Predictors of cumulative cost for patients with severe aortic stenosis referred for surgical or transcatheter aortic valve replacement: a population-based study in Ontario, Canada
Bibliographic record
Abstract
AIMS: Transcatheter aortic valve replacement (TAVR) as an alternative to surgical aortic valve replacement (SAVR) has transformed severe aortic stenosis (AS) management. Our aim was understand AS cost drivers from referral to 1-year post-procedure. METHODS AND RESULTS: We identified patients referred for either TAVR/SAVR between 1 April 2015 and 31 March 2018, with follow-up until 31 March 2019 in Ontario, Canada. We stratified costs into (i) a referral phase, (ii) a procedural phase from the procedure date to 60 days post-procedure, and (iii) post-procedure phase from 61 days to 1 year. Multivariable regression modelling using generalized linear models with a log link gamma distribution was used to identify cost drivers in each phase. The study cohort included 12 086 AS patients; 4832 were referred for TAVR and 7254 were referred for SAVR. The median cost for TAVR was higher than SAVR in the referral ($3593 vs. $2944) and post-procedural ($5938 vs. $3257) phases. In contrast, for the procedural phase, SAVR had a median cost of $29 756 vs. $27 907 for TAVR. Predictors of high cost in the referral phase were longer wait-time, and an urgent in-hospital procedure. In the procedural phase, procedural complications were the major drivers of higher cost. In the post-procedural phase, patient co-morbidities were the major drivers, specifically dialysis, liver disease, cancer, peripheral vascular disease, and diabetes mellitus. CONCLUSION: We identified distinct patterns of cost accumulation and modifiable drivers for SAVR compared with TAVR; these drivers may guide clinical and health policy decisions to make AS care more efficient.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".