Argatroban Dosing in Intensive Care Patients with Acute Renal Failure and Liver Dysfunction
Bibliographic record
Abstract
STUDY OBJECTIVE: To demonstrate dosing adjustment difficulties of argatroban encountered in critically ill patients with acute liver dysfunction who are receiving continuous renal replacement therapy. DESIGN: Case description. SETTING: Medical and surgical intensive care unit in a tertiary care, university-affiliated hospital. PATIENTS: Four consecutive patients with proven heparin-induced thrombocytopenia (HIT), acute renal failure requiring continuous renal replacement therapy, and various levels of transient hepatic impairment. INTERVENTION: Argatroban, a direct synthetic thrombin inhibitor, was given continuously and stabilized at 0.125-0.85 microg/kg/minute to attain an activated partial thromboplastin time (aPTT) 1.5-2.5 times the normal value for periods of 6-36 days. MEASUREMENTS AND RESULTS: Argatroban was started at the usual dosage of 2 microg/kg/minute, which resulted in significant overshooting of the aPTT and international normalized ratio (INR). No patient experienced bleeding or thrombotic complications. All patients were stabilized with reduced dosages such as those recommended for patients with chronic hepatic impairment. CONCLUSION: We recommend argatroban therapy for intensive care patients with HIT, especially those with renal failure. However, in all patients with suspected liver dysfunction due to recent elevation of liver transaminase levels and combined renal failure, a decrease in the initial dosage and careful titration of the infusion are mandatory. Further studies are needed to fully elucidate argatroban elimination and dosage adjustments for intensive care patients.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".