Identifying women with severe angiographic coronary disease
Bibliographic record
Abstract
Abstract. Kreatsoulas C, Natarajan MK, Khatun R, Velianou JL, Anand SS (McMaster University; CARING Network, McMaster University; Population Health Research Institute, McMaster University and Hamilton Health Sciences; Interventional Cardiology, Hamilton Health Sciences; Eli Lilly Canada–May Cohen Chair in Women's Health, McMaster University; Michael G. DeGroote‐Heart and Stroke Foundation of Ontario Chair in Population Health Research, McMaster University; Population Genomics Program, McMaster University; McMaster University, Hamilton, ON, Canada). Identifying women with severe angiographic coronary disease. J Intern Med 2010; 268 :66–74. Objectives. To determine sex/gender differences in the distribution of risk factors according to age and identify factors associated with the presence of severe coronary artery disease (CAD). Design. We analysed 23 771 consecutive patients referred for coronary angiography from 2000 to 2006. Subjects. Patients did not have previously diagnosed CAD and were referred for first diagnostic angiography. Outcome measures. Patients were classified according to angiographic disease severity. Severe CAD was defined as left main stenosis ≥50%, three‐vessel disease with ≥70% stenosis or two‐vessel disease including proximal left anterior descending stenosis of ≥70%. Univariate and multivariate logistic regression was used to assess the association between risk factors and angina symptoms with severe CAD. Results. Women were less likely to have severe CAD (22.3% vs. 36.5%) compared with men. Women were also significantly older (69.8 ± 10.6 vs. 66.3 ± 10.7 years), had higher rates of diabetes (35.0% vs. 26.6%), hypertension (74.8% vs. 63.3%) and Canadian Cardiovascular Society (CCS) class IV angina symptoms (56.7% vs. 47.8%). Men were more likely to be smokers (56.9% vs. 37.9%). Factors independently associated with severe CAD included age (OR = 1.05; 95% CI 1.05–1.05, P < 0.01), male sex (OR = 2.43; CI 2.26–2.62, P < 0.01), diabetes (OR = 2.00; CI 1.86–2.18, P < 0.01), hyperlipidaemia (OR = 1.50; CI 1.39–1.61, P < 0.01), smoking (OR = 1.10; CI 1.03–1.18, P = 0.06) and CCS class IV symptoms (OR = 1.43; CI 1.34–1.53, P < 0.01). CCS Class IV angina was a stronger predictor of severe CAD amongst women compared with men (women OR = 1.82; CI 1.61–2.04 vs. men OR = 1.28; CI 1.18–1.39, P < 0.01). Conclusions. Women referred for first diagnostic angiography have lower rates of severe CAD compared with men across all ages. Whilst conventional risk factors, age, sex, diabetes, smoking and hyperlipidaemia are primary determinants of CAD amongst women and men, CCS Class IV angina is more likely to be associated with severe CAD in women than men.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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".