Current trends of sutureless and rapid deployment valves: an 11-year experience from the Sutureless and Rapid Deployment International Registry
Bibliographic record
Abstract
OBJECTIVES: Current evidence on sutureless and rapid deployment aortic valve replacement (SURD-AVR) is limited and does not allow for the assessment of the clinical impact and the evolution of procedural and clinical outcomes of this new valve technology. The Sutureless and Rapid Deployment International Registry (SURD-IR) represents a unique opportunity to evaluate the current trends and outcomes of SURD-AVR interventions. METHODS: Data from 3682 patients enrolled between 2007 and 2018 were analysed. Patients were divided according to the date of surgery into 6 equal groups and by the type of intervention: isolated SURD-AVR (n = 2472) and combined SURD-AVR (n = 1086). RESULTS: Across the 11-year study period, significant changes occurred in patient characteristics including a decrease in age and in estimated surgical risk. Less invasive approaches for isolated SURD-AVR increased considerably from 49.4% to 85.5%. The overall in-hospital mortality rate was 1.6% and 3.9% in isolated and combined procedures, respectively, with no change over time. The rate of perioperative stroke decreased significantly (from 4% to 0.5%), as did the rates of postoperative pacemaker implantation (from 12.8% to 5.9%) and aortic regurgitation (from 17.8% to 2.7%). CONCLUSIONS: The present study provides a comprehensive analysis of the current trends and results of SURD-AVR interventions. The most notable changes over time were the increasing implantation of SURD valves in a younger population, with more frequent utilization of less invasive techniques. SURD-AVR demonstrated remarkable improvements in clinical outcomes with a significant reduction in the rates of stroke, pacemaker implantation and postoperative aortic regurgitation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".