Trajectories in smoking habits and outcomes in patients with stable coronary artery disease
Notice bibliographique
Résumé
Abstract Background Smoking is a well-identified risk factor for recurrent cardiovascular (CV) events in patients with stable coronary artery disease (CAD). However, smoking status is not binary, and there is a wide diversity of smoking habits trajectories within this population, with different consequences on residual CV risk. Purpose To better understand how smoking status evolves in patients with CAD, and how it is associated with subsequent CV events. Methods We analysed the CLARIFY registry, which included 32,378 patients with CAD. We defined groups according to smoking status at inclusion in the registry: never, former and active smokers. For former smokers, we assessed the timing of definitive smoking discontinuation and its relationship with subsequent CV events. For active smokers, we investigated the association between smoking reduction or discontinuation in active smokers with outcomes. Our primary endpoint was the occurrence of MACE defined by CV death or myocardial infarction (MI) during the 5-year follow-up period adjusted on known predictors for MACE in CAD patients. Results At inclusion, 13,366 (41.3%) patients had never smoked, 14,973 (46.2%) were former smokers, and 4,039 (12.5%) were current smokers, after an average of 6.5 (±6.3) years following CAD diagnosis. In former smokers that were smoking at the time of CAD diagnosis, 72.8% definitively discontinued smoking within the first year following diagnosis, while only 27.2% quit in subsequent years. Each additional year of active smoking after CAD diagnosis increased the risk of MACE (adjusted HR [aHR] 1.08, 95% CI 1.04-1.12 per year). However, regardless of the duration of smoking cessation, former smokers did not return to the cardiovascular risk levels observed in individuals who never smoked (figure). In active smokers at inclusion, smoking cessation was associated with improved CV outcomes, irrespective of the timing of cessation during follow-up (aHR 0.56, 95% CI 0.42-0.76, p < 0.001). Among active smokers, 25.7% reduced their quantity smoked within the first year of follow-up without quitting, but this did not significantly reduce the rate of CV death and MI (aHR 0.96, 95% CI 0.74-1.26, p = 0.78) compared to other active smokers. Conclusion In active smokers diagnosed with CAD, the vast majority of definite quitters cease smoking within the first year of CAD diagnosis. Each subsequent year of active smoking adversely affects CV outcomes. Smoking cessation at any time significantly reduces the risk of CV death or recurrent MI compared to persistent active smokers, although CV risk of former smokers does not decrease to the levels observed in never-smokers. Finally, smoking quantity reduction is not enough to reduce CV risk in this population. These findings underscore the critical importance of timely interventions for smokers diagnosed with CAD, and emphasize that when it comes to smoking cessation, sooner is better, but it is never too late.
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 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 | 0,004 |
| 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 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| 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 ».