Abstract 131: Trends in Cerebral Venous Thrombosis: Analysis of the National Inpatient Sample 2016‐2020
Bibliographic record
Abstract
Introduction Cerebral venous thrombosis (CVT) is an uncommon form of stroke with relatively low mortality but higher incidence in younger adults.1–3 Previous work has suggested decreased overall stroke hospitalization volumes, but preserved CVT hospitalization volumes and increased CVT mortality during the COVID‐19 pandemic.4,5 We sought to provide updated incidence and trend data for cerebral venous thrombosis (CVT) in the United States from 2016‐2020, examine the impact of the COVID‐19 pandemic on CVT, and identify predictors of in‐hospital mortality. Methods Validated ICD‐10 codes were used to identify patients with CVT in the National Inpatient Sample (NIS) between 2016 and 2020. The NIS is part of the Healthcare Cost and Utilization Project (HCUP) and is maintained the Agency for Healthcare Research and Quality. The NIS provides a stratified nationally representative 20% sample of all hospital discharges in the United States, excluding rehabilitation and long‐term acute care hospitals. Annual updates to the NIS are released approximately 20 months after the conclusion of the data year. Sample weights were applied to generate nationally representative estimates, and census data were used to compute incidence rates. The first wave of the COVID‐19 pandemic was defined as January‐May 2020. Predictor variables for mortality were selected based upon previous studies of incidence and outcomes of CVT and biological plausibility.6–8 Multivariable logistic regression was conducted using all predictor variables that achieved p<0.10 in univariable regression. Trend analysis was completed using Joinpoint regression. Results From 2016 to 2020, the incidence of CVT increased from 24.34 per 1,000,000 population per year (MPY) to 33.63 per MPY (Annual Percentage Change (APC) 8.6%; p<0.001). CVT incidence was higher in women than men (37.07 per MPY vs 30.10 per MPY) and the rate of increase was also higher in women (APC 10.1% vs APC 6.8%). Racial differences in incidence rate increases were noted, with incidence increasing by 9.8% annually for White patients, 16.1% for Black patients, and 6.7% for Hispanic patients. All‐cause in‐hospital mortality was 4.9% [95% CI 4.5‐5.4]. On multivariable analysis, use of thrombectomy, increased age, atrial fibrillation, stroke diagnosis, infection, presence of prothrombotic hematologic conditions, and male sex were associated with in‐hospital mortality. CVT incidence was similar comparing the first 5 months of 2020 and 2019 (31.37 vs 32.04; p=0.322) with no difference in median NIHSS (2 [IQR 1‐10] vs. 2 [1‐9]; p=0.959) or mortality (4.2% vs. 5.6%; p=0.176). Mortality was 6.7% [2.3‐17.9] in patients with both CVT and COVID (vs. no COVID 5.5% [4.6‐6.6]; p=0.705). Conclusion CVT incidence increased in the US from 2016 to 2020 while mortality did not change. CVT incidence was higher in women and Black patients. Increased age, prothrombotic state, stroke diagnosis, infection, atrial fibrillation, male sex, and use of thrombectomy were associated with in‐hospital mortality following CVT. During the first wave of the COVID‐19 pandemic, CVT volumes and mortality were similar to the prior year.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".