Immune checkpoint inhibitor use in patients with end-stage kidney disease: an analysis of reported cases and literature review
Bibliographic record
Abstract
Immune checkpoint inhibitors (ICIs), immunomodulatory antibodies that are used to enhance the immune system, have substantially improved the prognosis of patients with advanced malignancy. As the use of ICI therapy becomes increasingly widespread across different types of cancer, their use in patients receiving dialysis is likely to increase. In this review we summarize the current literature on the use of ICIs in end-stage kidney disease (ESKD) patients and provide aggregate data from reported cases and series. Based on available pharmacological information, ICIs require no dosing adjustment in ESKD patients. Analysis of the reported cases in the literature demonstrates a similar incidence of immune-related adverse events in patients with ESKD receiving dialysis as compared with the general population (49%). Severe reactions graded as 3 and 4 have been seen in 15 patients (16%). As such, it is important that these patients are monitored very closely for immune-related adverse events; however, the risk of these adverse events should not preclude patients on dialysis from receiving these therapies. Cancer remission (complete and partial) was seen in close to 30% of patients, stable disease was seen in 28% and progression of disease in ∼36%. One-third of the patients died. Urothelial and renal cell cancer represented approximately half of all treated cancers and accounted for ∼50% of all deaths reported. Additional data in the dialysis population with the use of ICIs and involvement in prospective studies are needed to better assess outcomes, particularly within specific cancer types.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".