Sex-Related Differences in Postoperative Outcomes After Transcatheter Aortic Valve Replacement: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Aortic stenosis is the most common valvular disease of the heart and is increasing in prevalence. Previous literature has found inferior outcomes for females undergoing surgical aortic valve replacement, while recent investigations have found equivalent or superior outcomes for females undergoing transcatheter aortic valve replacement (TAVR). PubMed and Medline were systematically searched for articles published from January 1, 2010, to April 30, 2021, for retrospective and prospective studies comparing outcomes between males and females undergoing TAVR. One thousand one hundred eighty titles and abstracts were screened, and 28 were included in this review. Risk of bias was assessed using questions derived from the ROBINS-I tool and previous literature. The data were compiled and analyzed using the RevMan 5.4 software. The results of this review confirm the previously published literature and have found rates of acute kidney injury ( P = 0.05) and postoperative pacemaker insertion ( P < 0.00001) favoring females and in-hospital mortality ( P = 0.04), stroke ( P < 0.00001), bleeding complications ( P < 0.00001), and vascular complications ( P < 0.00001) favoring males. The previously published literature has demonstrated consistently inferior outcomes for females undergoing heart valve surgery when compared to males. However, contemporary literature investigating sex differences after TAVR has found comparable outcomes for females. While the postoperative outcomes after surgical aortic valve replacement and TAVR are well established, the causal factors are still unidentified. Future studies utilizing matching based on preoperative characteristics and follow-up including collection of postoperative ventricular remodeling and prosthetic valve performance data will aid in elucidating the causal factors impacting outcomes for males and females after TAVR.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.029 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".