Immune checkpoint inhibitor–associated nephritis—treatment standard
Bibliographic record
Abstract
Over the last 13 years, the use of immune checkpoint inhibitor (ICI) therapy has grown remarkably, owing to their unprecedented anti-tumor efficacy in certain tumor groups. With increased use of ICIs, we are seeing immune-related adverse events (irAEs) more frequently. Renal irAEs, such as ICI-associated acute kidney injury (ICI-AKI), are reported in 2%-5% of patients treated with ICIs, with acute tubulointerstitial nephritis (ATIN) as the most common histopathologic lesion, though various forms of glomerulonephritis have also been reported. Modifiable risk factors for ICI-AKI include concurrent use of ATIN-associated drugs, like proton pump inhibitors, non-steroidal anti-inflammatory drugs and antibiotics, and dual ICI therapy with both Cytotoxic T-lymphocyte Associated Protein 4 (CTLA-4) and Programmed Cell Death Protein 1 and its ligand (PD1/PDL-1) blockade. Kidney biopsies remain the diagnostic modality of choice, though several promising non-invasive biomarkers, which have not yet been broadly clinically validated have emerged. The treatment of ICI-AKI involves holding ICIs, discontinuation of ATIN-associated drugs and initiation of immunosuppression with corticosteroids as first-line therapy. With prompt treatment initiation, most patients achieve full or partial renal recovery, allowing for re-challenge with ICI. However, a subset of patients will require additional steroid-sparing therapies for corticosteroid-dependent or refractory ICI-AKI. Here we review developments in our understanding of the pathophysiology of ICI-AKI, the approach to diagnosis (with a focus on the emergence of novel diagnostic tools), prognostic factors and the current evidence for establishing treatment standards for ICI-AKI. As the evidence base remains largely retrospective, we identify questions that would benefit from future prospective studies in the diagnosis, management and prognostication of ICI-AKI.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".