Drug-induced liver injury secondary to anakinra in a patient with systemic juvenile idiopathic arthritis—Case report and literature overview
Bibliographic record
Abstract
Background: Anakinra, an interleukin-1 receptor antagonist, is widely used to manage systemic juvenile idiopathic arthritis (sJIA) and related inflammatory conditions. While generally well tolerated, rare cases of drug-induced liver injury (DILI) have been reported. Methods: In addition to a literature review, we present a pediatric case of severe DILI attributed to anakinra. The case is of a 14-year-old male with sJIA complicated by macrophage activation syndrome (MAS) who developed DILI following the initiation of anakrina. Results: Anakrina was started after initial corticosteroid treatment. Following dose escalation, he developed a marked elevation in liver enzymes and cholestasis. Extensive workup, including a liver biopsy, excluded other causes and supported a diagnosis of anakinra-induced DILI. Discontinuation of anakinra led to the gradual normalization of liver function. Subsequent treatment with cyclosporine and tocilizumab was well tolerated. Conclusions: Differentiating the liver enzyme elevation to MAS versus DILI is challenging but critical, as management strategies differ. Anakinra-induced liver injury appears to be an idiosyncratic, immune-mediated phenomenon that resolves on drug withdrawal. Our case highlights the importance of monitoring liver function during anakinra therapy and the role of liver biopsy in complex cases.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".