BRIDGING THE GAP BETWEEN PATIENT’S PERCEPTION ON QUALITY OF LIFE AND DISEASE ACTIVITY AND DAMAGE IN SYSTEMIC LUPUS ERYTHEMATOUS PATIENT.
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".