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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".