Prevalence and impact of prosthesis-patient mismatch in patients with paradoxical low-flow severe aortic stenosis
Bibliographic record
Abstract
Purpose: Patients with severe aortic stenosis (AS) and paradoxical low flow (PLF) (indexed stroke volume< 35ml/m2) despite preserved left ventricular ejection fraction >50%, have worse outcome compared to those with AS but normal flow. Moreover, Prosthesis-Patient Mismatch (PPM) (indexed prosthetic valve effective orifice area< 0.85cm2/m2) after aortic valve replacement (AVR) is a predictor of higher mortality. However, the impact of PPM in patients with PLFAS on long-term survival is unknown. Our aim was to analyze the prevalence and the impact on long-term survival of PPM in patients with PLFAS. Methods: 667 consecutive patients (age 74±8 years, 42% female, AVA 0.69±0.16 cm2) with preserved LVEF who underwent AVR for severe AS at our institution between 2000 and 2010 were included in this study. Patients were divided into 4 groups according to the presence/absence of PLF at cardiac catheterization and presence/absence of PPM following AVR and we compared short and long-term survival between these groups. Results: Among the 667 patients, 26% had PLFAS and PPM occurred in 54% of patients after AVR. Compared to patients with no PLF & no PPM (36% of the total cohort), those with PLF & PPM (15%) were significantly older, with more comorbidities. The 30-day mortality did not differ between the PLF- PPM and no-PLF-no PPM group. The 10-yr survival rate was significantly reduced in the PLF-PPM (37±9%) group compared to no PLF-no PPM (70±5%; p=0.003). In multivariate analysis adjusting for all predictors of survival, concomitant presence of PLF & PPM was an independent predictor of survival (HR= 2.68 95% CI: 1.5-4.4; p=0.0003) 10-year survival according to PPM/PLF Conclusion: In this catheterization-based study, patients with PLF and PPM have worse outcome when compared to those without these 2 conditions.
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 | 0.003 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".