Sex Differences in Atherosclerotic Coronary Artery Disease Patterns
Bibliographic record
Abstract
Background Sex differences in coronary artery disease (CAD) have been increasingly recognized, as women present with distinct clinical characteristics and outcomes compared with men. This study investigated the impact of sex on pathophysiological CAD patterns (focal versus diffuse) in stable patients undergoing percutaneous coronary interventions (PCI). Methods We conducted a subanalysis of the PPG Global (Pullback Pressure Gradient Global Registry) study, a multicenter, prospective trial including 993 patients (236 [23.8%] women and 757 [76.2%] men) with hemodynamically significant CAD, defined as fractional flow reserve ≤0.80. The pullback pressure gradient metric categorized CAD patterns as focal or diffuse. Patient‐reported outcomes were collected using the 7‐item Seattle Angina Questionnaire. Optimal revascularization was defined as post‐PCI fractional flow reserve ≥0.88. Results Women were significantly older than men, with a mean age of 69.8±10.3 years compared with 67.0±10.1 years ( P <0.001). Despite similar baseline fractional flow reserve (0.69±0.12 versus 0.67±0.11, P =0.093), women reported more severe symptoms compared with men, as reflected in the Seattle Angina Questionnaire‐7 angina frequency score (mean 76.7±22.9 versus 81.5±20.3, P =0.002). Women exhibited a more focal CAD pattern (pullback pressure gradient 0.65±0.16 versus 0.61±0.16, P =0.001) and achieved higher post‐PCI fractional flow reserve values (0.88±0.07 versus 0.87±0.07, P =0.02). Women undergoing PCI had a higher rate of optimal revascularization (54% versus 44%, P =0.01). Conclusions This study reveals clinically significant differences in CAD patterns between sexes, with women demonstrating a higher burden of angina, more focal disease distribution, and better physiological results after PCI.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 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".