Rapid Deployment Biological Aortic Valve Replacement in Redo Operations: A Retrospective Real-Word Experience Report of Clinical and Echocardiographic Outcomes
Bibliographic record
Abstract
Abstract Objective This retrospective study aimed to compare the outcomes of rapid deployment aortic valve replacement (rdAVR) and conventional bioprosthetic sutured AVR (cAVR) in high-risk patients undergoing redo surgery. Methods A total of 79 patients who underwent redo AVR between 2014 and 2021 were included in the study. Of these, 27 patients underwent rdAVR and 52 underwent cAVR. Patient characteristics and clinical outcomes were analysed using multivariate regression and Cox-survival analysis. Results The groups were similar in terms of age, gender, left ventricular function, and number of previous sternotomies. In cases of isolated AVR, rdAVR had significantly lower cross clamp times than cAVR (71 vs. 86 minutes, p = 0.03). Postoperatively, 4 cAVR patients required pacemaker compared to zero patients in the rdAVR group. There were no significant differences between the two groups in terms of postoperative complications, intrahospital stay (median 9 days, IQR 7–20), or in-hospital mortality (1 rdAVR; 2 cAVR). The long-term survival rate was similar between the rdAVR (90%) and cAVR (92%) groups (log rank p = 0.8). The transvalvular gradients at follow-up were not affected by the type of valve used, regardless of the valve size (coef 2.68, 95%CI -3.14-8.50, p = 0.36). Conclusion The study suggests that rdAVR is a feasible and safe alternative to cAVR in high-risk patients undergoing redo surgery. The use of rdAVR offers comparable outcomes to cAVR, with reduced cross clamp times and a lower incidence of postoperative pacemaker requirement in isolated AVR cases. The
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".