An exploratory analysis of factors associated with length of stay following transcatheter aortic valve implantation
Bibliographic record
Abstract
Background: Transcatheter aortic valve implantation (TAVI) is a minimally invasive treatment option for higher surgical risk patients with severe symptomatic aortic stenosis (AS) and significant co-existing conditions which may preclude them from surgical valve replacement. Patient characteristics and wait time have been shown to impact length of stay (LOS) in individuals with heart disease; however, these variables have not been extensively evaluated in the TAVI population. Objective: The purpose of this study was to explore factors associated with post-TAVI recovery, as measured by hospital LOS. Method: A retrospective chart review of consecutive patients who underwent TAVI in Vancouver, British Columbia between January 01, 2013 to December 31, 2014 was conducted. Study variables included patient characteristics and wait time. The outcome variable, LOS, was defined as time, in days, from procedure to hospital discharge. Univariate and bivariate analyses were used to select moderately correlated variables for multivariate regression analysis. Results: The study sample consisted of 257 patients, with a mean age of 81.4 years. The median wait time from acceptance to procedure was 36 days, while the median LOS was 3.0 days. Bivariate analysis showed age, living situation, symptom severity, prior surgical aortic valve replacement (SAVR), and prior balloon aortic valvuloplasty (BAV) to be statistically significantly associated with post-TAVI stay in-hospital. The multivariate model revealed that relative to having a LOS of 1 to 2 days, patients who had previously undergone a BAV were 10.7 times more likely to stay 5 days or more (CI [1.16, 98.1]) compared with patients who had not undergone a BAV. No other baseline factors were found to be statistically predictive of prolonged LOS, although odds ratios suggest patients with lower symptom severity and patients who underwent valve-in-valve TAVI were less likely to experience a longer LOS. The model also showed that patients 75 to 79 years of age, with NYHA class III or IV symptoms, and no prior history of an AVR were more likely to follow a standard course of recovery, staying 3 to 4 days, while patients who had a valve-in-valve procedure were more likely to stay 1 to 2 days.
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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 | 0.001 | 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) | 0.002 | 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".