Immunosuppression Considerations for Older Kidney Transplant Recipients
Bibliographic record
Abstract
PURPOSE OF REVIEW: While kidney transplantation improves the long-term survival of the majority of patients with end-stage kidney disease (ESKD), age-related immune dysfunction and associated comorbidities make older transplant recipients more susceptible to complications related to immunosuppression. In this review, we discuss appropriate management of immunosuppressive agents in older adults to minimize adverse events, avoid acute rejection, and maximize patient and graft survival. RECENT FINDINGS: Physiological changes associated with senescence can impact drug metabolism and increase the risk of posttransplant infection and malignancy. Clinical trials assessing the safety and efficacy of immunosuppressive agents in older adults are lacking. Recent findings from U.S. transplant registry-based studies suggest that risk-adjusted death-censored graft failure is higher among older patients who received antimetabolite avoidance, mammalian target of rapamycin inhibitor (mTORi)-based, and cyclosporine-based regimens. Observational data suggest that risk-adjusted mortality may be increased in older patients who receive mTORi-based and cyclosporine-based regimens but lower in those managed with T-cell induction and maintenance steroid avoidance/withdrawal. SUMMARY: Tailored immunosuppression management to improve patient and graft survival in older transplant recipients is an important goal of personalized medicine. Lower intensity immunosuppression, such as steroid-sparing regimens, appear beneficial whereas mTORi- and cyclosporine-based maintenance are associated with greater potential for adverse effects. Prospective clinical trials to assess the safety and efficacy of immunosuppression agents in older recipients are urgently needed.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".