4 The Association Between Pro-Inflammatory Cytokines and C-Reactive Protein and the Cognitive and Neurological Outcome in Stroke Survivors: A Systematic Review
Notice bibliographique
Résumé
Objective: Childhood ischemic and hemorrhagic stroke is often associated with neuropsychological and cognitive deficits. Stroke induces an inflammatory response in the central and peripheral nervous systems. High levels of inflammatory markers in the plasma have been associated with poorer cognitive outcomes. The role of inflammation in neurological prognosis of stroke has been studied previously; however, there is a limited understanding of the association between inflammatory markers and neuropsychological outcome post-stroke. The present review examined the existing literature on the association between inflammatory markers and post-stroke functioning. Participants and Methods: Data bases (PsycINFO, PubMed, Web of Science, and Ovid) were reviewed in October 2020. Articles were restricted to English-language literature. Articles were included regardless of recruitment setting, number of strokes, mechanism of stroke, timing of blood collection and outcome assessment. The articles focused on patients with stroke (between the ages of 0 to 95), measured post-stroke outcome by neurological and cognitive outcome measures (i.e., it included findings on any aspect of cognition such as memory, information processing, or attention), and on pro-inflammatory cytokines and c-reactive proteins as measures of inflammation. The systematic literature search retrieved 954 articles to review against inclusion criteria. Descriptive statistics were performed using IBM SPSS 27.0 Statistics Software. Results: A total of 18 articles were included in this review. The population age ranged from 21 to 95, and, when reported (n=17), mean participant age was 66.31. Among stroke patient populations, ischemic stroke was most researched (n=15). The most widely investigated biomarkers were CRP (n=9), IL-6 (n=8), TNF- a (n=7), IL-1 b (n=5), and IL-10 (n=5). The time of initial blood collection ranged from on admission to within 3 months poststroke. Equal number of studies used both neurological and cognitive tests (n=7), or only neurological (n=7), 2 studies only used cognitive tests, and one study used all three types of measures. The most commonly used cognitive test was the Mini Mental State Examination, MMSE (n=7). The next commonly used cognitive test was the Montreal Cognitive Assessment (MoCA), (n=4). Only two studies used a comprehensive neuropsychological battery. Conclusions: There is a lack of research into diverse stroke populations. All the studies examined the association between inflammatory markers and the post-stroke outcomes in adult populations and mostly in patients with ischemic stroke. The lack of research on pediatric and young adult stroke represents a significant gap in understanding predictors of neurological and cognitive outcomes. Further, the review revealed a lack of comprehensive neurocognitive assessment post stroke, with most studies measuring neuropsychological outcome using brief cognitive instruments. Our findings highlight a critical need for addressing the above gaps to help elucidate the role of inflammatory markers in the neuropsychological prognosis of stroke in younger populations.
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 | 0,005 | 0,023 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,009 | 0,008 |
| Bibliométrie | 0,011 | 0,011 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,002 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 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 ».