Sex difference and outcome trends following surgical aortic valve replacement from the National Inpatient Sample (NIS) Database
Bibliographic record
Abstract
BACKGROUND: Based on worldwide registries, approximately 50% of patients who underwent aortic valve replacement (AVR) via surgical aortic valve replacement are females. Although AVR procedures have improved greatly in recent years, differences in outcome including mortality between sexes remain. We aimed to investigate the trends in SAVR outcomes in females versus males. METHODS: Using the 2011-2017 National Inpatient Sample (NIS) database, we identified hospitalizations for patients with diagnosis of aortic stenosis during which SAVR was performed. Patients' sociodemographic and clinical characteristics, procedure complications, and mortality were analyzed. Piecewise regression analyses were performed to assess temporal trends in SAVR utilization in females versus males. Multivariable analyses were performed to identify predictors of in-hospital mortality. RESULTS: A total of 392,087 hospitalizations for SAVR across the USA were analyzed. Utilization of SAVR in both sex patients decreased significantly during the years 2011-2017. Males compared to females had significantly higher rates of hyperlipidemia, chronic renal disease, peripheral artery disease, coronary artery disease and tended to be smokers. Differences in mortality rates among sexes were observed for SAVR procedures. Women had higher in-hospital mortality with 3.7% compared to men with 2.5% (OR 1.38 [95% CI 1.33-1.43, P<0.001]). In a multivariable regression model analysis adjusted for potential confounders, women had higher mortality risk with odd ratio (OR 1.38 [95% CI 1.33-1.43], P<0.001). Women had significantly higher rates of vascular complications (5.1% compared to men with 4.6%, P=0.002). CONCLUSIONS: Utilization of SAVR showed a downward trend during the study period. Higher in-hospital mortality was recorded in females compared to males.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.006 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".