Abstract 15744: Worse Impact of Diabetes on All-Cause Mortality in Women Following CABG
Bibliographic record
Abstract
Introduction: Women referred for coronary artery bypass grafting (CABG) have worse metabolic disorders. The latter are associated with adverse cardiovascular (CV) events and bear a worse prognosis in women. Yet, our understanding of the sex-specific impact of metabolic disorders on mortality after CABG is limited. Aim: To evaluate the interaction of sex with metabolic abnormalities on mortality after CABG, with the hypothesis that they would lead to higher mortality in women. Methods: In a prospective cohort (2006-19), we selected patients who underwent elective isolated CABG, excluding early (<48h) mortality, atrial fibrillation/flutter, pacemaker/defibrillator or heritable dyslipidemia. Sex-specific predictors of all-cause mortality after CABG and their interaction with sex were assessed with Cox proportional hazard models (stepwise selection for each model). Diabetes was defined as per Canadian Diabetes Association Guidelines. Results: We included 6,177 individuals (17% women) in this analysis. All-cause mortality incidence was 438 (9%) and 119 (11%) in men and women respectively (p=0.01) over a median of 5.7 yrs. Compared to men, women were older (67 ± 9 vs 65 ± 9 yrs), more likely to have hypertension (85% vs 78%), diabetes (40% vs 34%), abdominal obesity (82% vs 51%), chronic kidney disease (27% vs 15%), LVEF ≥40% (94% vs 92%) and had higher LDL-cholesterol (72 [17-238] vs 67 [10-221] md/dL) (all p<0.05). Prior myocardial infarction was similar in both sexes (11%, p=0.48). Predictors of mortality are in Figure 1. An interaction between sex and diabetes was found (p=0.03), suggesting worse impact of diabetes in women after CABG. Conclusion: Women undergoing CABG have worse CV risk factors and metabolic risk profile, including diabetes which significantly increased mortality risk vs men. Studies are needed to evaluate which sex- or gender-related factors, like poorer diabetes management or reduced participation in cardiac rehabilitation, may be involved.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.005 | 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".