Prosthesis-Patient Mismatch Negatively Affects Outcomes after Mitral Valve Replacement: Meta- Analysis of 10,239 Patients
Bibliographic record
Abstract
OBJECTIVE: This study sought to evaluate the impact of prosthesis-patient mismatch on the risk of perioperative and long-term mortality after mitral valve replacement. METHODS: Databases were researched for studies published until December 2018. Main outcomes of interest were perioperative and 10-year mortality and echocardiographic parameters. RESULTS: The research yielded 2,985 studies for inclusion. Of these, 16 articles were analyzed, and their data extracted. The total number of patients included was 10,239, who underwent mitral valve replacement. The incidence of prosthesis-patient mismatch after mitral valve replacement was 53.7% (5,499 with prosthesis-patient mismatch and 4,740 without prosthesis-patient mismatch). Perioperative (OR 1.519; 95%CI 1.194-1.931, P<0.001) and 10-year (OR 1.515; 95%CI 1.280-1.795, P<0.001) mortality was increased in patients with prosthesis-patient mismatch. Patients with prosthesis-patient mismatch after mitral valve replacement had higher systolic pulmonary artery pressure and transprosthethic gradient and lower indexed effective orifice area and left ventricle ejection fraction. CONCLUSION: Prosthesis-patient mismatch increases perioperative and long-term mortality. Prosthesis-patient mismatch is also associated with pulmonary hypertension and depressed left ventricle systolic function. The findings of this study support the implementation of surgical strategies to prevent prosthesis-patient mismatch 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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.021 |
| 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 | 0.001 | 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".