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Record W4297199976 · doi:10.1136/lupus-2022-elm2022.48

PO.1.15 Lupus brain fog: cognitive impairment and depression in systemic lupus erythematosus

2022· article· en· W4297199976 on OpenAlexaboutno aff
Silvia Fasano, M Patrone, F Riccio, A Milone, L Sadile, M Fabrazzo, E Di Caprio, R Tirri, F Ciccia

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicSystemic Lupus Erythematosus Research
Canadian institutionsnot available
Fundersnot available
KeywordsMontreal Cognitive AssessmentMedicineAnxietyCoping (psychology)Logistic regressionInternal medicineSystemic lupus erythematosusCohortDepression (economics)CognitionPhysical therapyClinical psychologyDiseasePsychiatryCognitive impairment

Abstract

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Purpose To investigate the prevalence and main determinants of cognitive dysfunction, anxiety and depression in a cohort of patients with Systemic Lupus Erythematosus (SLE); to explore the coping strategies and impact of these disorders on quality of life and function. Methods This observational cross-sectional study recruited patients of the University of Campania ‘Luigi Vanvitelli’, from February 4th to April 4h, 2022, who were diagnosed with SLE according to the Systemic Lupus International Collaborating Clinics (SLICC) Criteria. Demographic and clinical data, disease activity (SLEDAI-2k), damage (SDI) and concomitant therapies were analyzed. The definitions for remission (DORIS) and ‘Lupus Low Disease Activity State’ (LLDAS) were applied. At enrollment, each patient underwent a psychiatric evaluation completing the following questionnaires: Hamilton Depression and Anxiety Rating Scales (HAM-D, HAM-A), Montreal Cognitive Assessment (MoCA) and Coping Orientation to Problems Experienced (COPE) Inventory. Health Assessment Questionnaire-Disability Index (HAQ-DI) was also performed. The Spearman test was used for linear correlation. Multivariate analysis was performed by multiple linear and logistic regression. Results 61 consecutive patients with SLE were enrolled, the majority female (88%) and Caucasian with a mean age of 46 years. 70% were in remission or in LDA. The prevalence of cognitive dysfunction was 65%, executive function and memory were the most affected domains. We found isolated anxiety in 3% and isolated depression in 50.7% of patients, even if mild. Regarding coping strategies, SLE patients reported higher scores on emotion-focused coping, with respect to the other two coping strategies (p< 0,001). Pearson’s correlation analysis highlighted a relationship between higher levels of cognitive impairment and worse quality of life (r = -0.38, p = 0.002) and between hypocomplementemia and depression (r=-0,25; P=0,04). AntidsDNA antibody positivity was slightly significant (r=0,22; P=0,08). Our analysis also highlighted a positive correlation between emotion-focused and avoidance-focused strategies (0.43;P=0.0005). In the multivariate analysis, higher scores on the HAM-D questionnaire (higher levels of depression) were found to be independently associated with higher HAQ score (OR:1.2; p=0.03). Moreover, patients with active disease tend to be more depressed compared to patients in LDA or in remission (p : 0.04). Fibromyalgia was independently associated with depression (OR: 3.85 p : 0.03). Depression was found to be significantly linked to patients’ worse quality of life, irrespective of disease activity (OR:1.19; p=0.01). Depression, anxiety and fibromyalgia were not associated with objective cognitive dysfunction. Conclusion Our study confirms the high prevalence of cognitive dysfunction and depressive symptoms in SLE patients and determine a strong negative impact on function and quality of life. In the context of a multidisciplinary management, collaboration with clinical psychologists should be considered, to improve both coping strategies, patients’ perception of health status and quality of life.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.024
Threshold uncertainty score0.081

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0240.003

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.018
GPT teacher head0.296
Teacher spread0.278 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2022
Admission routes1
Has abstractyes

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