Subclinical bioprosthetic aortic valve thrombosis
Bibliographic record
Abstract
PURPOSE OF REVIEW: A recently published study has alerted the cardiovascular community to the existence of a significant and previously unrecognized risk of subclinical valve thrombosis following implantation of surgical and catheter-based bioprosthetic valves. The purpose of this article is to review our current understanding of this new clinical entity and to identify unanswered questions and areas for future research. RECENT FINDINGS: Subclinical bioprosthetic valve thrombosis (BPVT) is a more common phenomenon than previously appreciated. It appears that the incidence of BPVT is higher following transcatheter aortic valve replacement compared with surgical aortic valve replacement. Four-dimensional computed tomography (CT) is the most sensitive imaging modality for detection of leaflet immobility and subclinical BPVT. Certain echocardiographic findings, such as increasing transaortic gradients, increased cusp thickness and abnormal cusp mobility, predict the presence of BPVT on four-dimensional CT. There is a growing body of evidence linking subclinical BPVT with premature valvular hemodynamic deterioration and structural valve degeneration. Furthermore, subclinical leaflet thrombosis may constitute a nidus for unrecognized subacute cerebral or other thromboembolic events. Oral anticoagulation seems effective in both the prevention and treatment of BPVT. SUMMARY: Subclinical valve thrombosis is an important and underappreciated cause of early bioprosthetic valve failure. Although several recent studies have improved our understanding of this newly recognized clinical entity, a number of questions remain unanswered. Further studies are warranted to elucidate the true incidence of subclinical BPVT, its clinical consequences, as well as the optimal antithrombotic regimen following bioprosthetic valve implantation. The subgroups of patients at highest risk of BPVT will need to be identified for risk stratification purposes. Several ongoing clinical trials will shed some light on these important issues.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".