Use of tacrolimus in the treatment of autoimmune hepatitis: a single centre experience
Bibliographic record
Abstract
Sirs, We read with interest the paper by Yeoman et al. on the modern management of autoimmune hepatitis (AIH).1 Current standard treatment for AIH includes prednisone alone or in combination with azathioprine with 80% remission rate.2, 3 There is no standard treatment for steroid refractory AIH. Tacrolimus is a macrolide antibiotic, which exerts potent immunosuppressive effects on CD4+ T-helper cells. In their review, they highlight the few reports on tacrolimus as a second line agent in the treatment of AIH.4-8 To expand the literature, we present our experience in using tacrolimus in treating patients with AIH. We analysed retrospectively all AIH patients followed at Virginia Commonwealth University Health System between 1995 and 2009. Of 222 patients with AIH meeting the AASLD guidelines,2 13 received tacrolimus at the discretion of their treating hepatologist, mostly due to failure to normalise liver function enzymes with prednisone and/or intolerance to other immunosuppressive agents. Their characteristics are shown in Table 1. The dose range was 2–6 mg/day; mean trough serum concentration of 6.0 ng/mL, and duration of treatment was from 1 to 65 months. Remission, defined as normalisation of liver enzymes, was achieved in 12 of 13 patients (92%) who tolerated it. One patient developed nausea/vomiting, and one complained of hair loss, but both were able to continue. Tacrolimus was discontinued in one patient due to hemolytic uraemic syndrome after 4 weeks. She was subsequently converted to steroids, and was able to achieve remission and recovered normal renal function. One patient discontinued tacrolimus after 12 months therapy secondary to squamous cell carcinoma. In conclusion, our experience supports a role for tacrolimus in the treatment of AIH refractory to standard treatment. However, its use is not without risks, and randomised clinical trials of its application are needed before it can be considered as second line therapy. Declaration of personal and funding interests: None.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".