Sex-Specific Cardiometabolic Determinants of Postoperative Atrial Fibrillation After Cardiac Surgery
Bibliographic record
Abstract
BACKGROUND: Cardiometabolic diseases increase the risk of postoperative atrial fibrillation (POAF), a complication leading to higher long-term risk of major cardiovascular events (MACE). It remains unknown whether the effect of these risk factors differs according to sex. We sought to evaluate the sex-specific predictors of POAF after coronary artery bypass grafting (CABG). METHODS: In a prospective registry of patients undergoing isolated CABG, we compared predictors of POAF between sexes with logistic regression models. Because of high prevalence of abdominal obesity in women, > 80% having a waist circumference (WC) ≥ 88 cm, median WC values were used to define abdominal obesity (men ≥ 102 cm, women ≥ 100 cm). RESULTS: This analysis included 6177 individuals (17% women). Mean age was 65.6 ± 8.9 years. POAF occurred in 32% of men and 28% of women (P < 0.05). Compared with men, women with POAF had similar WC; higher prevalence of hypertension and diabetes; lower high-density lipoprotein (HDL)-cholesterol; and higher glucose, triglyceride, low- density lipoprotein (LDL)-cholesterol, and C-reactive protein levels (all P < 0.05). After adjustment, age and abdominal obesity were associated with POAF in both sexes (P < 0.05). The interaction of WC with sex suggested a worse impact of WC on POAF risk among women (adjusted odds ratio [OR], 1.97; 95% confidence interval [CI], 1.48-2.62 vs in men 1.33; 95% CI, 1.17-1.50; P for interaction = 0.01). CONCLUSIONS: Abdominal obesity is a major predictor of POAF in both sexes, with higher risk in women. These results emphasize the need for enhanced strategies to manage abdominal obesity and its cardiometabolic consequences in the general population and the potential to develop sex-specific preventive interventions to reduce risk of POAF.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".