Pre-morbid Risk Factor Control Differences by Age in Patients Undergoing Thrombectomy for Acute Ischemic Stroke (P8-5.014)
Notice bibliographique
Résumé
Objective: We proposed to estimate the percentage of large vessel strokes that are potentially attributable to unmanaged vascular risk factors and how this may differ by age to improve stroke prevention and patient outcomes. Background: Controlling metabolic and behavioral risk factors may avert 75% of the world stroke burden. We anticipate risk factors, and their management may differ depending on age of patient and may offer insight into preventative strategies. Design/Methods: A retrospective chart review was conducted on patients undergoing endovascular therapy from 2012–2019 at University of Maryland. Patients were stratified based on age into three groups: <50, 50–70, and >70 years of age. Risk factors prior to admission including hypertension, diabetes, hyperlipidemia, atrial fibrillation, history of vascular disease, and current smoking were recorded. A possibly preventable stroke was defined as having a history of at least one of these risk factors that was not being optimally controlled according to current guidelines. Chi squared tests of proportion were used to evaluate differences between age groups. Results: 396 patients underwent thrombectomy (50% female, mean age 64.7). The most prominent etiology of stroke across all age groups was cardioembolic. 62% of strokes in patients <50 years of age were preventable as compared to 82% in patients 50–70 years old and 79% in patients older than 70. Smoking was a common risk factor in all age groups. Despite overall lower incidence of diabetes and hyperlipidemia in patients <50 with stroke, a large proportion of these patients presented with poor control of these risk factors (p=0.04 and p=0.01). Conclusions: Most patients who underwent thrombectomy had at least one cardiovascular risk factor that was uncontrolled despite age group. This illustrates the importance of cardiovascular risk factor management in primary prevention of stroke, especially control of diabetes and hyperlipidemia in the younger population. Disclosure: Dr. Grimes has nothing to disclose. Dr. Mehndiratta has nothing to disclose. Dr. Chaturvedi has received personal compensation in the range of $500-$4,999 for serving as a Consultant for Astra Zeneca. Dr. Chaturvedi has received personal compensation in the range of $500-$4,999 for serving on a Scientific Advisory or Data Safety Monitoring board for University of Calgary. Dr. Chaturvedi has received personal compensation in the range of $10,000-$49,999 for serving as an Editor, Associate Editor, or Editorial Advisory Board Member for American Heart Association. Dr. Chaturvedi has received personal compensation in the range of $10,000-$49,999 for serving as an Expert Witness for Ramar & Paradiso. Dr. Chaturvedi has received personal compensation in the range of $10,000-$49,999 for serving as an Expert Witness for Cole, Scott, Kissane. The institution of Dr. Chaturvedi has received research support from NINDS.
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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 | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
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 ».