Association Between Wait Time for Transcatheter Aortic Valve Replacement and Early Postprocedural Outcomes
Bibliographic record
Abstract
Background Rapid growth in transcatheter aortic valve replacement ( TAVR ) demand has translated to inadequate access, reflected by prolonged wait times. Increasing wait times are associated with important adverse outcomes while on the wait-list; however, it is unknown if prolonged wait times influence postprocedural outcomes. Our objective was to determine the association between TAVR wait times and postprocedural outcomes. Methods and Results In this population-based study in Ontario, Canada, we identified all TAVR procedures between April 1, 2010, and March 31, 2016. Wait time was defined as the number of days between initial referral and the procedure. Primary outcomes of interest were 30-day all-cause mortality and all-cause readmission. Multivariable regression models incorporated wait time as a nonlinear variable, using cubic splines. The study cohort included 2170 TAVR procedures, of which 1741 cases were elective and 429 were urgent. There was a significant, nonlinear relationship between TAVR wait time and post- TAVR 30-day mortality, as well as 30-day readmission. We observed an increased hazard associated with shorter wait times that diminished as wait times increased. This statistically significant nonlinear relationship was seen in the unadjusted model as well as after adjusting for clinical variables. However, after adjusting for case urgency status, there was no relationship between wait times and postprocedural outcomes. In sensitivity analyses restricted to either only elective or only urgent cases, there was no relationship between wait times and postprocedural outcomes. Conclusions Wait time has a complex relationship with postprocedural outcomes that is mediated entirely by urgency status. This suggests that further research should elucidate factors that predict hospitalization requiring urgent TAVR while on the wait list.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".