BRIDGING THE GAP BETWEEN PATIENT’S PERCEPTION ON QUALITY OF LIFE AND DISEASE ACTIVITY AND DAMAGE IN SYSTEMIC LUPUS ERYTHEMATOUS PATIENT.
Notice bibliographique
Résumé
PT013 / #74 Topic: AS23 - SLE-Diagnosis, Manifestations, & Outcomes POSTER TOUR 03: RECENT ADVANCEMENTS IN SLE CLINICAL OUTCOMES AND THERAPY 23-05-2025 10:00 AM - 10:40 AM Background/Purpose Systemic Erythematosus Lupus (SLE) is a chronic autoimmune disease affecting multiple organs and systems. It often begins at a young age and can lead to severe complications, prolonged treatments, and emotional challenges, affecting patients’ self-perception and quality of life (QoL). Healthcare professionals are increasingly concerned about the impact of SLE on patients’ mental and emotional well-being. Tools like the Lupus Impact Tracker (LIT), which consists of 10 questions, assess how patients manage the disease, their self-esteem, psychological status, and family responsibilities. LIT is designed to measure the impact of lupus on QoL (1) and has been linked to disease activity (2). Objectives: To analyze the correlation between SLE activity, accumulated organ damage, and patients’ self-perception of QoL, focusing on pain, fatigue, and mental health. To explore the influence of additional factors like comorbidities, socioeconomic status, and chronic treatments on QoL in SLE patients. Methods The study analyzed data from the RELESSER-PROS cohort at the first annual visit (V1). LIT scores were divided into quartiles, and variables in each group were examined. Chi-square/Fisher tests were used for categorical data, and ANOVA/Kruskal-Wallis for continuous variables. Logistic regression identified factors influencing LIT scores above 50, with a 5% significance level using R software. Results A total of 1,417 SLE patients were included in the study, with 90% female and 94.2% Caucasian. The average age at diagnosis was 34.7 years, and the median Lupus Impact Tracker (LIT) score at the first visit (V1) was 25. The highest scoring domains were “pain/fatigue” (mean score 1.52 per question) and “emotional health” (1.29), while the lowest were “body image dissatisfaction” (0.87) and “lupus medication side effects” (0.69). At V1, the mean clinical SLEDAI score (disease activity) was 1.92, and the mean SLE Damage Index (SDI) score was 1.42. Patients with higher LIT scores (50-100) had significantly higher SLEDAI and SDI scores (Table 1), indicating more severe disease and damage. These patients were also less likely to be in low disease activity (LLDAS) or 2021 DORIS remission (p=0.04). The study also examined additional factors influencing quality of life (QoL), including educational and laboral status, comorbidities (eg, pulmonary disease, depression, cardiovascular disease), and therapies (eg, glucocorticoids, immunosuppressants). A multivariate analysis identified variables significantly associated with higher LIT scores (Table 2), showing that these factors contribute to a greater impact of SLE on patients’ QoL. Table 1. Disease activity and damage accrual by subgroups according to the LIT (quartile) values Table 2. Factors associated with a higher impact of SLE on QoL (dependent variable: LIT score >50) in the multivariate analysis. Conclusions We observed a positive correlation between LIT values and the activity of SLE (measured by cSLEDAI) and accumulated damage (measured by SDI) in our cohort. We found a correlation between hydroxychloroquine treatment, male sex and higher studies and better outcomes in QoL of SLE patients. The presence of comorbidities such fibromyalgia, depression or thyroid disease was related to a higher negative impact in QoL. High doses of Glucocorticoids are also related to a poor outcome in LIT values. Beyond the activity and damage of the disease, there are other variables that significantly influence patients with SLE and have an impact on their quality of life. These results highlight the relevance of considering these factors when making clinical decisions, with the purpose of optimizing medical care.
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 | 0,002 | 0,006 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| 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 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 ».