Successful Treatment of Pediatric Ventricular Assist Device Thrombosis
Bibliographic record
Abstract
Pump thrombosis represents a significant cause of morbidity and mortality in patients on continuous flow ventricular assist devices (CF-VAD). Pump thrombosis in the pediatric CF-VAD population has been reported between 11% and 44%, with the largest reported series from the PediMACS registry reporting a rate of approximately 15%. We report our early experience with four pediatric patients who developed pump thrombosis on a CF-VAD. Our limited experience suggests that the treatment of pediatric VAD thrombosis can be approached with similar principles to the adult population. Our current strategy includes:i. Initiating treatment with bivalirudin for an isolated rise in lactate dehydrogenase (LDH) with no corresponding rapid rise in plasma-free hemoglobin which may prevent further progression.ii. Treatment with a low-dose systemic tissue plasminogen activator (TPA) protocol as opposed to targeted therapy via catheter intervention if bivalirudin fails.iii. If there are concerns with respect to the impact of hemolysis on kidney function or the patient is close to a previous surgery, device exchange can be considered.The balance between achieving appropriate anticoagulation/antiplatelet therapy in the face of bleeding/hemorrhagic complications remains a challenge. There is a need for larger studies in the pediatric population to outline an algorithm for the definitive management of VAD thrombosis.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".