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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".