Organ Damage and Its Associated Factors in Systemic Lupus Erythematosus Patients: A Retrospective Cohort Study
Bibliographic record
Abstract
Background: Systemic lupus erythematosus (SLE) can affect a plethora of organ systems and cause organ damage due to the disease process and medication toxicity, notably corticosteroids. Patients with SLE often suffer irreversible organ damage. Older age, glucocorticoid use, longer disease duration, and disease activity all represent risk factors for organ damage. This study aims to assess the incidence and predictors of organ damage among Saudi Arabian SLE patients. Methods: This study is a single-center, retrospective cohort observational study conducted at the adult Rheumatology Outpatient Clinic in King Fahad Medical City, Riyadh, Saudi Arabia. It included all patients aged 16 years and older who met at least four of the American College of Rheumatology Classification criteria for SLE or had a renal biopsy consistent with lupus nephritis and had regular follow-ups at our hospital, with the last visit occurring within 2 years. Results: The study included 196 patients with SLE, predominantly female (92.9%) with a mean age of 36.2 years and an average disease duration of 8.88 years. Among the patients, 38.8% had a positive Systemic Damage Index (SDI) score. Hydroxychloroquine was used by 93.4% of the patients, and 46.9% had a Systemic Lupus Erythematosus Disease Activity Index 2000 (SLEDAI-2K) score of 3 or higher. The neuropsychiatric system was most affected, with 16.8% of patients having positive SDI scores in this domain, followed by the renal system at 9.2%. Patients with positive SDI scores were significantly older, had longer disease duration, and had higher prevalence of diabetes mellitus and hypertension. Conclusion: To address organ damage in SLE patients, integrating adjunctive therapies like antihypertensives and antidiabetic agents into management plans is essential. Future research should adopt prospective cohort designs to evaluate the dynamic interactions between comorbidities and organ damage over time. Additionally, studies should assess the effectiveness of combined treatment strategies and develop targeted approaches for high-risk groups to enhance outcomes 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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".