Patient‐prosthesis mismatch and surgical aortic valve replacement outcomes: Retrospective analysis of single‐center surgical data
Bibliographic record
Abstract
BACKGROUND: Patient-prosthesis mismatch (PPM) has been identified as a risk factor for mortality and reoperation in patients undergoing surgical aortic valve replacement (SAVR). We present a retrospective analysis of risk factors for PPM and the effects of PPM on early postoperative outcomes after SAVR. METHODS: Chart review was conducted for patients (N = 3003) undergoing SAVR. PPM was calculated from valve reference orifice areas and patient body surface area. Logistic regression was used to analyze risk factors for PPM and develop a risk score from these results. Regression was also conducted to identify associations between projected PPM status and postoperative outcomes. RESULTS: Risk factors for PPM included female sex, higher body mass index (BMI), and use of the St. Jude Epic valve. Patients receiving St. Jude trifecta valves or mechanical valves were less likely to have predicted PPM. We developed a risk score using BMI, sex, and valve type, and retrospectively predicted PPM in our cohort. Mild PPM (odds ratio [OR] = 2.267), severe PPM (OR = 2.869), male sex (OR = 2.091), and younger age (OR = 0.940) were all predictors of SAVR reoperation, while aortic root replacement was associated with reduced reoperation rates (OR = 0.122). Severe PPM carried a risk of in-hospital mortality (OR = 3.599), and moderate PPM carried a smaller but significant risk (OR = 1.920). Other factors increasing postoperative morbidity and mortality included older age, renal failure, and diabetes. CONCLUSION: PPM could be retrospectively predicted in our cohort using a risk calculation from sex, BMI and valve type. We conclude that all degrees of PPM carry risk for mortality and reoperation.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".