Assessing cumulative exposure to maintenance immunosuppressive drugs: Metrics, outcomes, and implications for transplant patients
Bibliographic record
Abstract
Immunosuppressive drugs are used in the management of transplant patients to prevent organ rejection. However, immunosuppression can be associated with adverse effects such as infections and cancers. This study aimed to characterize the measures of cumulative immunosuppressive drug exposure (CIDE) used in the literature and their associated outcomes in transplant patients. A literature search was conducted in Ovid MEDLINE, Ovid EMBASE, Cochrane CENTRAL, and Cochrane Database of Systematic Reviews using search terms related to maintenance immunosuppressants and CIDE. Study risk of bias was assessed using the Quality in Prognostic Studies tool. Thirty-one articles were included in this qualitative synthesis. Sixteen articles (52 %) calculated the total dose of immunosuppression over the treatment period, while eight (26 %) used area-under-the-curve of trough level concentrations to quantify CIDE. Five (16 %) articles investigated time-weighted metrics of calcineurin inhibitors and four (13 %) used other metrics that could not be categorized into the previous groups. Most studies investigated CIDE with calcineurin inhibitors and used additive dosing methods. This approach was also popular with corticosteroids and multi-drug exposures. The variety of metrics used in the literature reveals a lack of standardization in the evaluation of CIDE and long-term outcomes. Future studies should validate these metrics for clinical application, especially pertaining to infectious outcomes. • Immunosuppressive drugs are used in the management of transplant patients to prevent organ rejection so measures of cumulative exposure to these drugs may help guide management. • This systematic review showed widespread variability in the way cumulative immunosuppressive drug exposure (CIDE) is measured and assessed, from total dose of immunosuppression over the treatment period, to the area-under-the-curve of trough level concentrations, and time-weighted metrics of calcineurin inhibitors • CIDE metrics that adequately capture the net state of immunosuppression across multiple drugs were wanting. • Future studies should validate these metrics and/or develop new approaches that may be effectively translated to clinical care.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".