Comparing Trajectory of Surgical Aortic Valve Replacement in the Early vs. Late Transcatheter Aortic Valve Replacement Era
Bibliographic record
Abstract
Background: Traditionally, the only effective treatment for aortic stenosis was surgical aortic valve replacement (SAVR). Transcatheter aortic valve replacement (TAVR) was approved in the United States in late 2011 and provided a critical alternative therapy. Our aims were to investigate the trends in the utilization of SAVR in the early vs. late TAVR era and to assess SAVR and TAVR outcomes. Methods: Using the 2011–2017 National Inpatient Sample database, we identified hospitalizations for patients with a most responsible diagnosis of aortic stenosis during which an aortic valve replacement (AVR) was performed, either SAVR or TAVR. Patients' sociodemographic and clinical characteristics, procedure complications, length of stay, and mortality were analyzed. Multivariable analyses were performed to identify predictors of in-hospital mortality. Piecewise regression analyses were performed to assess temporal trends in SAVR and TAVR utilization. Results: A total of 542,734 AVR procedures were analyzed. The utilization of SAVR was steady until 2014 with a significant downward trend in the following years 2015–2017 (P = 0.026). In contrast, a steady upward trend was observed in the TAVR procedure with a significant increase during the years 2015–2017 (P = 0.006). Higher in-hospital mortality was observed in SAVR patients. The mortality rate declined from 2011 to 2017 in a significantly higher proportion in the TAVR compared with the SAVR group. Conclusion: Utilization of SAVR showed a downward trend during the late TAVR era (2015–2017), and TAVR utilization demonstrated a steady upward trend during the years 2011–2017. Higher in-hospital mortality was recorded in patients who underwent SAVR.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".