Sex‐Associated Disparities in Surgical and Percutaneous Management of Aortic Stenosis With Severe Features: Retrospective Analysis From the National Readmission Database
Bibliographic record
Abstract
Background Referral for valve intervention for severe aortic stenosis (AS) may exhibit sex‐associated disparities independent of the growth of transcatheter interventions. This study aimed to determine whether there were sex‐associated differences in the use of aortic valve replacement (AVR), either surgical or transcatheter, in patients with aortic stenosis and severe features from a national cohort of patients. Methods Using the National Readmission Database, all patients with an index diagnosis of AS between January 2015 and December 2019 were included and stratified by their 90‐day readmission status and sex. AS with severe features was defined as the combination of primary‐ or secondary‐coded diagnosis of AS in combination with heart failure, syncope, angina pectoris, cardiac arrest, or cardiogenic shock. A 1:1 nested case–control matching was performed to account for competing risk. The main investigated outcome was the sex‐associated rate of AVR in the 90 days after index hospitalization. Results A total of 31 712 matched weighted discharges were included in the analysis, 16 597 men (52.3%) and 15 116 women (47.7%). At 90 days, the rate of AVR was significantly lower in women (45.7% versus 53.6%, P <0.001) with significant difference for both surgical ( P <0.001) and transcatheter ( P =0.010) interventions. After multivariable adjustment, these differences persisted with women significantly less likely to receive AVR (adjusted odds ratio [aOR], 0.67 [95% CI, 0.63–0.71], P <0.001), either surgical AVR (aOR, 0.48 [95% CI, 0.43–0.54], P <0.001) or transcatheter aortic valve implantation (aOR, 0.79 [95% CI, 0.75–0.84], P <0.001). Conclusions The use of surgical AVR and transcatheter aortic valve implantation was significantly lower in female patients with AS and severe features independent from patient‐ and hospital‐level characteristics.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".