Simulation of the effect of hemolysis on thrombosis in blood-contacting medical devices
Bibliographic record
Abstract
Heart failure, broadly characterized by the gradual decline of the ability of the heart to maintain adequate blood flow throughout the body's vascular network of veins and arteries, is one of the leading causes of death worldwide. Mechanical Circulatory Support is one of the few available alternative interventions for late-stage heart failure with reduced ejection fraction. A ventricular assist device is surgically implanted and connected to the left and or right heart ventricles to provide additional bloodflow, off-loading the work required by the heart to maintain circulation. Modern mechanical circulatory support devices generate non-physiological flow conditions that can lead to the damage and rupture of blood cells (hemolysis), and the formation of blood clots (thrombosis), which pose severe health risks to the patient. It is essential to improve prediction tools for blood damage to reduce the risk of hemolysis and thrombosis. A simulation-based approach examines the interaction between hemolysis and thrombosis. Incompressible finite-volume computational fluid dynamics simulations are executed on an open-hub axial flow ventricular assist device. A continuum model of thrombosis and the intrinsic coagulation process is extended to include the effect of hemolysis. The model accounts for the effect of activation of platelets by shear stress, paracrine signaling, adhesion, and hemoglobin and ADP released during hemolysis. The effect of hemolysis with thrombosis is modelled by accounting for the hyper-adhesivity of von-Willebrand Factor on extracellular hemoglobin, and the increased rate of platelet activation induced by ADP release. Thrombosis is assessed at varying inflow rates and rotor speeds, and cases are executed where thrombosis is affected by ADP release and Hb-induced hyper-adhesivity. It is found that there is a non-negligible effect from hemolysis on thrombosis across a range of rotor speeds, and that hyperadhesivity plays a dominant role in thrombus formation in the presence of hemolysis.
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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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".