Abstract 061: Identification of Proteins Predictive of Post‐Thrombectomy Outcome Based on an Appalachian vs. Non‐Appalachian Cohort
Notice bibliographique
Résumé
Introduction The Appalachia region of North America is known to have significant health disparities, specifically, worse risk factors and outcomes for stroke. Appalachians are more likely to have comorbidities related to stroke, such as diabetes, obesity, and tobacco use, and are often less likely to have stroke interventions such as mechanical thrombectomy (MT) for emergent large vessel occlusion (ELVO). As our Comprehensive Stroke Center directly serves stroke subjects from both Appalachian and non‐Appalachian areas, we set out to identify proteomic biomarkers predictive of stroke outcomes specific to subjects residing in Appalachia. Methods Eighty‐one subjects met inclusion criteria for this study. These subjects underwent MT for ELVO, and during the procedure, carotid arterial blood samples were acquired and subsequently sent for proteomic analysis. Samples were processed in accordance with the Blood And Clot Thrombectomy Registry And Collaboration (BACTRAC; clinicaltrials.gov; NCT 03153683). Statistical analyses were utilized to examine whether relationships between protein expression and outcomes differed by Appalachian status for functional outcomes (NIH Stroke Scale; NIHSS and Modified Rankin Score; mRS), cognitive outcomes (Montreal Cognitive Assessment; MoCA), and mortality. Results No significant differences were found in demographic data nor co‐morbidities when comparing Appalachia to non‐Appalachia subjects. However, time from stroke onset to treatment (last known normal) was significantly longer in patients from Appalachia, so this datapoint was entered as a co‐variate in all predictive models. A comprehensive analysis of 184 cardiometabolic and inflammatory proteins revealed seven Appalachia‐specific proteins predictive of NIHSS, fourteen predictive of MoCA, six predictive of mRS, and seven proteins related to mortality. Specifically, within the Appalachian group, the protein multiple epidermal growth factor‐like domains protein 9 (MEGF9) was positively correlated to discharge NIHSS, elevated levels of the proteins coagulation factor XI (F11) and mannose binding protein (MBL2) were found to have an increased likelihood of worse mRS, but there were no proteins identified from the Appalachian cohort that were predictive of worse MoCA score, nor worse mortality. Conclusion Appalachia is an underserved population with significantly worse health disparities, specifically related to ischemic stroke. Our study found that patients who presented from Appalachian regions have a different proteomic response at time of MT when compared to otherwise similar subjects presenting from non‐Appalachian communities. These differentially expressed proteins could be used as prognostic biomarkers as well as novel therapies targeted at an underserved population. Lastly, expression differences may be in part related to environmental exposures, such as coal pollution, which will require additional studies moving forward.
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 | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 ».