A Systematic Review of Immune Checkpoint Inhibitor–Associated Glomerular Disease
Bibliographic record
Abstract
INTRODUCTION: Immune checkpoint inhibitors (ICIs) are increasingly used to treat cancers. Kidney immune-related adverse events (IRAEs) are now well recognized, with the incidence of IRAEs ranging from 2% to 5%. Most of the initial data related to kidney IRAEs have focused on acute interstitial nephritis (AIN). There are minimal data on the types and relative frequencies of glomerular diseases associated with ICIs, their treatment, and outcomes. METHODS: We performed a systematic review and meta-analysis of all biopsy-proven published cases/series of glomerular pathology associated with ICIs. We searched the MEDLINE, EMBASE, and Cochrane databases from inception to February 2020. We abstracted patient-level data, including demographics, cancer and ICI therapy details, and characteristics of kidney injury. RESULTS: After screening, 27 articles with 45 cases of biopsy-confirmed ICI-associated glomerular disease were identified. Several lesion types were observed, with the most frequent being pauci-immune glomerulonephritis (GN) and renal vasculitis (27%), podocytopathies (24%), and complement 3 GN (C3GN; 11%). Concomitant AIN was reported in 41%. Most patients had ICIs discontinued (88%), and nearly all received corticosteroid treatment (98%). Renal replacement therapy (RRT) was required in 25%. Most patients had full (31%) or partial (42%) recovery from acute kidney injury (AKI), although 19% remained dialysis-dependent, and approximately one-third died. Complete or partial remission of proteinuria was achieved in 45% and 38%, respectively. CONCLUSION: Multiple forms of ICI-associated glomerular disease have been described. Pauci-immune GN, podocytopathies, and C3GN are the most frequently reported lesions. ICI-associated glomerular disease may be associated with poor kidney and mortality outcomes. Oncologists and nephrologists must be aware of glomerular pathologies associated with ICIs and consider obtaining a kidney biopsy specimen when features atypical for AIN are present.
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.008 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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".