Brentuximab-Induced Acute Interstitial Nephritis: A Case Report
Bibliographic record
Abstract
Brentuximab vedotin is a combination monoclonal antibody to anti-CD30 conjugated to the anti-tubulin agent monomethyl auristatin E. It is approved for the treatment of mycosis fungoides, Hodgkin’s lymphoma, and systemic anaplastic large cell lymphoma. Brentuximab has been associated with a number of potential adverse reactions; however, reports of renal complications are rare. A 73-year-old male with mycosis fungoides was admitted to hospital with acute kidney injury following his third cycle of brentuximab. The patient’s serum creatinine (SCr) was 122 µmol/L with an estimated glomerular filtration rate (eGFR) of 58 mL/min/1.73 m 2 at baseline. Following brentuximab, his SCr peaked at 1073 µmol/L over a 4-week period. Acute interstitial nephritis (AIN) was diagnosed after other causes of acute kidney injury were ruled out and subsequently confirmed on kidney biopsy. The patient was started on prednisone 50 mg daily. This was continued for 3 weeks, followed by a 5-week taper. The patient’s SCr decreased to 156 µmol/L by completion of the prednisone taper. He was not rechallenged with brentuximab. A kidney biopsy confirmed AIN in keeping with injury from an immune checkpoint inhibitor (ICI). However, brentuximab is not an ICI. The AIN from ICIs typically has tubulointerstitial inflammatory infiltrate comprised of T lymphocytes such as the case presented here. Therefore, this represents both a novel histopathologic finding in AIN from a non-ICI medication and a rare complication of brentuximab, previously only presented in abstract form.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.009 | 0.006 |
| 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".