Management of patients with severe aortic stenosis in the TAVI-era: how recent recommendations are translated into clinical practice
Bibliographic record
Abstract
OBJECTIVE: Approximately 3.4% of adults aged >75 years suffer from aortic stenosis (AS). Guideline indications for aortic valve replacement (AVR) distinguish between patients with symptomatic and asymptomatic severe AS. The present analysis aims to assess contemporary practice in the treatment of severe AS across Europe and identify characteristics associated with treatment decisions, namely denial of AVR in symptomatic patients and assignment of asymptomatic patients to AVR. METHODS: Participants of the prospective, multinational IMPULSE database of patients with severe AS were grouped according to AS symptoms, and stratified into subgroups based on assignment to/denial of AVR. RESULTS: Of 1608 symptomatic patients, 23.8% did not undergo AVR and underwent medical treatment. Denial was independently associated with multiple factors, including severe frailty (p=0.024); mitral (p=0.002) or tricuspid (p=0.004) regurgitation grade III/IV, and the presence of renal impairment (p=0.017). Of 392 asymptomatic patients, 86.5% had no prespecified indication for AVR. Regardless, 36.3% were assigned to valve replacement. Those with an indexed aortic valve area (AVA; p=0.045) or left ventricular ejection fraction (LVEF; p<0.001) below the study median; or with a left ventricular end systolic diameter above the study median (p=0.007) were more likely to be assigned to AVR. CONCLUSIONS: There may be considerable discrepancies between guideline-based recommendations and clinical practice decision-making in the treatment of AS. It appears that guidelines may not fully capture the complete clinical spectrum of patients with AS. Thus, there is a need to find ways to increase their acceptance and the rate of adoption.
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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 | 0.023 | 0.098 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".