Acetaminophen-Induced Acute Liver Failure Treated with Single-Pass Albumin Dialysis: Report of a Case
Bibliographic record
Abstract
BACKGROUND: Acetaminophen (paracetamol) overdose is a leading cause of acute liver failure (ALF). When patients fulfill the King's College criteria for acetaminophen-induced ALF (AALF), they have a poor prognosis for survival without liver transplantation. Recent advances in artificial liver support have used albumin as a binding and scavenging molecule in ALF. One method, single-pass albumin dialysis (SPAD), involves dialyzing blood against an albumin-containing solution across a high-flux membrane to remove albumin-bound toxins. Herein, we describe our protocol for SPAD and report its use in a case of AALF as a bridge to native liver recovery. CASE: A 41-year-old female with no documented history of liver disease presented with acute acetaminophen toxicity and developed hepatic encephalopathy, coagulopathy and lactic acidosis. The patient met King's College criteria for liver transplantation, based on pH and INR, but was deemed not suitable as a candidate due to psychosocial comorbidities. On day 3 of her ICU admission, she received the first of five consecutive daily runs (total ~77 hours) of SPAD. The patient's course was complicated by cerebral edema requiring mannitol. She was extubated on day 11 and transferred to the ward by day 13. At ICU discharge, her liver function (INR 1.9, bilirubin 435 mmol/L) and kidney function were recovering. She did not have any long-term neurological sequelae. By hospital discharge (day 46) her native liver function had recovered with a bilirubin <100mmol/L. CONCLUSION: We describe a case of a patient with acetaminophen-induced acute liver failure who was successfully bridged to spontaneous native liver recovery as a result of SPAD treatment. In patients with ALF, SPAD may be an additional intervention for temporary extracorporeal support. Further investigation in larger prospective studies is warranted.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.009 | 0.006 |
| 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".