Comparative effectiveness of immunosuppressive drugs and corticosteroids for lupus nephritis: a systematic review and network meta-analysis
Bibliographic record
Abstract
BACKGROUND: There is a lack of high-quality meta-analyses and network meta-analyses of immunosuppressive drugs for lupus nephritis. Our objective was to assess the comparative benefits and harms of immunosuppressive drugs and corticosteroids in lupus nephritis. METHODS: We conducted a systematic review and network meta-analysis (NMA) of trials of immunosuppressive drugs and corticosteroids in patients with lupus nephritis. We calculated odds ratios (OR) and 95 % credible intervals (CrI). RESULTS: Sixty-five studies that met inclusion and exclusion criteria; data were analyzed for renal remission/response (37 trials; 2697 patients), renal relapse/flare (13 studies; 1108 patients), amenorrhea/ovarian failure (eight trials; 839 patients) and cytopenia (16 trials; 2257 patients). Cyclophosphamide [CYC] low dose (LD) and CYC high-dose (HD) were less likely than mycophenolate mofetil [MMF] and azathioprine [AZA], CYC LD, CYC HD and plasmapharesis less likely than cyclosporine [CSA] to achieve renal remission/response. Tacrolimus [TAC] was more likely than CYC LD to achieve renal remission/response. MMF and CYC were associated with a lower odds of renal relapse/flare compared to PRED and MMF was associated with a lower rate of renal relapse/flare than AZA. CYC was more likely than MMF and PRED to be associated with amenorrhea/ovarian failure. Compared to MMF, CYC, AZA, CYC LD, and CYC HD were associated with a higher risk of cytopenia. CONCLUSIONS: In this systematic review and NMA, we found important differences between immunosuppressives used for the treatment of lupus nephritis. Patients and physicians can use this information for detailed informed consent in a patient-centered approach. Study limitations of between-study clinical heterogeneity and small sample size with type II error must be considered when interpreting these findings. PROSPERO: CRD42016032965.
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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 | 0.027 | 0.061 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.042 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".