Drivers of healthcare costs associated with the episode of care for surgical aortic valve replacement versus transcatheter aortic valve implantation
Bibliographic record
Abstract
OBJECTIVE: Transcatheter aortic valve implantation (TAVI) is generally more expensive than surgical aortic valve replacement (SAVR) due to the high cost of the device. Our objective was to understand the patient and procedural drivers of cumulative healthcare costs during the index hospitalisation for these procedures. DESIGN: All patients undergoing TAVI, isolated SAVR or combined SAVR+coronary artery bypass grafting (CABG) at 7 hospitals in Ontario, Canada were identified during the fiscal year 2012-2013. Data were obtained from a prospective registry. Cumulative healthcare costs during the episode of care were determined using microcosting. To identify drivers of healthcare costs, multivariable hierarchical generalised linear models with a logarithmic link and γ distribution were developed for TAVI, SAVR and SAVR+CABG separately. RESULTS: Our cohort consisted of 1310 patients with aortic stenosis, of whom 585 underwent isolated SAVR, 518 had SAVR+CABG and 207 underwent TAVI. The median costs for the index hospitalisation for isolated SAVR were $21 811 (IQR $18 148-$30 498), while those for SAVR+CABG were $27 256 (IQR $21 741-$39 000), compared with $42 742 (IQR $37 295-$56 196) for TAVI. For SAVR, the major patient-level drivers of costs were age >75 years, renal dysfunction and active endocarditis. For TAVI, chronic lung disease was a major patient-level driver. Procedural drivers of cost for TAVI included a non-transfemoral approach. A prolonged intensive care unit stay was associated with increased costs for all procedures. CONCLUSIONS: We found wide variation in healthcare costs for SAVR compared with TAVI, with different patient-level drivers as well as potentially modifiable procedural factors. These highlight areas of further study to optimise healthcare delivery.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".