Do postoperative hemodynamic parameters add prognostic value for mortality after surgical aortic valve replacement?
Bibliographic record
Abstract
Background: Although various hemodynamic parameters to assess prosthetic performance are available, prosthesis-patient mismatch (PPM) is defined exclusively by effective orifice area (EOA) index thresholds. Adjusting for the Society of Thoracic Surgeons predicted risk of mortality (STS PROM), we aimed to explore the added value of postoperative hemodynamic parameters for the prediction of all-cause mortality at 5 years after aortic valve replacement. Methods: Data were obtained from the Pericardial Surgical Aortic Valve Replacement (PERIGON) Pivotal Trial, a multicenter prospective cohort study examining the performance of the Avalus bioprosthesis. Candidate predictors were assessed at the first follow-up visit; patients who had no echocardiography data, withdrew consent, or died before this visit were excluded. Candidate predictors included peak jet velocity, mean pressure gradient, EOA, predicted and measured EOA index, Doppler velocity index, indexed internal prosthesis orifice area, and categories for PPM. The performance of Cox models was investigated using the c-statistic and net reclassification improvement (NRI), among other tools. Results: A total of 1118 patients received the study valve, of whom 1022 were eligible for the present analysis. In univariable analysis, STS PROM was the sole significant predictor of all-cause mortality (hazard ratio, 1.40; 95% confidence interval, 1.26-1.55). When extending the STS PROM with single hemodynamic parameters, neither the c-statistics nor the NRIs demonstrated added prognostic value compared to a model with STS PROM alone. Similar findings were observed when multiple hemodynamic parameters were added. Conclusions: The STS PROM was found to be the main predictor of patient prognosis. The additional prognostic value of postoperative hemodynamic parameters for the prediction of all-cause mortality was limited.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".