Impact of procedural capacity on transcatheter aortic valve replacement wait times and outcomes: a study of regional variation in Ontario, Canada
Bibliographic record
Abstract
BACKGROUND: There has been rapid growth in the demand for transcatheter aortic valve replacement (TAVR), which has the potential to overwhelm current capacity. This imbalance between demand and capacity may lead to prolonged wait times, and subsequent adverse outcomes while patients are on the waitlist. We sought to understand the relationship between regional differences in capacity, TAVR wait times and morbidity/mortality on the waitlist. METHODS AND RESULTS: We modelled the effect of TAVR capacity, defined as the number of TAVR procedures per million residents/region, on the hazard of having a TAVR in Ontario from April 2012 to March 2017. Our primary outcome was the time from referral to a TAVR procedure or other off-list reasons on the waitlist/end of the observation period as measured in days. Clinical outcomes of interest were all-cause mortality, all-cause hospitalisations or heart failure-related hospitalisations while on the waitlist for TAVR. There was an almost fourfold difference in TAVR capacity across the 14 regions in Ontario, ranging from 31.5 to 119.5 TAVR procedures per million residents. The relationship between TAVR capacity and wait times was complex and non-linear. In general, increased capacity was associated with shorter wait times (p<0.001), reduced mortality (HR 0.94; p=0.08) and all-cause hospitalisations (p=0.009). CONCLUSIONS: The results of the present study have important policy implications, suggesting that there is a need to improve TAVR capacity, as well as develop wait-time strategies to triage patients, in order to decrease wait times and mitigate the hazard of adverse patient outcomes while on the waitlist.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".