Approaching the Therapeutic Window for Cyclosporine in Kidney Transplantation
Bibliographic record
Abstract
Neoral dosing is traditionally based on cyclosporine (CyA) trough levels (C(0)). Four-h area under the curve (AUC(0-4)) for Neoral in the early posttransplantation period was shown previously to have a better correlation to acute rejection (AR) and CyA nephrotoxicity (CyANT), compared with C(0). An AUC(0-4) range of 4400 to 5500 microg/h per L during the first week was associated with the lowest AR and CyANT. This article describes a prospective study to assess the feasibility, safety, and efficacy of dosing Neoral solely by AUC(0-4) monitoring, regardless of C(0), in the first 3 mo after kidney transplantation. Fifty-nine kidney transplant recipients received Neoral-based triple immunosuppression. AUC(0-4) was measured on days 3, 5, 7, 10, and 14 and weeks 3, 4, 6, and 8, then monthly. Target AUC(0-4) was 4400 to 5500 microg/h per L. Dose was adjusted by percentage difference from target AUC(0-4). Ninety-four percent of AUC were performed on the scheduled day or close to it. No patients had CyANT while AUC(0-4) was in target range. Four patients had reversible CyANT with AUC(0-4) > 5500. Only 1 of 33 patients (3%) who achieved and maintained AUC(0-4) > 4400 by day 3 posttransplantation had AR, whereas 10 of 22 (45%) of those with day 3 to 5 AUC(0-4) < 4400 had AR (P: = 0.0002). In logistic regression analysis, higher early AUC(0-4) was the only significant variable associated with lower serum creatinine at 3 mo. Neoral dose monitoring by AUC(0-4) is a potentially valuable tool for optimizing Neoral immunosuppression. Attainment of a target range of 4400 to 5500 microg/h per L for AUC(0-4) early after transplantation has been demonstrated to reduce significantly the risk of AR and CyANT.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".