Exploring the Reduction in Hospitalization Costs Associated with Next-Day Discharge following Transfemoral Transcatheter Aortic Valve Replacement in the United States
Bibliographic record
Abstract
BackgroundIndex hospital costs for transcatheter aortic valve replacement (TAVR) remain high. Next-day discharge (NDD) is a safe and feasible strategy in select patients. We sought to explore cost savings associated with NDD TAVR.MethodsWe conducted a retrospective observation cohort study of all fee-for-service Medicare beneficiaries who underwent elective, uncomplicated, transfemoral TAVR in 2016. We employed a cross-sectional regression analysis to estimate risk-adjusted hospital costs savings of NDD relative to longer length of stay (LOS), and logistic regression to determine differences in direct home discharge and readmission.ResultsAmong 14,765 patients (59.2% of all TAVR), 2,169 (14.7%) were identified as NDD. They were younger (81.3 vs. 82.2, p < 0.01), more likely to be male (63% vs. 52%, p < 0.01), and had lower Charlson Comorbidity Index scores (2.73 vs. 2.98, p < 0.01). The adjusted cost for NDD was $7,499 lower compared to non-NDD (p < 0.001), and $5,188 when controlling for hospital fixed effects (p < 0.001); estimated total cost savings ranged from $6,522,183 to $16,265,331. NDD was associated with higher rates of discharged home (83% vs. 59%, p < 0.0001) without home health (16% vs. 28%, p < 0.0001), and lower incidence of readmission at 30 days [OR 0.72 (0.61–0.86), p = 0.0003], 60 days [0.71 (0.61–0.82), p < 0.0001] and 90 days [0.74 (0.65–0.85), p < 0.0001] than non-NDD.ConclusionsThere is significant heterogeneity in LOS after TAVR. Cost savings could be achieved with the implementation of clinical pathways and other strategies. Future research is needed to fully capture the multiple effects associated with LOS.
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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 | 0.004 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".