Acute Allograft Rejection in Kidney Transplant Recipients Treated With Immune Checkpoint Inhibitors: An Educational Case Report
Bibliographic record
Abstract
Rationale: Kidney transplant (KT) recipients have an increased risk of malignancy due to chronic immunosuppression. The emerging use of immune checkpoint inhibitors (ICIs) has been a promising development for the treatment of malignancy, but their use adds to the complexity of immunosuppression management for KT recipients. This case report describes 2 cases of acute rejection in KT recipients following ICI initiation and discusses the balance of malignancy treatment with adequate immunosuppression. Presenting Concerns of Patients: The first patient is a 44-year-old male KT recipient with a diagnosis of metastatic renal cell carcinoma presenting with acute kidney injury 6 days following initiation of an ICI. The second patient is a 73-year-old male KT recipient with a diagnosis of squamous cell carcinoma presenting with acute kidney injury 2 weeks following initiation of an ICI. Diagnoses: Both patients were diagnosed with acute rejection in the setting of reduced immunosuppression and initiation of an ICI. Interventions: Both cases received an increased dose of steroid without improvement of graft function. The first patient subsequently underwent a delayed graft nephrectomy due to complications of acute rejection, whereas the second patient did not undergo nephrectomy. Outcomes: The first patient experienced complications including perioperative bleeding requiring multiple operations, but ultimately stabilized on hemodialysis and showed a durable response to ICI. The second patient remained dialysis-dependent post-ICI treatment and was readmitted with allograft complications leading to his eventual death. Teaching Points: This study underscores the complexity of managing KT recipients diagnosed with malignancy and receiving ICIs. The balance between immunosuppression reduction to treat malignancy and preventing allograft rejection presents a significant challenge. Key considerations include the risk of acute allograft rejection and patient-centered decision-making. These cases highlight the need for further research to develop evidence-based guidelines for managing this patient population. In addition, the patient perspective in this study highlights the importance of careful risk-benefit analysis and the impact of treatment decisions on patient-focused outcomes.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.004 |
| 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".