Abstract 17651: Long-Term Outcomes of Prosthesis-Patient Mismatch After Surgical Aortic Valve Replacement: Meta-Analysis of Kaplan-Meier-Derived Individual Patient Data
Bibliographic record
Abstract
Introduction - It remains controversial whether prosthesis-patient mismatch (PPM) impacts long-term outcomes after surgical aortic valve replacement (SAVR). Hypothesis - PPM is associated with poor outcomes after SAVR. Methods - Study-level meta-analysis of reconstructed time-to-event data from Kaplan-Meier curves of studies published by March 2023. Results - Sixty-five studies met our eligibility criteria and included 122,989 patients (any PPM: 68,332 patients, 55.6%). At 25 years of follow-up, the survival rates were 11.8% and 20.6% in patients with and without any PPM, respectively (HR 1.16; 95%CI 1.13-1.18, p<0.001). At 20 years of follow-up, the survival rates were 19.5%, 12.1 and 8.8% in patients with no, moderate and severe, respectively (moderate vs no PPM: HR 1.09; 95%CI 1.06-1.11; p<0.001; severe vs no PPM: HR 1.29; 95%CI 1.24-1.35; p<0.001). RMST was 0.8 years shorter in patients with moderate PPM, and 2.1 years shorter in patients with severe PPM in comparison with patients without PPM (10.2 years and 8.9 years vs. 11.0 years respectively, p<0.001). PPM was associated with higher risk of cardiac death, HF-related hospitalizations and aortic valves reinterventions over time (p<0.001). Statistically significant associations between PPM and worse survival were observed regardless of type of valves (bioprosthetic vs mechanical valves), contemporary PPM definitions unadjusted and adjusted to body mass index and PPM quantification method (in vitro, in vivo, Doppler echocardiography). Our meta-regression analysis revealed that populations with more women tend to have higher HRs for all-cause death associated with PPM. Conclusions - The results of the present study suggest that any degree of PPM is associated with poorer long-term outcomes following SAVR and provide support for implementation of preventive strategies to avoid PPM after SAVR.
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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 | 0.018 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.039 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".