Impact of Sex on Long-Term Outcomes Following Surgical Aortic Valve Replacement
Bibliographic record
Abstract
BACKGROUND: The impact of sex on outcomes following surgical aortic valve replacement (SAVR) remains unclear. It has been proposed that females experience inferior outcomes, but this has yet to be conclusively established, particularly in the long term. The objective of this study is to identify discrepancies in postoperative outcomes between males and females following SAVR to better inform consideration for surgical intervention. METHOD: We retrospectively reviewed the outcomes of 4,927 patients who underwent SAVR from 2004 to 2018 at our centre. In total, 531 propensity-matched males and females were included in the final analysis. The primary outcome was mortality at any point during the follow-up period. Secondary outcomes included various measures of postoperative morbidity. Follow-up duration was 15 years. RESULTS: In SAVR all-comers, females experienced inferior short-term mortality, but equivalent mid-term and long-term mortality. Rates of mediastinal bleeding, sternal wound infections, sepsis, heart failure, and pacemaker insertion were all equivalent between the sexes; however, males experienced a higher rate of acute kidney injury and readmission for stroke at the longest follow-up while females experienced a longer intensive care unit and hospital length of stay. In a sub-analysis of isolated SAVR, males and females experienced equivalent early, mid, and late mortality. Of note, a trend towards increased aortic valve reoperation was noted in females at the longest follow-up. CONCLUSIONS: Males and females experience equivalent long-term mortality following isolated SAVR. Sex is not an independent risk factor of poor outcomes post-SAVR; however, the increased preoperative risk profile of females requires diligent consideration.
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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 | 0.006 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".