Early intervention or watchful waiting for asymptomatic severe aortic stenosis: a systematic review and meta-analysis
Bibliographic record
Abstract
Abstract Background The management of patients with severe but asymptomatic aortic stenosis (AS) is a challenge of daily practice. Evidence on early aortic valve replacement (AVR) versus symptom-driven approach is controversial. Methods Electronic databases were searched and articles comparing early-AVR with conservative management for severe asymptomatic AS were identified. The pooled unadjusted odds ratio (OR) was computed using a random-effects model. Results A total of 10 studies (observational=9, and randomized clinical trials=1) consisting of 5,291 patients were included. On a mean follow up duration of 4 years, the odds of early-AVR vs conservative management were in favour for early intervention in terms of cardiovascular mortality (OR 0.26, 95% CI 0.13–0.53, p=0.0002, figure-1) and all-cause mortality (OR 0.27, 95% CI: 0.17–0.41, p≤00001). The number needed to treat (NNT) by performing early AVR (either SAVR or TAVR) to prevent one death secondary to either cardiovascular or all-cause was 8 (95% CI: 6–10), and 5 (95% CI, 4.2–5.4) respectively. The odds of all-cause mortality in a selected patient population undergoing surgical AVR (SAVR) (OR 0.17, 95% CI 0.10–0.29, p≤0.00001) or transcatheter AVR (TAVR) (OR 0.45, 95% CI 0.31–0.64, p≤0.0001) were significantly lower compared to patients who were managed conservatively. A subgroup sensitivity analysis based on severe vs. very severe AS and early vs. symptom based AVR also mirrored the findings of overall results. Conclusions Patients with asymptomatic aortic valve stenosis have lower odds of all-cause and cardiovascular mortality when managed with early-AVR compared to conservative management. Figure 1 Funding Acknowledgement Type of funding source: None
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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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.029 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".