Factors associated with length of stay following trans-catheter aortic valve replacement - a multicenter study
Bibliographic record
Abstract
BACKGROUND: Most patients undergoing Transcatheter aortic valve implantation (TAVR) are elderly with significant co-morbidities and there is limited information available regarding factors that influence length of stay (LOS) post-procedure. The aim of this study was to identify the patient, and procedural factors that affect post-TAVR LOS using a contemporary multinational registry. METHODS: We conducted a retrospective cohort study, with patients recruited from three high volume tertiary institutions. The primary outcome was the LOS post-TAVR procedure. We examined patient and procedural factors in a cause-specific Cox multivariable regression model to elucidate their effect on LOS, accounting for the competing risk of post-procedural death. Hazard ratios (HR) greater than 1 indicate a shorter LOS, while HRs less than 1 indicate a longer LOS. RESULTS: The cohort consisted of 809 patients. Patient factors associated with longer LOS were older age, prior atrial fibrillation, and greater patient urgency. Patient factors associated with shorter LOS were lower NYHA class, higher ejection fraction and higher mean aortic valve gradients. Procedural characteristics associated with shorter LOS were conscious sedation (HR = 1.19, 95% CI 1.06-1.35, p = 0.004). Transapical access was associated with prolonged LOS (HR = 0.49, 95% CI 0.41-0.58, p < 0.001). CONCLUSION: This multicenter study identified potentially modifiable patient and procedural factors associated with a prolonged LOS. Future research is needed to determine if interventions focused on these factors will translate to a shorter 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 | 0.002 | 0.003 |
| 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 | 0.000 | 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".