Use of population modeling to define rational monitoring of amiodarone hepatic effects*1
Bibliographic record
Abstract
BACKGROUND: Amiodarone causes hepatotoxicity in experimental models, but in humans, the relationships between drug administration, serum concentrations, markers of liver function, and how to monitor for hepatotoxicity have not been well characterized. METHODS: An open-dose, prospective study collected serum amiodarone, desethylamiodarone, ALT, AST, lactate dehydrogenase (LDH), alkaline phosphatase, total bilirubin, and albumin concentrations over a 5-year period from 125 patients. Nonlinear mixed-effects modeling (NONMEM) was used to explore the relationship between markers of hepatotoxicity and concentrations of amiodarone and desethylamiodarone. RESULTS: No patients had clinical symptoms of hepatotoxicity during follow-up. The natural history of changes in hepatic makers showed ALT to have the strongest independent relationship to changes in serum amiodarone (r = 0.32, P <.001). An ALT greater than 3 times the upper limit of normal developed in only 8 patients (7%), with the earliest occurrence at 55 days of therapy. A mixed-effects model relating ALT elevation to serum amiodarone was improved by the addition of an effect compartment having an equilibration half-time of 87 days (r = 0.81, P <.001). The model predicts that 6% of patients will have an ALT greater than 3 times the upper limit of normal if amiodarone concentrations are maintained at less than 2.5 mg/L, and virtually no patients will have such ALT elevations if amiodarone concentrations are maintained at less than 1.5 mg/L. CONCLUSIONS: Concentrations of amiodarone below a threshold of 1.5 mg/L are associated with a minimal risk of hepatotoxicity, whereas concentrations greater than 2.5 mg/L are associated with a greater than 6% risk of hepatotoxicity. There is significant hysteresis between changes in amiodarone concentration and the resulting change in ALT. The model suggests that monitoring ALT at baseline, 1, 3, and 6 months, and then semiannually would be an efficient strategy to detect amiodarone-induced hepatotoxicity.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".