Midterm Survival of Low‐Risk Patients Treated With Transcatheter Versus Surgical Aortic Valve Replacement: Meta‐Analysis of Reconstructed Time‐to‐Event Data
Bibliographic record
Abstract
Background We performed a meta‐analysis of reconstructed time‐to‐event data from randomized controlled trials (RCTs) and propensity‐score matched (PSM) studies comparing transcatheter versus surgical aortic valve replacement (TAVR versus SAVR) to evaluate midterm outcomes in patients considered low risk for SAVR. Methods and Results Study‐level meta‐analysis of reconstructed time‐to‐event data from Kaplan–Meier curves of RCTs and PSM studies published by December 31, 2022 was conducted. Eight studies (3 RCTs, 5 PSM studies) met our eligibility criteria and included 5444 patients; 2639 patients underwent TAVR, and 2805 patients underwent SAVR. TAVR showed a higher risk of all‐cause mortality at 8 years of follow‐up (hazard ratio [HR] 1.22, [95% CI, 1.03–1.43], P =0.018). Up to 2 years of follow‐up, TAVR was not inferior to SAVR (HR, 1.08 [95% CI, 0.89–1.31], P =0.448); however, we observed a statistically significant difference after 2 years with higher mortality with TAVR (HR, 1.51 [95% CI, 1.14–2.00]; P =0.004). This difference was driven by PSM studies; our sensitivity analysis showed a statistically significant difference between TAVR and SAVR when we included only PSM studies (HR, 1.41 [95% CI, 1.16–1.72], P =0.001) but no statistically significant difference when we included only RCTs (HR, 0.89 [95% CI, 0.69–1.16], P =0.398). Conclusions In comparison with TAVR, SAVR appeared to be associated with improved survival beyond 2 years in low‐risk patients. However, the survival benefit of SAVR was observed only in PSM studies and not in RCTs. The addition of data from ongoing RCTs as well as longer follow‐up in previous RCTs will help to confirm if there is a difference in mid‐ and long‐term survival between TAVR versus SAVR in the low‐risk population.
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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 | 0.020 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.041 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".