Abstract 131: Trends in Cerebral Venous Thrombosis: Analysis of the National Inpatient Sample 2016‐2020
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,004 | 0,005 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».