Outcomes of patients with calcific aortic valve disease according to the extent of cardiac damage
Bibliographic record
Abstract
Abstract Background A staging system for aortic stenosis (AS) based upon the extent of cardiac damage has been proposed to better stratify risk and evaluate the benefit of aortic valve intervention (AVI), especially in those with moderate AS. We sought to evaluate the prognostic value of this staging system. Methods Data from initial clinically indicated echocardiograms performed between 2010 and 2018 in patients >18 years of age were extracted and linked to national outcome data. The combined primary outcome was mortality or hospitalization with heart failure. Results Amongst 24,699 patients, 513 and 920 had moderate and mild AS, respectively. In moderate AS, Stage 0 cardiac damage was present in 9.4%, Stage 1 in 53.7%, Stage 2 in 31.1%, Stage 3 in 3.2%, and Stage 4 in 2.6%. In mild AS, rates were 11.5%, 57.8%, 25.0%, 2.6%, and 3.0% for each consecutive stage. Increasing stage was associated with increased risk of the primary outcome in both moderate (HR 1.62/stage) and mild AS (HR 1.93/stage). After censoring at the time of AVI, increasing stage was also associated with mortality in moderate (HR 1.97/stage) and mild AS (HR 2.06/stage). Conclusion Stage of cardiac damage predicts prognosis in both moderate and mild AS to a similar extent. Outcomes may therefore not be fully related to the haemodynamic consequences of valve disease, and hence may not be entirely reversible after valve intervention. Revised management algorithms focusing on earlier intervention and novel treatment strategies targeting cardiac damage are needed to improve clinical outcomes in patients with AS.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".