Early intervention or watchful waiting for asymptomatic severe aortic valve stenosis: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: The management of patients with severe but asymptomatic aortic stenosis is challenging. Evidence on early aortic valve replacement (AVR) versus symptom-driven intervention in these patients is unknown. METHODS: Electronic databases were searched, articles comparing early-AVR with conservative management for severe aortic stenosis were identified. Pooled adjusted odds ratio (OR) was computed using a random-effect model to determine all-cause and cardiovascular mortality. RESULTS: A total of eight studies consisting of 2201 patients were identified. Early-AVR was associated with lower all-cause mortality [OR 0.24, 95% confidence interval (CI) 0.13-0.45, P ≤ 0.00001] and cardiovascular mortality (OR 0.21, 95% CI 0.06-0.70, P = 0.01) compared with conservative management. The number needed to treat to prevent 1 all-cause and cardiovascular mortality was 4 and 9, respectively. The odds of all-cause mortality in a selected patient population undergoing surgical AVR (SAVR) (OR 0.16, 95% CI 0.09-0.29, P ≤ 0.00001) and SAVR or transcatheter AVR (TAVR) (OR 0.53, 95% CI 0.35-0.81, P = 0.003) were significantly lower compared with patients who are managed conservatively. A subgroup sensitivity analysis based on severe aortic stenosis (OR 0.24, 95% CI 0.11-0.52, P = 0.0004) versus very severe aortic stenosis (OR 0.20, 95% CI 0.08-0.51, P = 0.0008) also mirrored the findings of overall results. CONCLUSION: Patients with asymptomatic aortic valve stenosis have lower odds of all-cause and cardiovascular mortality when managed with early-AVR compared with conservative management. However, because of significant heterogeneity in the classification of asymptomatic patients, large scale studies are required.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.028 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 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".