Impact of Prosthesis‐Patient Mismatch After Surgical Aortic Valve Replacement: Systematic Review and Meta‐Analysis of Reconstructed Time‐to‐Event Data of 122 989 Patients With 592 952 Patient‐Years
Bibliographic record
Abstract
Background It remains controversial whether prosthesis‐patient mismatch (PPM) impacts long‐term outcomes after surgical aortic valve replacement. We aimed to evaluate the association of PPM with mortality, rehospitalizations, and aortic valve reinterventions. Methods and Results We performed a systematic review with meta‐analysis of reconstructed time‐to‐event data of studies published by March 2023 (according to the Preferred Reporting Items for Systematic Reviews and Meta‐Analyses). 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 (hazard ratio [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 PPM, respectively (moderate versus no PPM: HR, 1.09 [95% CI, 1.06–1.11], P <0.001; severe versus no PPM: HR, 1.29 [95% CI, 1.24–1.35], P <0.001). PPM was associated with higher risk of cardiac death, heart failure–related hospitalizations, and aortic valve reinterventions over time ( P <0.001). Statistically significant associations between PPM and worse survival were observed regardless of valve type (bioprosthetic versus mechanical valves), contemporary PPM definitions unadjusted and adjusted for 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 surgical aortic valve replacement and provide support for implementation of preventive strategies to avoid PPM after surgical aortic valve replacement.
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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 | 0.011 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.029 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".