Temporal Trends and Drivers of Heart Team Utilization in Transcatheter Aortic Valve Replacement: A Population‐Based Study in Ontario, Canada
Bibliographic record
Abstract
Background The multidisciplinary Heart Team (HT) is recommended for management decisions for transcatheter aortic valve replacement (TAVR) candidates, and during TAVR procedures. Empiric evidence to support these recommendations is limited. We aimed to explore temporal trends, drivers, and outcomes associated with HT utilization. Methods and Results TAVR candidates were identified in Ontario, Canada, from April 1, 2012 to March 31, 2019. The HT was defined as having a billing code for both a cardiologist and cardiac surgeon during the referral period. The procedural team was defined as a billing code during the TAVR procedure. Hierarchical logistical models were used to determine the drivers of HT. Median odds ratios were calculated to quantify the degree of variation among hospitals. Of 10 412 patients referred for TAVR consideration, 5489 (52.7%) patients underwent a HT during the referral period, with substantial range between hospitals (median odds ratio of 1.78). Utilization of a HT for TAVR referrals declined from 69.9% to 41.1% over the years of the study. Patient characteristics such as older age, frailty and dementia, and hospital characteristics including TAVR program size, were found associated with lower HT utilization. In TAVR procedures, the procedural team included both cardiologists and cardiac surgeons in 94.9% of cases, with minimal variation over time or between hospitals. Conclusions There has been substantial decline in HT utilization for TAVR candidates over time. In addition, maturity of TAVR programs was associated with lower HT utilization.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".