Increasing Wait-Time Mortality for Severe Aortic Stenosis
Bibliographic record
Abstract
Background: Transcatheter aortic valve replacement (TAVR) has emerged as a reasonable alternative to surgical aortic valve replacement (SAVR) for patients with severe aortic stenosis (AS). There is limited data on temporal trends in wait-times and access to care for patients with AS, irrespective of treatment modality. We sought to investigate the trends in wait-times for the treatment (either SAVR or TAVR) of AS in Ontario, Canada, and to understand the drivers of wait-list mortality and hospitalization due to heart failure. Methods: In this population-level retrospective cohort study, we identified patients from April 1, 2012, to March 31, 2018, who were referred for treatment of symptomatic severe AS awaiting either SAVR or TAVR. The primary outcome was the median total wait-time from referral date to either SAVR or TAVR procedure. Primary clinical outcomes were all-cause mortality and heart failure-related hospitalizations while on the wait-list. Results: The referral cohort consisted of a total of 22 876 referrals for aortic valve replacement, with (N=8098) TAVR and (N=14 778) SAVR referrals. The mean and median wait times for the overall AVR cohort were 87 and 59 days, respectively. The TAVR subcohort had longer wait-times (median 84 days) compared with the SAVR subcohort (median 50 days). Year over year, there was a statistically significant an increase in wait-times ( P <0.001) for the overall AS cohort as well as each of the TAVR ( P <0.0001) and SAVR ( P <0.0001) subgroups. Wait-time mortality was 2.5% (TAVR 5.2% and SAVR 1.05%), while the cumulative probability of heart failure hospitalization was 3.6% (TAVR 7.7% and SAVR 1.3%). Conclusions: In patients with severe symptomatic AS awaiting aortic valve replacement, there has been a trend of increasing wait times for both SAVR and TAVR. This was associated with increasing mortality and hospitalizations related to heart failure while on the wait-list.
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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 | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".