Impact of Prosthesis-Patient Mismatch on Survival After Mitral Valve Replacement
Bibliographic record
Abstract
BACKGROUND: We recently reported that valve prosthesis-patient mismatch (PPM) is associated with persisting pulmonary hypertension after mitral valve replacement. Thus, the objective of this study was to evaluate the impact of PPM on mortality in patients undergoing mitral valve replacement. METHODS AND RESULTS: The indexed valve effective orifice area was estimated for each type and size of prosthesis being implanted in 929 consecutive patients and used to define PPM as not clinically significant if > 1.2 cm2/m2, as moderate if > 0.9 and < or = 1.2 cm2/m2, and as severe if < or = 0.9 cm2/m2. Moderate PPM was present in 69% of patients; severe PPM was seen in 9%. For patients with severe PPM, 6-year survival (74+/-5%) and 12-year survival (63+/-7%) were significantly less than for patients with moderate PPM (84+/-1% and 76+/-2%; P=0.027) or nonsignificant PPM (90+/-2% and 82+/-4%; P=0.002). On multivariate analysis, severe PPM was associated with higher mortality (hazard ratio, 3.2; 95% confidence interval, 1.5 to 6.8; P=0.003). CONCLUSIONS: Severe PPM is an independent predictor of mortality after mitral valve replacement. As opposed to other independent risk factors, PPM may be avoided or its severity may be reduced with the use of a prospective strategy at the time of operation. For patients identified as being at risk for severe PPM, every effort should be made to implant a prosthesis with a larger effective orifice area.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.001 | 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".