Assessing Cumulative Immunosuppressive Drug Exposure: Metrics, Outcomes, and Implications for Kidney and Non-Kidney Transplant Patients
Bibliographic record
Abstract
Background: Immunosuppressive drugs are used in the long-term management of post-transplant patients to prevent rejection of transplanted organs. Lacking a prior qualitative systematic review on this topic, we aimed to characterize the metrics used to measure cumulative immunosuppressant exposure and their associated outcomes in kidney and non-kidney transplant patients. Methods: We conducted a literature search using search terms related to immunosuppressants and cumulative exposure in Ovid MEDLINE, Ovid EMBASE, Cochrane CENTRAL, and Cochrane Database of Systematic Reviews. No date restrictions were applied. An additional search was performed on Google Scholar and references of studies included in the primary search were screened. Studies were limited to the English language with adult human transplant patient populations. Study risk of bias was assessed using the Quality in Prognostic Studies Tool where each domain was rated as low, medium, or high risk of bias. Results: A total of 29 articles were included in our qualitative synthesis. Kidney transplant populations account for 12 (41%) of the studies in our analyses. Fifteen of the articles (51%) calculated the total dose of immunosuppression over the treatment period while 9 (31%) used long term area-under-the-curve (LT-AUC) of trough level concentrations to quantify cumulative immunosuppression exposure. Nine articles found certain cumulative exposure metrics to be predictive of adverse outcomes such as decreased kidney function, cancer recurrence, and bone fractures. Furthermore, an adequate mycophenolic acid LT-AUC was associated with a decreased risk of allograft rejection, while cumulative corticosteroid exposure was not associated with allograft rejection. Conclusions: This review analyzed a comprehensive set of articles and metrics that predict long-term outcomes of immunosuppressants in transplant patients. The wide variety of metrics studied highlight the lack of agreement on the best measures of drug exposure in transplant patients. Although certain metrics may demonstrate an association with outcomes, future studies should investigate the predictive power and validation of these metrics.
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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 | 0.061 | 0.238 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.008 |
| Bibliometrics | 0.019 | 0.017 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.003 |
| 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".