Canary in the cardiac-valve coal mine. Flow velocity and inferred shear during prosthetic valve closure –predictors of blood damage and clotting
Bibliographic record
Abstract
ABSTRACT Objective To demonstrate a clear link between predicted blood shear forces during valve closure and thrombogenicity that explains the thrombogenic difference between tissue and mechanical valves and provides a practical metric to develop and refine prosthetic valve designs for reduced thrombogenicity. Methods Pulsatile and quasi-steady flow systems were used for testing. The time-variation of projected open area (POA) was measured using analog opto-electronics calibrated to projected reference orifice areas. Flow velocity determined over the cardiac cycle equates to instantaneous volumetric flow rate divided by POA. For the closed valve interval, data from quasi-steady back pressure/flow tests was obtained. Performance ranked by derived maximum negative and positive closing flow velocities, evidence potential clinical thrombogenicity via inferred velocity gradients (shear). Clinical, prototype and control valves were tested. Results Blood shear and clot potential from multiple test datasets guided empirical optimization and comparison of valve designs. Assessment of a 3-D printed prototype valve design (BV3D) purposed for early soft closure demonstrates potential for reduced thrombogenic potential. Conclusions The relationship between leaflet geometry, flow velocity and predicted shear at valve closure illuminated an important source of prosthetic valve thrombogenicity. With an appreciation for this relationship and based on our experiment generated comparative data, we achieved optimization of valve prototypes with potential for reduced thrombogenicity. Competing Interests None declared. Financial Disclosure This research has been done on a pro bono basis by all authors. Graphical Abstract Visualization of water jetting through closed mechanical heart valve under steady flow. Under pulsatile conditions, similar jet patterns near valve closure and leaflet rebound are likely. Dynamic metrics for several valves assessed in vitro are important in prediction of comparable blood cell damage and potential life-threatening thrombotic outcomes. Red star indicates moment of valve closure. CENTRAL MESSAGE A derived laboratory metric for valve closing flow velocity offers a way to rank valve models for potential blood damage. These results provide new insight and a mechanistic explanation for prior clinical observations where aortic and mitral valve replacements differ in thrombogenic potential and anticoagulation requirement. The study suggests a path forward to design and evaluate novel mechanical valve models for future development. As multiple modifications to mechanical and bioprosthetic valves have not resolved chronic shortcomings related to thrombogenicity and durability, a new development avenue was required to lead to eliminate thrombogenicity in the former and extend durability in the latter. PERSPECTIVE Prosthetic mechanical valve devices cause blood cell damage. Activation of the coagulation cascade is initiated by dynamic valve function. Design innovation focusing on valve closure behavior may reduce valve thrombogenic potential. Our study demonstrates that valve design can be empirically optimized with emphasis on that phase. SIGNIFICANCE Emphasis on open valve performance has encouraged a long-standing bias while under appreciation of the closing phase vital to identification of potential thrombogenic complications persist. Our multiple data sets are useful in challenging this bias. Dynamic motion(s) of mechanical valves and derived regional flow velocity are impacted by valve geometry. Focus on valve closure dynamics may lead to the development of potentially less thrombogenic prototype valves. Laboratory experiments support the supposition that valve regional flow velocity is associated with valve thrombogenic potential. This study compares three clinical valves and two experimental prototypes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".