Successful use of a bivalirudin treatment protocol to prevent extracorporeal thrombosis in ambulatory hemodialysis patients with heparin‐induced thrombocytopenia
Bibliographic record
Abstract
Heparin-induced thrombocytopenia (HIT) is an uncommon problem in hemodialysis (HD) patients. There have been a few reports on the use of lepirudin, argatroban, or danaparoid in the management of extracorporeal thrombosis (ECT) during dialysis in these patients, because heparin is contraindicated. Here, we report the first long-term use of bivalirudin to prevent ECT. Our study was conducted at Fahd Bin Jassim Kidney Center in Doha, Qatar. All patients diagnosed with HIT were included. A bivalirudin treatment protocol was developed with the initial dosage and dosage adjustments based on the value of activated partial thromboplastin time (aPTT), the risk of bleeding, and the recurrence of ECT. Eight patients were positive for HIT AB. Among them, three were excluded: two due to the use of warfarin for atrial fibrillation and one due to a negative repeat HIT AB test with no ECT. Five patients who were positive for HIT AB and experienced recurrent ECT events during dialysis were included. These patients were monitored while on bivalirudin protocol for a mean of 4.6 ± 2 months, during which they received a mean number of HD treatments of 66 ± 24. There were no bleeding events or adverse reactions related to bivalirudin during the study. Here, we report the first long-term successful use of a bivalirudin protocol to prevent ECT in ambulatory HD patients with HIT. This protocol allowed for a simple dosing initiation with easy adjustment based on weight, aPTT, and recurrence of ECT events. The protocol provided excellent safety.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".