Immune Checkpoint Inhibitor-Associated Glomerular Disease: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background: Immune checkpoint inhibitors (ICI) are increasingly used to treat several cancers. Kidney immune-related adverse events (IRAE) are now well-recognized, with purported incidence of 2-5%. The majority of initial data related to kidney IRAE has focused on acute interstitial nephritis (AIN). Recently various glomerular diseases have been reported; however, there is minimal data on the types and relative frequencies of glomerular diseases associated with ICI, 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 ICI therapy. We searched the MEDLINE, EMBASE and Cochrane Central databases from inception to February 2020. We abstracted patient-level data, including demographics, cancer and ICI therapy details, and characteristics of kidney injury. We performed exploratory univariate logistic regressions for predictors of end stage kidney disease (ESKD) or death. Results: After screening, 27 manuscripts with 45 cases of biopsy-confirmed ICIassociated glomerular disease were identified. Several types of lesions were observed, with the most frequent being pauci-immune glomerulonephritis and renal vasculitis (27%), minimal change disease (MCD) (20%), and C3 glomerulonephritis (11%). Concomitant AIN was reported among 41% of cases. The majority of patients had ICI discontinued (88%), and nearly all received corticosteroids (98%). Complete or partial remission of proteinuria was achieved in 45% and 38%, respectively. Most patients had full (31%) or partial (42%) recovery from AKI although 19% required dialysis and approximately one-third of patients died. In exploratory univariate logistic regression for predictors of endstage kidney disease (ESKD) or death, glomerular lesion, ICI class, peak creatinine and proteinuria were not significantly associated with this composite outcome. Conclusions: Glomerular diseases associated with ICI are not uncommon. Pauci-immune glomerulonephritis, MCD and C3GN are the most frequently reported lesions. ICI-associated glomerular disease may be associated with poor kidney and mortality outcomes. Oncologists and nephrologists need to be aware of glomerular pathologies associated with ICI treatment.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.026 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".