those with CNS infections (OR: 0.19, 95% CI: 0.07‐0.48, p=0.001), CNS cancer (OR: 0.24, 95% CI: 0.06‐0.93, p=0.039), other types of cancer (OR: 0.47, 95% CI: 0.32‐0.69, p<0.001), and traumatic head injuries (OR: 0.41, 95% CI: 0.25‐0.68, p<0.001). Individuals with Medicare (OR: 0.39, 95% CI: 0.26‐0.59, p<0.001) or Medicaid insurance (OR: 0.70, 95% CI: 0.50‐0.99, p=0.041), were also less likely to undergo EVT. EVT was associated with an increased odds of in‐hospital mortality (OR: 4.01, 95% CI 2.82‐5.68, p<0.001), a relationship that persisted even after multivariable adjustment with model one (adjusted OR: 2.37 95% CI 1.50‐3.74, p<0.001) and model two (adjusted OR: 7.32 95% CI 2.62‐20.5, p<0.001). Conclusion We identified specific demographic and clinical factors associated with EVT use in CVT and found EVT use linked to higher mortality. The directionality of this relation is uncertain but could suggest more frequent use of EVT in individuals with very severe clinical presentations. These findings underscore the need for further prospective work to assess which individuals may benefit from therapy, in addition to optimal timing and approaches.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".