FACTORS INFLUENCING SUBJECTIVE MENTAL ALTERATIONS: UNRAVELING THE LUPUS BRAIN FOG
Notice bibliographique
Résumé
PV043 / #685 Poster Topic: AS05 - CNS Lupus Background/Purpose Brain fog is a common symptom in systemic lupus erythematosus (SLE) patients that refers to a “clouding of mental functions.” Its prevalence is probably underestimated due to the fact that a universal definition of “lupus fog” does not exist. The aim of the study was to evaluate in a cohort of SLE patients the prevalence of subjective mental alterations, named “lupus fog” (LF) and objective mental alterations (depression, cognition, fatigue) adopting screening tools validated in SLE. The second objective was to investigate which factors are associated with LF. Methods A cross-sectional study was conducted enrolling adult SLE patients (ACR/EULAR 2019 criteria). LF referred to the presence of mental alterations (ie, memory, concentration, attention impairment) as reported by participants. Demographic, clinical, clinimetrics, therapeutic data were collected (Table). Serum anti-ribosomal P antibodies (anti-Rib-P) were quantified using ELISA kits. Cognitive deficits were screened using the Montreal Cognitive Assessment (MoCA) test performed by certified personnel (using the standard cut-off of <26/30 and a more sensitive cut-off <28/30)[1] and assessed by a neuropsychologist exploring deficits in 8 cognitive domains with a battery of neuropsychological tests. Depressive symptoms were evaluated using the Center for Epidemiologic Studies Depression Scale (CES-D) and adopting a more sensitive cut-off (>15) and the cut-off suggested by Kwan et al (>25).[2] Fatigue was measured using the the Functional Assessment of Chronic Illness Therapy (FACIT-F) (cut-off <30 and a more sensitive cut-off <34).[3] Chi-squared and the Mann-Whitney test were used for univariate analysis; multivariate analysis was performed building logistic regression models including variables showing p values of <0.10. Table. Results 114 SLE patients were enrolled (Table), 105 female (92.1%), mean age 43.7 years (+-12.2). LF was found in 54/100 patients (54%). CES-D >15 was altered in 56/105 pts (53.3%), CES-D >25 in 26 (24.7%), MoCA <26 in 45/100 pts (45%), MoCA <28 in 75 (75%), FACIT <30 in 31/69 (44.9%) and FACIT <34 in 36 (52.9%). At univariate analysis, an association emerged between the presence of LF and CES-D alterations (CES-D >15 p=0.014; CES-D >25 p=0.010, CES-D score p<0.001), FACIT (FACIT <30 p=0.039, FACIT <34 p=0.042, FACIT score p=0.012), fibromyalgia (p<0.001), the neuropsychiatric involvement (NPSLE) (p=0.015), anti-Ribonucleoprotein RNP (p=0.014), anti Rib-P (p=0.018) and disease duration (p=0.045). No association was found with MoCA test, the battery of neuropsychological test, disease activity scores or treatment. Two different models of logistic regression were built. Model 1 including fibromyalgia, showed independent association between LF and fibromyalgia (OR=34.6; 95% CI 2.3-523.1, p=0.011), CES-D score (OR=1.1 per unit, 95% CI 1.0-1.2, p=0.033) and disease duration (OR=1.1 per year, 95% CI 1.0-1.2, p=0.038). Model 2 excluding fibromyalgia showed independent association between LF and FACIT score (OR=0.93 per unit, 95% CI 0.88-0.99, p=0.013), anti-Rib-P (OR=5.2 per unit, 95% CI 1.0-1.2, p=0.033). Conclusions Lupus fog is frequent. Our study also confirms the high prevalence of cognitive impairment, depressive symptoms and fatigue in SLE. A longer disease duration, depressive symptoms and the diagnosis of fibromyalgia were factors independently associated with lupus fog. Our findings suggest that SLE patients frequently have a negative perception about proper cognitive performances, without having a real impairment, supporting the multifactorial etiology of clinical issues in SLE, related to direct and indirect factors. References: [1.] Raghunath S. Lupus Sci Med 2021;8(1):e000580. [2.] Kwan A. Semin Arthritis Rheum 2019;49(2):260-6. [3.] Kawka L. RMD Open 2023;9:e003476.
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 | 0,002 |
| 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 | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| 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 ».