Post-cardiac surgery atrial fibrillation and sex differences in clinical outcomes: a systematic review and meta-analysis
Bibliographic record
Abstract
Abstract There is conflicting literature on sex differences and clinical outcomes in patients who develop atrial fibrillation (AF) post-cardiac surgery. Our aim was to compare clinical outcomes between females and males with post-cardiac surgery AF. A systematic search was conducted for studies published until 27 September 2024 in MEDLINE, Embase, and CENTRAL. Included studies compared mortality and stroke in females vs. males who developed AF after cardiac surgery. Outcomes of interest were mortality and stroke. Pooled prevalence was used to compare comorbidities. Raw event rates were used to calculate odds ratios (ORs), which were pooled with a fixed-effects model. 10 422 studies were identified and 5 studies met inclusion criteria. 14 970 patients who developed AF after cardiac surgery were included, of whom 3748 were females. The length of follow-up was up to 10 years. The weighted average age was 70.4 ± 2.9 years in females and 66.7 ± 2.4 years in males (P = 0.32). At baseline, females had a higher prevalence of hypertension, diabetes, dyslipidemia, stroke, and peripheral vascular disease. The odds of in-hospital mortality among females were higher compared to males (5.5 vs. 3.0%; OR 2.04, 95% CI 1.42–2.91, P < 0.001, I2 = 57%). There were no significant differences in post-discharge mortality (45.6 vs. 42.9%; OR 1.05, 95% CI 0.97–1.15, P = 0.23, I2 = 0%) or in-hospital stroke (2.5 vs. 1.9%; OR 1.30, 95% CI 0.79–2.13, P = 0.30, I2 = 57%) in females vs. males. In conclusion, females with post-cardiac surgery AF had a higher prevalence of comorbidities at baseline. The odds of in-hospital mortality were twice as high among females. There were no significant differences in post-discharge mortality or in-hospital stroke. Future studies are warranted to understand the mechanisms of increased in-hospital mortality in females and to develop effective monitoring strategies and interventions.
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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 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.031 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".