Disease Flare of Systemic Lupus Erythematosus in Patients With Endstage Renal Disease on Dialysis
Bibliographic record
Abstract
Objective Although systemic lupus erythematosus (SLE) disease activity diminishes after starting dialysis, flares have been documented during dialysis. Hence, we studied the various clinical and therapeutic variables of patients with SLE who had a disease flare while on dialysis. Methods The medical records of patients with SLE who received dialysis at 2 tertiary referral hospitals in South Korea were reviewed. The disease activity was analyzed in terms of the nonrenal SLE Disease Activity Index (SLEDAI), and the factors associated with SLE flares were evaluated. Results Of the total of 121 patients with SLE on dialysis, 96 (79.3%) were on hemodialysis (HD) and 25 (20.7%) were on peritoneal dialysis (PD). During a median follow-up of 45 months (IQR 23-120) after the initiation of dialysis, 32 (26.4%) patients experienced an SLE flare (HD, n = 25; PD, n = 7). The most common features of SLE flare were hematologic (40.6%; thrombocytopenia [31.2%] and leukopenia [21.8%]) and constitutional manifestations (40.6%). Fever was the most common (34.3%) feature among the constitutional symptoms. Treatments for disease flares were based on corticosteroids, and 11 (34.3%) patients required additional immunosuppressants, including cyclophosphamide and mycophenolate mofetil. Nonrenal SLEDAI score before dialysis initiation (HR 1.24, 95% CI 1.12-1.36; P = 0.001) was a significant risk factor for disease flare during dialysis. Conclusion More than a quarter of the patients with SLE experienced a disease flare during dialysis, which most commonly had hematologic manifestations, particularly thrombocytopenia. Continued follow-up and appropriate treatments, including immunosuppressants, should be considered for patients with SLE receiving dialysis.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".