Real-World Study of Once-Daily, Extended-Release Tacrolimus Versus Twice-Daily, Immediate-Release Tacrolimus in Kidney Transplantation: Clinical Outcomes and Healthcare Resource Utilization
Bibliographic record
Abstract
INTRODUCTION: Real-world data with extended-release tacrolimus (ER-T) are lacking in the USA. This study examined clinical outcomes and healthcare resource utilization in kidney transplant patients receiving ER-T in clinical practice. METHODS: This was a retrospective, single-center analysis (February-June 2016) using data from Northwestern University's Enterprise Data Warehouse. Adult patients receiving a kidney transplant in the preceding 4 years, treated de novo or converted to ER-T from immediate-release tacrolimus (IR-T) within 10 days post-transplantation, and maintained on ER-T (at least 3 months) were included. Patients were matched for demographic and clinical characteristics with IR-T-treated control patients. Endpoints included clinical outcomes and healthcare resource utilization up to 1 year post-transplantation. RESULTS: A total of 19 ER-T-treated patients were matched with 55 IR-T-treated patients. No ER-T-treated patients experienced biopsy-confirmed acute rejection (BCAR) or graft failure versus 3 (5.5%) and 3 (5.5%) IR-T-treated patients, respectively. Mean estimated glomerular filtration rate (eGFR), the number of all-cause outpatient visits, readmissions, and all-cause hospitalization days were comparable between groups. Tacrolimus trough levels, days to target level (6-10 ng/mL), and number of required dose adjustments were also similar. CONCLUSION: Real-world clinical outcomes and healthcare resource utilization were similar with ER-T and IR-T. Larger studies will need to investigate the trend toward fewer BCAR events, and increased graft survival with ER-T. FUNDING: Astellas Pharma Global Development, Inc. Plain language summary available for this article.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".