Trends in aortic valve replacement for aortic stenosis: a French nationwide study
Bibliographic record
Abstract
AIMS: Transcatheter aortic valve replacement (TAVR) as an alternative to surgical aortic valve replacement (SAVR) has profoundly changed the management of patients with aortic valve stenosis (AS). Large unbiased nationwide data regarding TAVR implementation, impact on SAVR and their respective outcomes are scarce. METHODS AND RESULTS: Based on a French administrative hospital-discharge database, we collected data on all consecutive aortic valve replacements (AVRs) performed in France for AS between 2007 and 2019 [106 253 isolated SAVR (49%), 46 514 combined SAVR (21%), and 65 651 TAVR (30%)]. The number of AVR linearly increased between 2007 and 2019 (from 10 892 to 23 109, P for trend < 0.0001) due to a marked increase in TAVR (from 253 to 13 030, P for trend < 0.0001), while SAVR increased up to 2013 and then declined (10 892 in 2007, 12 699 in 2013, and 10 079 in 2019). The Charlson index decreased linearly for TAVR, but in two steps for SAVR (2011 and 2017). In-hospital mortality rates of both SAVR and TAVR declined (both P for trend < 0.0001) and were similar or lower for TAVR than for isolated SAVR in patients 75 years or above in the last 3 years (2017-19). Complication rates of TAVR also declined but permanent pacemaker rates remained high and length of stay substantial (16.7% and median 6 days, respectively, in 2017-19). CONCLUSION: The number of AVR has doubled in a decade and TAVR has become the dominant form of AVR in 2018. The improvement in patient profiles seems to have anticipated the demonstrated benefit of TAVR in intermediate and low-risk patients. In patients 75 years or older, TAVR should be considered as the first option. We also highlight two important areas for improvement, the high permanent pacemaker rates, and the long length of stay even in the contemporary era. Our results may have major implications for clinical practice and policymakers.
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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) | 0.000 | 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".