Bivalirudin Experience in a Heterogeneous Ventricular Assist Device Population
Bibliographic record
Abstract
Ventricular assist devices (VADs) are an increasingly common therapy for end-stage heart failure across all ages as a bridge to recovery or transplant and more recently as destination therapy. With increasing experience and difficulties with establishing therapeutic heparin levels, we have begun to explore the effectiveness of direct thrombin inhibitors in this patient population. This is a retrospective review of all long-term VAD patients, both adult and pediatric, who were anticoagulated with bivalirudin between January 2009 and January 2016. The starting dose was 0.3 mg/kg/hr, and dose was titrated for a goal partial thromboplastin time (PTT) of 70-100. There were 14 patients (13 males, 5 ≤18 years) with 17 episodes of bivalirudin therapy. The median age on initiation was 45 years (range, 15 days-67 years) with 10 episodes associated with a HeartWare HVAD, five a HeartMate II, and two with a Berlin Heart EXCOR. The predominant indication of bivalirudin therapy was suspected pump thrombosis (13/17). The median time from VAD insertion to initiation of bivalirudin was 116 days (range, 3-1,870) with the median duration of therapy being 21 days (range, 3-113). In patients with pump thrombosis, the mean baseline lactate dehydrogenase (LDH) was 229 ± 64 U/L, peak 690 ± 380 U/L, and decreased to 330 ± 243 U/L when bivalirudin was stopped. The outcomes following suspected pump thrombosis included: transitioned to warfarin (n = 7), death in two destination therapy patients who did not undergo pump exchange, transplantation (n = 2), and pump exchange (n = 2). A major bleeding complication occurred in only one patient. Our experience highlights the potential use of bivalirudin in a heterogenous VAD population. Although these initial results suggest some potential role for direct thrombin inhibitors for use in long-term VADs, larger prospective studies are required to support these preliminary observations and to determine who may benefit from direct thrombin inhibitors (DTIs) and the side effect profile in this patient population.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".