Incremental cost and length of stay associated with postprocedure delirium in transcatheter and surgical aortic valve replacement patients in the United States
Bibliographic record
Abstract
OBJECTIVES: To explore the impact of post-procedure delirium on resource utilization following transcatheter and surgical aortic valve replacement (TAVR and SAVR, respectively). BACKGROUND: Postprocedure delirium is associated with worse long-term survival after TAVR and SAVR. However, its effect on resource utilization has been understudied. METHODS: Using the 2015 Medicare Provider Analysis and Review File (MedPAR), we retrospectively analyzed elderly (≥80 years) Medicare beneficiaries receiving either SAVR or endovascular TAVR in the United States. Multivariate regression models estimating hospitalization cost and length of stay (LoS) were adjusted for patient demographics, comorbidities, and nondelirium complications. RESULTS: A total of 21,088 discharges were available for analysis (12,114 TAVR and 8,974 SAVR). TAVR patients were older (87 ± 3.8 vs. 84 ± 2.7 years; P < 0.001) with a higher comorbidity burden (Charlson index 3.0 ± 1.8 vs. 2.1 ± 1.7; P < 0.0001). Despite this, fewer TAVR patients (1.6%) experienced postoperative delirium during the index hospitalization compared to surgical patients (3.6%; P < 0.0001). Delirium was associated with a 4.16 [3.51-4.81] day longer hospital LoS and $15,592 ($12,849-$18,334) higher incremental hospitalization cost. When stratified by treatment approach, the adjusted incremental cost of delirium was +$13,862 ($9,431-$18,292) with TAVR and +$16,656 ($13,177-$20,136) with SAVR with an additional hospital LoS of +3.39 (2.34-4.43) days and +4.63 (3.81-5.45) days for TAVR and SAVR, respectively. CONCLUSIONS: Postprocedure delirium is associated with significantly increased hospitalization costs and LoS following AVR. TAVR was associated with a lower postoperative delirium rate compared to SAVR. Post-TAVR delirium may be associated with less resource consumption than post-SAVR delirium.
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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 | 0.008 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".