Association between transitional care factors and hospital readmission after transcatheter aortic valve replacement: a retrospective observational cohort study
Bibliographic record
Abstract
BACKGROUND: Studies have shown that patients who undergo trans-catheter aortic valve replacement (TAVR) have high rates of hospital readmission. Our objectives were to identify the causes of readmission after TAVR, determine whether transitional care factors were associated with a reduction in readmission and to identify other predictors that could be used to target quality improvement efforts. METHODS: We conducted a chart abstraction study that included all patients who underwent TAVR in Ontario, Canada between 2007 and 2013 and survived to hospital discharge. These data were linked to provincial administrative databases. The association between transitional care factors (home care, rehabilitation, family physician and cardiologist follow-up) and 1-year hospital readmission was examined using a time-to-event analysis. Cause-specific hazards models were used to account for the competing risk of death. RESULTS: There were 937 patients in the cohort and the rate of readmission at 1-year was 49%. The most common causes of readmission were heart failure and bleeding. Rehabilitation (HR 1.34, 95% CI 1.11-1.62; p = 0.002) and cardiologist follow-up (HR 1.41, 95% CI 1.14-1.75; p = 0.002) were both associated with higher readmission rates. While, home care (HR 1.18, 95% CI 0.96-1.44; p = 0.12) and family physician follow-up (HR 1.04, 95% CI 0.85-1.28; p = 0.71) were not associated with readmission. CONCLUSION: Readmission post TAVR is common; however, we did not identify any transitional care factors associated with reductions in hospital readmission. This suggests ongoing research is required to identify targets for improvement in post-procedural care.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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".