Cost of operating room time for endovascular transcatheter aortic valve replacement
Bibliographic record
Abstract
Background: Procedural efficiencies can contribute to cost reductions in transcatheter aortic valve replacement procedures (TAVR). The objective of this study is to determine operating room (OR) variable cost per minute in endovascular TAVR procedures, in a real-world hospital setting.Methods: Using Premier data from January 2015–June 2016 for patients undergoing a primary endovascular TAVR (primary ICD-9 code of 35.05, ICD-10 code of 02RF37Z, 02RF38Z, 02RF3JZ, or 02RF3KZ) procedure, the OR cost per minute was calculated for each patient by dividing the total hospital OR variable cost by the OR time (minutes).Results: Of the 4,573 patients in the cohort, the average age was 80 years, 77% were admitted electively, and the vast majority were discharged home with (30%) or without (45%) home care. Median OR time for endovascular TAVR procedures was 180 min. The trimmed mean OR cost per minute was $43.59 (SD = $28.68). When stratified by Elixhauser Risk score and Charlson comorbidity index, OR cost per minute increased with higher risk and comorbidity (p < 0.0001 and p < 0.041, respectively).Conclusions: This contemporary estimate of the real-world variable OR cost per minute provides researchers with a critical parameter to refine economic models of TAVR and aid clinical program directors in resource planning according to a priori risk and comorbidity.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".