Prevalence and Long-Term Outcome of Aortic Prosthesis–Patient Mismatch in Patients With Paradoxical Low-Flow Severe Aortic Stenosis
Bibliographic record
Abstract
BACKGROUND: Patients with severe aortic stenosis (AS) and paradoxical low flow (PLF) have worse outcome compared with those with normal flow. Furthermore, prosthesis-patient mismatch (PPM) after aortic valve replacement is a predictor of reduced survival. However, the prevalence and prognostic impact of PPM in patients with PLF-AS are unknown. We aimed to analyze the prevalence and long-term survival of PPM in patients with PLF-AS. METHODS AND RESULTS: Between 2000 and 2010, 677 patients with severe AS, preserved left ventricular ejection fraction, and aortic valve replacement were included (74±8 years; 42% women; aortic valve area, 0.69±0.16 cm(2)). A PLF (indexed stroke volume ≤35 mL/m(2)) was found in 26%, and after aortic valve replacement, 54% of patients had PPM, defined as an indexed effective orifice area ≤0.85 cm(2)/m(2). The combined presence of PLF and PPM was found in 15%. Compared with patients with noPLF/noPPM, those with PLF/PPM were significantly older, with more comorbidities. They also received smaller and biological bioprosthesis more often (all P<0.01). Although early mortality was not significantly different between groups, the 10-year survival rate was significantly reduced in case of PLF/PPM compared with noPLF/noPPM (38±9% versus 70±5%; P=0.002), even after multivariable adjustment (hazard ratio, 2.58; 95% confidence interval, 1.5-4.45; P=0.0007). CONCLUSIONS: In this large catheterization-based study, the coexistence of PLF-AS before surgery and PPM after surgery is associated with the poorest outcome.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".