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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.021 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.085 | 0.008 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".