Prevalence, Risk Factors, and Outcomes of Chronic Kidney Disease in Patients With Systemic Lupus Erythematosus With and Without Lupus Nephritis
Bibliographic record
Abstract
Objective Chronic kidney disease (CKD) has significant clinical and therapeutic implications. This study assessed CKD prevalence, risk factors, and long-term outcomes in patients with systemic lupus erythematosus (SLE), both with and without lupus nephritis (LN). Methods This single-center, retrospective, medical records review study, conducted between 2014 and 2023, included adult patients with SLE. CKD was defined as estimated glomerular filtration rate (eGFR) < 60 mL/min/1.73 m2 or albuminuria ≥ 30 mg/24 h in ≥ 2 consecutive tests, spaced ≥ 3 months apart. Statistical analyses included chi-square tests, t tests, multivariable regression, and Cox proportional hazards models. Results A total of 175 patients with SLE were included, with a mean follow-up of 18.3 (SD 14.7) years. Of these patients, 12 required kidney replacement therapy. CKD was diagnosed in 54.6% (89/163) of patients, including 15.7% with reduced eGFR only, 52.8% with albuminuria only, and 31.5% with both. LN was associated with a higher hazard ratio (HR) of 5.4 for CKD, and 46.1% of patients with CKD had no history of LN. CKD was associated with increased cardiovascular morbidity and hospitalization rates for SLE exacerbations and infections. Cox analyses identified LN as the strongest predictor of CKD, with age and lower eGFR at diagnosis identified as other predictors. CKD was an important predictor of mortality among patients with SLE, in both univariate and multivariable analyses (19.1% vs 1.4%, P < 0.001). Conclusion CKD is highly prevalent in SLE, including in patients without prior LN. CKD is associated with increased morbidity and mortality. This study emphasizes the clinical relevance of CKD diagnosis and management in patients with SLE.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".