The role of fomepizole in acetaminophen-related poisoning: a narrative review
Bibliographic record
Abstract
N-acetylcysteine (NAC) as the gold standard antidote in cases of acetaminophen (APAP)-related poisoning might not be sufficient with delayed presentation or massive ingestion. Human reports published up to July 2021 suggest that fomepizole could play a role in APAP overdoses by inhibiting cytochrome P450 2E1 (CYP2E1)-mediated N-acetyl-p-benzoquinone imine (NAPQI) production and JNK-mediated oxidative damage. This narrative review builds on previous systematic and scoping reviews by adding the latest evidence about the use of fomepizole in APAP poisoning to better understand the hepatoprotective role and safety profile of this medication, as well as its practical place in therapy. A systematic search of MEDLINE and Embase was completed through November 2024. Studies involving human patients with APAP toxicity who received fomepizole treatment were included. Each case was summarized in tables to identify clinical trends, particularly the risk of hepatotoxicity, quantity of ingestion, time of presentation since ingestion, therapeutic and dosing regimens, and clinical outcomes. This review covers 28 studies and 45 patients across 18 case reports and 6 case series. When used with NAC, fomepizole seemed to result in favorable laboratory and clinical outcomes in most patients at high risk of hepatotoxicity because of late presentation or massive APAP ingestion. The available data suggest that fomepizole might complement NAC in treating severe APAP toxicity. Though they lack detailed clinical outcome analyses, case studies suggest that fomepizole could improve hepatotoxicity, survival, and transplant-free days.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".