Renal Replacement Therapy in Lupus Nephritis–Related End‐Stage Kidney Disease: A Systematic Review and Meta‐Analysis
Bibliographic record
Abstract
OBJECTIVE: To assess the impact of renal replacement therapy in people with lupus nephritis (LN)-associated end-stage kidney disease (ESKD) and support the development of the 2024 American College of Rheumatology LN treatment guidelines. METHODS: We conducted a systematic literature review and meta-analysis to address three Population, Intervention, Comparison, and Outcome (PICO) questions related to renal replacement therapy for ESKD due to LN, including comparisons of kidney transplant versus dialysis, hemodialysis versus peritoneal dialysis, and preemptive kidney transplant versus no preemptive kidney transplant. Outcomes of interest included mortality, cardiovascular (CV) events, infections, lupus flares, disease-related damage, graft failure, and quality of life. We conducted a meta-analysis and analyzed hazard ratios for time-to-event analyses and risk ratios for dichotomous outcomes, as well as absolute risk estimates. RESULTS: Sixteen comparative observational studies addressed at least one of the three PICO questions. Kidney transplant was found to reduce the risks of all-cause mortality, CV mortality, infection-related mortality, and CV events compared with dialysis (high certainty). Dialysis modality (peritoneal vs hemodialysis) was not associated with mortality (high certainty) or with other outcomes of infection, CV complications, and systemic lupus erythematosus flares (low certainty). Preemptive kidney transplant was associated with lower risks of graft failure and mortality (low certainty). CONCLUSION: This systematic review identified improved outcomes with kidney transplant versus dialysis for people with LN-associated ESKD and potential benefits of preemptive kidney transplant. This evidence supports the use of kidney transplant as a preferred renal replacement therapy for people with LN-ESKD.
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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 | 0.009 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.027 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".