Identifying women with severe angiographic coronary disease
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».