Importance of implant technique on risk of major paravalvular leak (PVL) after St. Jude mechanical heart valve replacement: a report from the Artificial Valve Endocarditis Reduction Trial (AVERT)☆
Bibliographic record
Abstract
OBJECTIVE: To examine risk factors for major paravalvular leak (PVL) events after mechanical heart valve replacement. METHODS: We analyzed outcome of 807 patients randomized into the Artificial Valve Endocarditis Reduction Trial (AVERT). The mean follow-up time was 30.6 months and 21 major PVL events were reported. Three additional major PVL events associated with endocarditis were excluded from analysis. All baseline medical history variables, as well as operative parameters (including use of pledgets and suture technique) were examined using Cox regression. RESULTS: Major PVL was reported after 11 aortic, 9 mitral, and 1 double valve replacement. 6/404 (1.5%) patients with conventional valves experienced a major PVL event versus 15/403 (3.7%) in the Silzone group. 10/172 (5.8%) patients with valve suture technique without pledgets experienced a major PVL event versus 11/635 (1.7%) patients with pledgets. Final multivariable model showed that only suture technique without pledgets (p=0.005) was an independent significant risk factor for major PVL events. Silzone cuff showed a strong trend (p=0.055). CONCLUSIONS: Suture technique without pledgets is an independent significant risk factor for major PVL events. In this study, use of pledgets during valve replacement had a protective effect against subsequent paravalvular leak, supporting the use of buttress reinforcement for valve suture. The use of Silzone cuff, although not statistically significant, showed a strong trend as a risk factor.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".