Short Length of Stay After Elective Transfemoral Transcatheter Aortic Valve Replacement is Not Associated With Increased Early or Late Readmission Risk
Bibliographic record
Abstract
Background Elderly patients undergoing transcatheter aortic valve replacement ( TAVR ) are at risk of hospital readmission postprocedure. It is not known whether the index hospital length of stay and, specifically, early discharge post‐ TAVR is associated with an increased risk of readmission. We hypothesized a nonlinear relationship whereby both short and long lengths of stay were associated with increased readmission risk. Methods and Results We performed a retrospective multicenter cohort analysis of patients undergoing elective transfemoral TAVR and surviving to discharge between January 2007 and March 2014. The exposure variable was hospital length of stay measured from the procedure date to the date of discharge and modeled as a continuous variable in a multivariable cause‐specific Cox regression. Main outcome measures were 30‐day and 1‐year all‐cause readmissions. The study population consisted of 709 patients with a median length of stay of 6 days (interquartile range, 4–8). At 30‐days and 1‐year, 13.5% and 44.0% of patients were readmitted, respectively. Although post‐ TAVR length of stay was not associated with 30‐day all‐cause readmissions ( P =0.925), there existed a significant association with 1‐year readmission ( P =0.010) after adjustment for baseline clinical variables. The association between post‐ TAVR length of stay and 1‐year readmission was linear ( P =0.549 for nonlinearity) with no evidence supporting an increased readmission risk for shorter length of stays. Conclusions Among elderly survivors of elective transfemoral TAVR , a short postprocedural length of stay was not associated with an increased risk readmission within 30 days or 1 year. However, the risk of 1‐year readmission increased with longer post‐ TAVR lengths of stay.
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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 | 0.006 |
| 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 | 0.001 | 0.000 |
| 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".