Surgical aortic valve replacement and patient–prosthesis mismatch: a meta-analysis of 108 182 patients
Bibliographic record
Abstract
OBJECTIVES: This study sought to evaluate the impact of patient-prosthesis mismatch (PPM) on the risk of perioperative, early-, mid- and long-term mortality rates after surgical aortic valve replacement. METHODS: Databases were searched for studies published until March 2018. The main outcomes of interest were perioperative mortality, 1-year mortality, 5-year mortality and 10-year mortality. RESULTS: The search yielded 3761 studies for inclusion. Of these, 70 articles were analysed, and their data were extracted. The total number of patients included was 108 182 who underwent surgical aortic valve replacement. The incidence of PPM after surgical aortic valve replacement was 53.7% (58 116 with PPM and 50 066 without PPM). Perioperative mortality [odds ratio (OR) 1.491, 95% confidence interval (CI) 1.302-1.707; P < 0.001], 1-year mortality (OR 1.465, 95% CI 1.277-1.681; P < 0.001), 5-year mortality (OR 1.358, 95% CI 1.218-1.515; P < 0.001) and 10-year mortality (OR 1.534, 95% CI 1.290-1.825; P < 0.001) were increased in patients with PPM. Both severe PPM and moderate PPM were associated with increased risk of perioperative mortality, 1-year mortality, 5-year mortality and 10-year mortality when analysed together and separately, although we observed a higher risk in the group with severe PPM. CONCLUSIONS: Moderate/severe PPM increases perioperative, early-, mid- and long-term mortality rates proportionally to its severity. The findings of this study support the implementation of surgical strategies to prevent PPM in order to decrease mortality rates.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.026 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".