Unplanned Hospital Readmissions After Transcatheter Aortic Valve Replacement in the Era of New-Generation Devices
Bibliographic record
Abstract
OBJECTIVES: Unplanned hospital readmissions after transcatheter aortic valve replacement (TAVR) are frequent and have been associated with a poor prognosis. We sought to determine the trends in the incidence and causes of unplanned hospital readmission after TAVR in patients receiving new-generation devices (NGDs) vs early-generation devices (EGDs). METHODS: The study population consisted of 1802 consecutive TAVR recipients (863 EGDs and 939 NGDs). Early and late readmissions were defined as those occurring ≤30 days and >30 days to 1-year post TAVR, respectively. RESULTS: A total of 986 unplanned hospital readmissions (cardiac cause, 38.4%; non-cardiac cause, 61.6%) were recorded at a median time of 110 days (interquartile range [IQR], 37-217) post TAVR. The rates of early (12.3% vs 9.4%; P=.046) and late (39.1% vs 31.6%; P<.01) readmission were lower in the NGD population. In the NGD group, major/life-threatening periprocedural bleeding (hazard ratio [HR], 2.40, 95% confidence interval [CI], 1.06-5.42; P=.04) and estimated glomerular filtration rate (eGFR) <60 mL/min at hospital discharge (HR, 1.80; 95% CI, 1.15-2.83; P=.01) were associated with an increased risk of early readmission post TAVR. Chronic obstructive pulmonary disease (HR, 1.42; 95% CI, 1.07-1.88; P=.02), eGFR <60 mL/min (HR, 1.43; 95% CI, 1.11-1.84; P<.01), and combining antiplatelet and anticoagulation therapy (HR, 1.37; 95% CI, 1.01-1.85; P=.04) determined an increased risk of late readmission. CONCLUSIONS: TAVR recipients receiving NGDs exhibited a significant but modest reduction in unplanned hospital readmissions, with about one-third of patients still requiring rehospitalization at 1-year follow-up in the contemporary TAVR era. Non-cardiac comorbidities, periprocedural bleeding events, and intensive antithrombotic therapy determined an increased risk.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".