Cost-Effectiveness of Ventricular Assist Device Therapy as a Bridge to Transplantation Compared With Nonbridged Cardiac Recipients
Bibliographic record
Abstract
BACKGROUND: Current available treatment options for advanced heart failure include heart transplantation and ventricular assist device (VAD) therapy. This project aimed to evaluate the cost-effectiveness of a bridge-to-transplantation (BTT)-VAD approach relative to direct heart transplantation in transplant-eligible patients. METHODS AND RESULTS: A Markov model was used to evaluate survival benefits and costs for BTT-VAD versus nonbridged heart transplant recipients. Three different scenarios were considered according to severity of patients' baseline hemodynamic status (high, medium, and low risk). Results are presented in terms of survival, costs, and cost-effectiveness ratio. Sensitivity analyses were used to analyze uncertainty in model estimates. Over a 20-year time horizon, BTT-VAD therapy increased survival at an increased cost relative to nonbridged heart transplant recipients: $100 841more in costs and 1.19 increased life years (LYs) in high-risk patients ($84 964/LY), $112 779 more in costs and 1.14 more LYs ($99 039/LY) in medium-risk patients, and an additional cost of $144 334 and incremental clinical benefit of 1.21 more LYs ($119 574/LY) in low-risk patients. The sensitivity analysis estimated a 59%, 54%, and 43% chance of BTT-VAD therapy being cost-effective for high-, medium-, and low-risk patients at a willingness-to-pay level of $100 000/LY. Subgroup analyses indicated that risk of post-VAD and transplantation complications, waiting time, renal dysfunction, and patient age substantially affected the cost-effectiveness ratio. CONCLUSIONS: BTT-VAD therapy is associated with improved survival and increased costs. On the basis of commonly accepted willingness-to-pay thresholds, BTT-VAD therapy is likely to be cost-effective relative to nonbridged heart transplantation in specific circumstances.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".