Impact of Valve Prosthesis-Patient Mismatch on Short-Term Mortality After Aortic Valve Replacement
Bibliographic record
Abstract
BACKGROUND: The prosthesis used for aortic valve replacement (AVR) can be too small in relation to body size, thus causing valve prosthesis-patient mismatch (PPM) and abnormally high transvalvular pressure gradients. This study examined if there is a relation between PPM and short-term mortality after operation. METHODS AND RESULTS: The indexed valve effective orifice area (EOA) was estimated for each type and size of prosthesis being implanted in 1266 consecutive patients and used to define PPM as not clinically significant if >0.85 cm2/m2, as moderate if >0.65 cm2/m2 and <or=0.85 cm2/m2, and as severe if <or=0.65 cm2/m2; it was correlated with 30-day mortality and compared with other relevant variables. Moderate or severe PPM was present in 38% of patients. Thirty-day mortality was 4.6% (58/1266 patients) and the strongest independent predictors in multivariate analysis were left ventricular ejection fraction <40% (P=0.007), infectious endocarditis (P=0.002), emergent/salvage operation (P=0.002), cardiopulmonary bypass time >120 minutes (P=0.001), and PPM (P=0.003). Relative risk of mortality was increased 2.1-fold (95% confidence interval, 1.2 to 3.7) in patients with moderate PPM and 11.4-fold (4.4 to 29.5) in those with severe PPM. Moreover, risk of mortality for every category of PPM was higher in patients with a left ventricular ejection fraction <40% as compared with >or=40% (nonsignificant PPM, 2.7 versus 1.0; moderate PPM, 7.1 versus 1.8; severe PPM, 77.1 versus 11.3). CONCLUSIONS: PPM is a strong and independent predictor of short-term mortality among patients undergoing AVR, and its impact is related both to its degree of severity and the status of left ventricular function. In contrast to other risk factors, moderate-severe PPM can be largely avoided with the use of a prospective strategy at the time of operation.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".