Gender-Specific Outcomes in TAVI with Self-Expandable Valves: Insights from a Large Real-World Registry
Bibliographic record
Abstract
Background/Objectives: Aortic stenosis (AS) is the most prevalent valvular heart disease in developed countries and imposes an increasing burden on aging populations. Although transcatheter aortic valve implantation (TAVI) has transformed the treatment of severe AS, current guidelines do not differentiate management based on gender. This study aimed to investigate gender-based differences in procedural complications and one-year clinical outcomes in patients treated with next-generation self-expandable TAVI devices. Methods: This retrospective, multicenter international registry included 3862 consecutive patients who received either the ACURATE neo or Evolut R/Pro valve. Patients were stratified by gender; propensity score matching (PSM) adjusted for baseline differences. The primary endpoint was a composite of all-cause mortality or stroke at one year. Secondary endpoints included major vascular complications, major or life-threatening bleeding and acute kidney injury (AKI). Results: Of 3353 patients included (64.5% female), women were older (82.3 ± 5.6 vs. 81.1 ± 6.2 years, p < 0.001) and had higher STS scores (5.2 ± 3.9 vs. 4.5 ± 3.4%, p < 0.001). In the unmatched population, major vascular complications occurred in 7.7% of females versus 4.1% of males (p < 0.001), life-threatening bleeding in 2.8% vs. 1.4% (p = 0.016) and AKI in 8.5% vs. 5.7% (p = 0.009). After PSM, the primary endpoint was more frequent in females (9.4% vs. 6.0%, p = 0.014), largely driven by stroke (2.8% vs. 1.2%, p = 0.024), while overall mortality was similar (11.3% vs. 9.5%, p = 0.264). Conclusions: Despite comparable long-term survival, female patients undergoing TAVI with self-expandable valves experience higher rates of procedural complications, notably stroke and major vascular events. These findings underscore the need for tailored procedural strategies to improve outcomes in female patients.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".