Utilization of Advanced Cardiovascular Therapies in the United States and Canada
Bibliographic record
Abstract
Background: Endovascular aortic aneurysm repair (EVAR), left ventricular assist device (LVAD), and transcatheter aortic valve replacement (TAVR) are expensive cardiovascular technologies with potential to benefit large numbers of patients. There are few population-based studies comparing utilization between countries. Our objective was to compare patient characteristics and utilization patterns of EVAR, LVAD, and TAVR in Ontario, Canada, and New York State, United States. Methods and Results: We performed a retrospective cohort study using administrative data to identify all adults who received EVAR, LVAD, or TAVR in Ontario and New York between 2012 and 2015. We compared socio-demographics of EVAR, LVAD, and TAVR recipients in Ontario and New York. We compared standardized utilization rates between jurisdictions for each procedure. We identified 3295 EVAR recipients from Ontario and 6236 from New York (mean age 74.6 versus 74.5 years; P =0.61): 136 LVAD recipients from Ontario and 686 from New York (age, 57.4 versus 57.7 years; P =0.80): 1708 TAVR recipients from Ontario and 4838 from New York (age, 83.1 versus 83.1; P =1.0). A significantly smaller percentage of EVAR and TAVR recipients in Ontario were female compared to New York (EVAR, 15.8% versus 22.1% female; P <0.001; TAVR, 45.9% versus 51.8%; P <0.001), but for LVAD the percentage female was similar (21.3% versus 20.8%; P =0.99). Utilization was significantly higher in New York for all procedures: EVAR (12.8 procedures per-100 000 adults per-year in Ontario, 20.2 in New York; P <0.001); LVAD (0.3 in Ontario versus 1.3 in New York; P <0.001); and TAVR (6.6 in Ontario, 14.3 in New York; P <0.001). Higher utilization of EVAR and TAVR in New York relative to Ontario increased substantially with increasing age. Conclusions: We observed significantly higher utilization of EVAR, LVAD, and TAVR in New York compared to Ontario. Our results highlight important differences in how 2 different countries are using advanced cardiovascular therapies.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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".