Abstract 031: Determination of Drivers of Cost Variation in Aortic Valve Replacement; a Case Costing Approach
Bibliographic record
Abstract
Objective: We sought to establish the cost of an aortic valve replacement (AVR) in a single, tertiary cardiac centre through a detailed case costing approach, and to identify the cost drivers of AVR. Methods: Intra-operative consumables were collected directly from the operating room during the procedure while indirect costs were calculated after the procedure had been finished using time based calculations and straight line depreciation. Costs were divided into four departments: Pharmacy costs, including all drugs and fibrinogen, Perfusion costs, including all required blood products and cardiopulmonary bypass consumables excluding cannulas, Cardiac Surgery costs, including valves, cannulas and catheters opened for each case, and Anesthesia costs, including rotational thromboelastometry (ROTEM) test, central monitoring cannulas, endotracheal tubes and monitoring disposables. Results: We were able to calculate costs for 24 patients undergoing isolated AVR between June and August, 2016.. The average intra-operating room per-patient costs were $8,629.70 with a standard deviation of $1,623.74. Backwards-stepwise linear regression showed that perfusion (beta coefficient 0.332; p = 0.002), pharmacy (beta coefficient 0.425; p < 0.001) and cardiac surgery (beta coefficient 0.587; p < 0.001) were all significant sources of cost variability between cases. In Pharmacy, the largest driver of cost was the use of fibrinogen. In Perfusion, the largest driver of cost was blood product use. . In Cardiac Surgery, the largest driver of cost was the selection of prosthesis by the surgeon. Although anesthesia costs did not significantly affect cost variation, interestingly there was a negative beta coefficient (beta coefficient -.063. Where the ROTEM test was the single largest cost contributor to anesthesia costs this suggests the ROTEM may be ultimately cost savings. Conclusion: We demonstrate that there is significant cost variation in AVR, and that the major drivers are the use of fibrinogen; the use of blood products; and the choice of valve prosthesis. These costs are arguably both patient and practitioner driven. Interestingly the use of diagnostic ROTEM tests may mitigate costs through more directed strategies toward controlling bleeding. Identification of drivers of cost variation may allow for future cost reduction.
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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 | 0.008 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".