Comparison of Early Surgical or Transcatheter Aortic Valve Replacement Versus Conservative Management in Low‐Flow, Low‐Gradient Aortic Stenosis Using Inverse Probability of Treatment Weighting: Results From the TOPAS Prospective Observational Cohort Study
Bibliographic record
Abstract
Background No randomized comparison of early (ie, ≤3 months) aortic valve replacement (AVR) versus conservative management or of transcatheter AVR (TAVR) versus surgical AVR has been conducted in patients with low‐flow, low‐gradient (LFLG) aortic stenosis (AS). Methods and Results A total of 481 consecutive patients (75±10 years; 71% men) with LFLG AS (aortic valve area ≤0.6 cm 2 /m 2 and mean gradient <40 mm Hg), 72% with classic LFLG and 28% with paradoxical LFLG, were prospectively recruited in the multicenter TOPAS (True or Pseudo Severe Aortic Stenosis) study. True‐severe AS or pseudo‐severe AS was adjudicated by flow‐independent criteria. During follow‐up (median [IQR] 36 [11–60] months), 220 patients died. Using inverse probability of treatment weighting to address the bias of nonrandom treatment assignment, early AVR (n=272) was associated with a major overall survival benefit (hazard ratio [HR], 0.34 [95% CI, 0.24–0.50]; P <0.001). This benefit was observed in patients with true‐severe AS but also with pseudo‐severe AS (HR, 0.38 [95% CI, 0.18–0.81]; P =0.01), and in classic (HR, 0.33 [95% CI, 0.22–0.49]; P <0.001) and paradoxical LFLG AS (HR, 0.42 [95% CI, 0.20–0.92]; P =0.03). Compared with conservative management in the conventional multivariate model, trans femoral TAVR was associated with the best survival (HR, 0.23 [95% CI, 0.12–0.43]; P <0.001), followed by surgical AVR (HR, 0.36 [95% CI, 0.23–0.56]; P <0.001) and alternative‐access TAVR (HR, 0.51 [95% CI, 0.31–0.82]; P =0.007). In the inverse probability of treatment weighting model, trans femoral TAVR appeared to be superior to surgical AVR (HR [95% CI] 0.28 [0.11–0.72]; P =0.008) with regard to survival. Conclusions In this large prospective observational study of LFLG AS, early AVR appeared to confer a major survival benefit in both classic and paradoxical LFLG AS. This benefit seems to extend to the subgroup with pseudo‐severe AS. Our findings suggest that TAVR using femoral access might be the best strategy in these patients. Registration URL: https://www.clinicaltrials.gov ; Unique identifier: NCT01835028.
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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 | 0.010 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".