Anticoagulation strategies for left ventricular assist devices
Bibliographic record
Abstract
PURPOSE OF REVIEW: Pump thrombosis can be a devastating complication in patients with a left ventricular assist device (LVAD). Treatment options with intravenous anticoagulation can lead to further complications. The present review discusses the current antithrombotic and anticoagulation strategies following LVAD implantation and during suspected pump thrombosis. RECENT FINDINGS: Recently, a significant increase in pump thrombosis (HeartMate II) at 3 months after LVAD implantation starting in March 2011 has been observed. This observation is likely multifaceted; however, recent changes in perioperative anticoagulation, accepting lower target international normalized ratios and lack of heparin bridging may play a substantial role. The International Society for Heart and Lung Transplantation published guidelines surrounding LVAD anticoagulation and management options in the setting of pump thrombosis. SUMMARY: Recommendations for thromboprophylaxis in patients with LVADs are scarce. The International Society for Heart and Lung Transplantation has put together minimum criteria for perioperative anticoagulation; however, this is on the basis of poor level of evidence (observational studies and expert opinion). Ultimately, clinicians will need to individualize the intensity and timing of anticoagulation following LVAD implantation to ensure adequate thromboprophylaxis while simultaneously minimizing bleeding.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".