Rates and predictors of hospital readmission after transcatheter aortic valve implantation
Bibliographic record
Abstract
AIMS: To analyse reasons, timing and predictors of hospital readmissions after transcatheter aortic valve implantation (TAVI). METHODS AND RESULTS: Patients included in the Bern TAVI Registry between August 2007 and June 2014 were analysed. Fine and Gray competing risk regression was used to identify factors predictive of hospital readmission within 1 year after TAVI with bootstrap analysis for internal validation. Of 868 patients alive at discharge, 221 (25.4%) were readmitted within 1 year. Compared with patients not requiring readmission, those with at least one readmission more frequently were male and more often had atrial fibrillation and higher creatinine values (P < 0.05 for all cases). For overall 308 readmissions, cardiovascular causes accounted for 46.1% with heart failure as the most frequent indication; non-cardiovascular readmissions occurred for surgery (11.7%), gastrointestinal disorders (9.7%), malignancy (4.9%), respiratory diseases (4.6%) and chronic kidney failure (2.6%). Male gender (subhazard ratio, SHR, 1.33, 95% confidence intervals, CI, 1.02-1.73, P = 0.035) and stage 3 kidney injury (SHR 2.04, 95% CI 1.12-3.71, P = 0.021) were found independent risk factors for any hospital readmission, whereas previous myocardial infarction (SHR 1.88, 95% CI 1.22-2.90, P = 0.004) and in-hospital life-threatening bleeding (SHR 2.18, 95%CI 1.24-3.85, P = 0.007) were associated with cardiovascular readmissions. The event rate for mortality was significantly increased after readmissions for any cause (RR 4.29, 95% CI 2.86-6.42, P < 0.001). CONCLUSION: Hospital readmission was observed in one out of four patients during the first year after TAVI and was associated with a significant increase in mortality.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".