Use of argatroban for hemodialysis and continuous veno‐veno hemodialysis in a patient with heparin‐induced thrombocytopenia
Bibliographic record
Abstract
Objective: To describe the use of argatroban in a post‐cardiac operation patient with heparin‐induced thrombocytopenia requiring hemodialysis and continuous veno‐veno hemodialysis (CVVH). Case Summary:A 23‐year‐old Caucasian female with heparin‐induced thrombocytopenia developed acute renal failure after cardiovascular surgery. Argatroban was used as a substitute for heparin during hemodialysis and CVVH. Both activated partial thromboplastin time (aPTT) and activated clotting time (ACT) were used to guide the dosage of argatroban. The patient was successfully dialyzed without clotting of the circuit. The dosage required in our patient was much lower than the manufacturer's recommendation. Discussion:Argatroban is a thrombin inhibitor that does not cross react with heparin. It is metabolized by the liver, and dosage adjustment is recommended in patients with severe hepatic impairment. The correct dosage for patient with unstable hemodynamics is not known. Our patient had apparently normal hepatic function at the initiation of dialysis, but the dosage of argatroban recommended by the manufacturer resulted in prolonged elevation of the aPTT and ACT with associated gastrointestinal bleeding. This may be related to hepatic congestion secondary to poor cardiac function and/or severe anasarca. And the dosage of argatroban required during dialysis was much lower than the recommendation. Conclusions:Argatroban is an effective alternative of heparin for CVVH. The correct initial dosage in patients with mild hepatic impairment and unstable hemodynamics is still unclear.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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".