Cost-Effectiveness of Thoracotomy Approach for the Implantation of a Centrifugal Left Ventricular Assist Device
Bibliographic record
Abstract
This study reports the first analysis regarding cost-effectiveness of left ventricular assist device (LVAD) implantation via thoracotomy. Cost-effectiveness of LVADs implanted via the traditional surgical approach of sternotomy has been improved through the years because of technological advances, along with understanding the importance of patient selection and postimplant management have on positively affecting outcomes. Given the positive clinical outcomes of the thoracotomy approach, we seek to study the cost-effectiveness of a centrifugal LVAD via this less invasive approach. We developed a Markov model. Survival and quality of life inputs (QALY) for the LVAD arm were based on data from the LATERAL clinical trial. For the Medical Management arm, survival was derived from the Seattle Heart Failure Model. The heart transplant probability was derived from INTERMACS. Survival after heart transplantation used International Society for Heart and Lung Transplantation data. Cost inputs were calculated based on Medicare data and past literature. The incremental cost-effectiveness ratio was found to be $64,632 per quality adjusted life year and $57,891 per life year in the bridge to transplant indication. These results demonstrate further improvement in the overall cost-effectiveness of LVAD therapy and confirm implantation of LVADs via a less invasive approach as being cost-effective.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".