Immunosuppressive therapy withdrawal in lupus nephritis, is it possible?: A systematic review
Bibliographic record
Abstract
IntroductionMaintenance of immunosuppressants (IST) is critical for sustaining remission in lupus nephritis (LN) patients. However, long-term use is associated with an increased risk of side effects such as infection. Yet, early IST withdrawal also poses a high risk of flare. This study aims to provide an overview of the impact of optimal IST withdrawal in patients with LN who have achieved remission.MethodWe conducted a systematic review of randomized controlled trials (RCTs) and observational studies regarding the discontinuation of IST in patients with proliferative LN who had been in remission for at least 1 year. Data from PubMed, ProQuest, and Web of Science were extracted on patient demographics, baseline characteristics, treatment regimens, and outcomes, including flare rates, renal function, and biopsy findings. The risk of bias was assessed using the Newcastle-Ottawa Scale and the JADAD Score.ResultsFive studies with 310 patients were included. The mean age of participants ranged from 26 to 38 years. Overall, flares following IST withdrawal occurred in an average of 28.7% of patients. Between the two groups (flare and no-flare), baseline serum creatinine was comparable, but baseline proteinuria and C3 & C4 levels were varied across studies. The duration of lupus before study entry was approximately 4-10 years, with a duration of complete remission of 12-59.5 months before IST withdrawal. Follow-up periods ranged from 24 to 215 months. The Biopsy Activity Index and Chronicity Index at baseline also showed variation but generally indicated a higher level of chronic damage in the flare group.DiscussionDiscontinuation of IST is feasible but may be associated with an increased risk of severe flares, often requiring reintroduction of induction therapy. Careful assessment and monitoring of both histologic and clinical activity are essential when evaluating remission and considering IST withdrawal, as a low activity index may guide safer withdrawal strategies.ConclusionIST withdrawal is feasible in patients with LN who have achieved remission, but careful monitoring is required due to the risk of relapse and potential progression of chronic kidney damage. Histological confirmation and predictive tools may support safer withdrawal decisions.
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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 | 0.012 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.008 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".