Transcatheter Aortic Valve Replacement After Heart Failure Hospitalization: Too Little, too Late?
Bibliographic record
Abstract
This article refers to 'Impact of recent heart failure hospitalization on clinical outcomes in patients with severe aortic stenosis undergoing transcatheter aortic valve replacement: an analysis from the PARTNER 2 trial and registries' by S. Chen et al., published in this issue on pages 1866-1874.Among adults with severe aortic stenosis (AS) who are scheduled to undergo surgical (SAVR) or transcatheter aortic valve replacement (TAVR), a substantial proportion is hospitalized with heart failure (HF) in the weeks preceding the procedure.1,2 In Ontario, Canada, 12% of patients referred for TAVR were hospitalized for HF within a median of 80 days wait time. 1 Similarly, in the FinnValve registry, 11% of patients waiting for TAVR or SAVR developed acute HF within 60 days prior to the procedure.2 Patients with AS who develop HF have suboptimal outcomes after TAVR or SAVR compared to their non-HF counterparts.[3][4][5] Acute HF at presentation in TAVR or SAVR candidates is associated with even worse prognosis despite correction of AS. 2,6,7 In the FinnValve registry, recent acute HF was associated with lower 30-day and 5-year survival, more post-procedural complications (including acute renal failure), and higher rates of healthcare resource utilization in both the SAVR and TAVR groups. 2 Similarly, in a retrospective cohort from France, TAVR recipients presenting with acute HF had more post-procedural acute renal failure and requirement for inotropic support, and significantly higher mortality both at 30 days (10% vs. 5%, P = 0.02) and at 1 year (27% vs. 15%, P < 0.001) compared to stable patients.7 Importantly, acute HF was widely prevalent (>50%) in patients with AS on the TAVR waitlist who were untreated.7 This finding is consistent with contemporary reports of high rates of hospitalization for HF among untreated patients with AS. 8 In this issue of the Journal, Chen and colleagues provide additional insights into the impact of acute HF on the clinical outcomes of intermediate-to-high or prohibitive surgical risk patients undergoing TAVR for severe symptomatic AS. 9 The investigators The opinions expressed in this article are not necessarily those of the Editors of the European Journal of Heart Failure or of the European Society of Cardiology.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".