CCS patients with polyvascular disease are a high risk but heterogenous subset of patients: insights from the CLARIFY registry
Notice bibliographique
Résumé
Abstract Introduction Polyvascular disease constitutes a powerful predictor of cardiovascular events, is found in 10 to 15% of chronic coronary syndromes (CCS) patient. Smoking and diabetes mellitus are strongly associated with polyvascular disease. Risk stratification is key to select the most appropriate therapeutic strategy for a given patient. Purpose We aimed to describe 5-year ischaemic risk of CCS patients according to vascular disease phenotype and diabetic or smoking status. Method We analyzed data from 32 703 consecutive CCS outpatients (45 countries) enrolled between November 2009 to June 2010 in the prospective observational CLARIFY registry. Three mutually exclusive groups were compared: Coronary artery disease (CAD) alone, CAD with peripheral artery disease (PAD) or cerebrovascular disease (CVD) (CAD+1), CAD with CVD and PAD (CAD+2). Primary outcome was a composite of cardiovascular death, myocardial infarction or stroke, adjusted on age, sex and geographic origin at 5 years. Results At baseline, 26440 (80.8%) patients were diagnosed with CAD alone, 4967 (15.2%) had CAD+1, 1296 (4%) had CAD+2. Overall, 9501 (29%) patients were diabetics, 19184 (58.7%) were smokers or ex-smokers and only 9220 (28.2%) were free of these two major cardiovascular risk factors. Primary outcome increasing gradually according to the number of arterial diseases locations from 8.4% (95% CI 8.09–8.73) in patients with CAD alone to 17.4% (95% CI 16.95–17.83) of CAD+2 patients (p<0.001). Subgroup analysis according to diabetes or smoking status further enriched risk stratification from 7% (95% CI 6.48–7.59) in non-diabetic, non-smoking CAD alone patients to 20.3% (95% CI 19.08–21.44) in diabetics and smokers CAD+ 2 patients (Figure 1). Diabetic CAD alone patients had a comparable risk to that of non-diabetic and non-smoking polyvascular patients, 9.8% (95% CI 8.82–10.68) vs 10.3% (95% CI 9.61–10.96), p=0.38. Outcome was similar between polyvascular diabetic patients, regardless of the number of arterial diseases, 15.5% (95% CI 14.31–16.60) for CAD+1 and 15.0 (95% CI 13.88–16.13) for CAD+2, p=0.83. Smoking increased 5-year risk proportionally to the number of symptomatic arterial bed, 8.2% (95% CI 7.72–8.68) vs 11.8% (95% CI 11.18–12.31) vs 17.9% (95% CI 17.18–18.54), respectively for CAD alone, CAD+1 and CAD+2. Conclusion CCS patients with polyvascular disease remain at high risk of ischaemic events in the contemporary practice with widespread secondary prevention therapies. Polyvascular is a very heterogenous subset of patients with ischaemic risk varying not only according to the number of vascular bed diseased but also according to smoking and diabetes status, two conditions present in the vast majority of CCS patients. Diabetes confers upfront a maximal increased risk. Identification of higher risk subsets in polyvascular patients can potentially identify those that could derived the greatest benefit from new secondary prevention strategies. Figure 1 Funding Acknowledgement Type of funding source: Public hospital(s). Main funding source(s): Assistance Publique-Hôpitaux de Paris
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 source (Gemma direct ou Codex distillé), 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 ».