Practice patterns and outcomes in cancer patients developing immune checkpoint inhibitors–related AKI
Bibliographic record
Abstract
BACKGROUND: Acute kidney injury (AKI) is a known immune-related adverse event of cancer immune checkpoint inhibitor (ICI) therapy. Further population-based data on AKI incidence, risk factors and practice patterns post-ICI therapy are needed. METHODS: We measured the cumulative incidence of AKI among advanced cancer patients while receiving ICI therapy and non-ICI systemic therapy in Ontario, Canada (2012-18). An increase in serum creatinine was used to define AKI and graded according to event severity. Time to event modeling was used to compare the risk of developing AKI, pre-disposing factors and survival outcomes. RESULTS: We studied 16 425 patients with advanced cancer receiving either ICI or non-ICI systemic therapy. Among 4380 patients receiving ICI therapy, the overall crude 4-year incidence of AKI (any stage) was 29% and severe AKI (stage ≥2) was 7%. Characteristics associated with a higher risk of AKI included male sex, genitourinary (versus other) malignancy, the presence of hypertension, diabetes or chronic kidney disease, and prescription of a non-steroidal anti-inflammatory drug. The risk of experiencing AKI was significantly lower among patients treated with ICI versus non-ICI systemic therapy [adjusted hazards ratio (aHR) 0.80, 95% confidence interval (CI) 0.74-0.86, P-value <.0001]. Among the 587 patients who experienced an AKI and were both alive and discontinued ICI therapy within 30 days, 54 (9%) were re-challenged with ICI in the following 6 months and 24 (44%) had a recurrent AKI event. Patients who were re-challenged with ICI therapy had improved overall survival as compared with patients that received other non-ICI systemic therapy (aHR 0.38, 95% CI 0.22-0.67, P-value <.001). CONCLUSION: Our real-world study demonstrates a modest risk for severe AKI among cancer patients receiving ICI therapy, lower than with exposure to other systemic cancer therapies. Among patients who developed AKI and stopped ICI therapy, re-challenge was uncommon but may warrant consideration for select patients.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".