Temporal Trends and Clinical Consequences of Wait Times for Transcatheter Aortic Valve Replacement
Bibliographic record
Abstract
BACKGROUND: Transcatheter aortic valve replacement (TAVR) represents a paradigm shift in the therapeutic options for patients with severe aortic stenosis. However, rapid and exponential growth in TAVR demand may overwhelm capacity, translating to inadequate access and prolonged wait times. Our objective was to evaluate temporal trends in TAVR wait times and the associated clinical consequences. METHODS: In this population-based study in Ontario, Canada, we identified all TAVR referrals from April 1, 2010, to March 31, 2016. The primary outcome was the median total wait time from referral to procedure. Piecewise regression analyses were performed to assess temporal trends in TAVR wait times, before and after provincial reimbursement in September 2012. Clinical outcomes included all-cause death and heart failure hospitalizations while on the wait list. RESULTS: The study cohort included 4461 referrals, of which 50% led to a TAVR, 39% were off-listed for other reasons, and 11% remained on the wait list at the conclusion of the study. For patients who underwent a TAVR, the estimated median wait time in the postreimbursement period stabilized at 80 days and has remained unchanged. The cumulative probability of wait-list mortality and heart failure hospitalization at 80 days was ≈2% and 12%, respectively, with a relatively constant increase in events with increased wait times. CONCLUSIONS: Postreimbursement wait time has remained unchanged for patients undergoing a TAVR procedure, suggesting the increase in capacity has kept pace with the increase in demand. The current wait time of almost 3 months is associated with important morbidity and mortality, suggesting a need for greater capacity and access.
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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 | 0.000 | 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".