Characterizing Risk Factors of Neurocognitive Impairments in Post COVID-19 Condition
Notice bibliographique
Résumé
ABSTRACT Introduction Post COVID-19 Condition (PCC) affects a substantial proportion of COVID-19 survivors and is frequently associated with persistent neurocognitive impairments. Despite its prevalence, the pathophysiological mechanisms underlying PCC-related neurocognitive decline remain unclear, although neuroinflammation is strongly implicated. Identifying key clinical and inflammatory risk factors associated with neurocognitive assessment scores in individuals with PCC will inform approach to care in clinical settings, support the development of diagnostic criteria, and guide targeted interventions. We hypothesized that individuals with lower MOCA and SDMT scores will exhibit elevated levels of systemic pro-inflammatory biomarkers compared to those with higher neurocognitive scores. Methods This retrospective, cross-sectional study examined 61 individuals with PCC recruited from Alberta Health Services Long COVID-19 Inter-Professional Outpatient Program, a specialized care center for post-COVID care. An additional 18 healthy controls were included for groupwise comparisons. Baseline demographic, clinical, and biochemical data were analyzed cross-sectionally alongside Montreal Cognitive Assessment (MOCA) and Symbol Digit Modalities Test (SDMT) scores. Multivariable linear regression was used to identify covariates associated with cognitive performance. To assess the likelihood of neurocognitive impairment within the PCC cohort, logistic regression was applied using established MOCA and SDMT cut-offs to classify participants as either neurocognitively impaired (NCI+) or unimpaired (NCI–). Group comparisons of demographics and biomarker levels were conducted using the Mann–Whitney U test and Fisher’s exact test. Results Older age was negatively associated, while female gender and higher education were positively associated with MOCA scores. SDMT scores were negatively associated with older age, PHQ-9 scores, and a prior diagnosis with depression or anxiety. Logistic regression analysis revealed that female gender was associated with lower odds of MOCA-defined neurocognitive impairment, whereas higher PHQ-9 scores and a history of depression or anxiety significantly increased the odds of SDMT-defined neurocognitive impairment. Serum IL-18 was the biomarker consistently associated with lower MOCA and SDMT scores in multivariable linear regression analysis, although it did not remain significant in logistic models. Groupwise comparisons confirmed IL-18 as the biomarker significantly elevated in NCI+ compared to NCI- individuals. Conclusion Neurocognitive impairments are highly prevalent deficits in PCC and are closely linked to mood disorders and the inflammatory biomarker, IL-18. This study highlights the importance of routine neurocognitive assessments in individuals with PCC, particularly those with risk factors such as older age, male gender, lower educational attainment, current or previous psychiatric vulnerabilities, and elevated serum IL-18 levels.
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 | 0,000 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| 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 ».