Early Aortic Valve Replacement versus Watchful Waiting in Asymptomatic Severe Aortic Stenosis: A Study-Level Meta-Analysis
Bibliographic record
Abstract
Background The management of patients with asymptomatic, severe aortic stenosis (AS) is controversial. We performed a meta-analysis to examine the impact on outcomes of early aortic valve replacement (AVR) in patients with severe asymptomatic AS versus a watchful-waiting (WW) approach. Methods Databases were searched for studies published until April 2019. Main outcome of interest was death during follow-up. Results The search yielded 1,889 studies for inclusion. Of these, seven articles were analyzed and their data extracted. The total number of patients included was 3,839. The overall HR (95% CI) for death showed a statistically significant difference between the groups, with lower risk in the "early AVR" group (random effect model: HR 0.280; 95% CI 0.159–0.494, P < 0.001). There was evidence of significant statistical heterogeneity of treatment effect among the studies for death. Funnel plot analysis disclosed no asymmetry around the axis for the outcome of interest, which means that we have low risk of publication bias related to this outcome. Sensitivity analysis showed that none of the studies had a particular impact on the results. The meta-regression coefficients for the modulating factors age, male sex, presence of hypertension and presence of diabetes were significant for mortality, showing that the early intervention becomes even more protective in comparison with the conservative approach when we take these factors into consideration. Conclusion Early AVR seems to be a better approach than WW in the treatment of asymptomatic patients with severe AS, but we would still advocate a case-by-case decision-making process.
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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 | 0.017 | 0.028 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.056 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".