Long-Term Safety and Effectiveness of Tacrolimus in Patients With Lupus Nephritis in Japan: 10-Year Analysis of the Real-World TRUST Study
Bibliographic record
Abstract
Objective To assess the long-term safety and effectiveness of tacrolimus as maintenance therapy in patients with lupus nephritis (LN) receiving treatment in real-world clinical settings in Japan. Methods An open-label, noncomparative, observational, prospective postmarketing surveillance study was conducted in 1395 patients with LN receiving maintenance treatment with tacrolimus at 278 medical institutions across Japan over a period of 10 years. Tacrolimus continuation rate and cumulative incidence of adverse drug reactions (ADRs), relapse, progression to renal failure, and progression to dialysis were calculated using Kaplan-Meier analysis. Results Safety data were available for 1355 patients, almost half (49.3%) of whom remained on tacrolimus for the full 10 years of follow-up. A significant reduction in mean (SD) daily oral corticosteroid dose was observed from 16.0 (9.7) mg/day at 4 weeks after initiation of tacrolimus treatment to 7.2 (4.4) mg/day at year 10 (P< 0.001). The most frequently reported serious ADRs were infections (reported for 131 [9.7%] patients). Except for infections, no marked increase in the incidence of any other ADRs was seen over time, including renal impairment, malignant tumors, and cardiac dysfunction. Renal function was generally well maintained over the 10 years of follow-up. At year 10, cumulative rates of relapse, renal failure, and dialysis were 44.5%, 12.2%, and 4.5%, respectively. Conclusion Tacrolimus was effective and generally well tolerated as maintenance therapy for LN in a large cohort of patients in Japan followed for 10 years, almost half of whom remained on therapy for the entire duration of follow-up. (ClinicalTrials.gov: NCT01410747 )
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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; 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".