Trends in management of cardiovascular risk factors in young adults who go on to develop coronary artery disease
Notice bibliographique
Résumé
Abstract Background Rates of premature coronary artery disease (CAD) have remained stagnant in many countries in the past two decades. While guidelines on cardiovascular prevention are evolving to improve risk assessment, more data are needed to evaluate changes in risk management in younger patients with high cardiovascular risk. Purpose To investigate the trends in pre-presentation prevalence and management of major cardiovascular risk factors (CVRFs) and predictors of initiation of lipid-lowering therapy (LLT) in patients with premature coronary artery disease. Methods In this retrospective cohort study, we identified women <55 years old and men <50 years old who presented with obstructive CAD (stenosis >50% or history of coronary revascularization) between 2000 and 2017. Data were collected from administrative databases and provincial cardiac information system and included demographics, all outpatient visits, hospitalizations, pharmacy dispenses in the period of 3 years prior to CAD, presentation characteristics, and results of invasive coronary angiography. Results In total, 14,470 patients (29.7% females) met the study criteria. During the study period, prevalence decreased for dyslipidemia and smoking and increased for hypertension, diabetes and obesity (Figure 1). Over 96.5% of patients were eligible for lipid screening 3 years prior to presentation with CAD, 93.5% had visits with primary care physicians, and 57.4% had visits with specialist physicians. Only 20.5% received LLT, with rates rising from 19% in the year 2000 to 24% in 2017 (p=0.023). Additionally, 38.1% of patients received medical treatment for other CVRFs. Figure 2 presents risk management and rates of LLT initiation in patients with five major CVRFs. In multivariable logistic regression analysis, LLT was more likely in patients with diabetes (odds ratio [OR] 3.04, 95% CI 2.74–3.36), hypertension (OR 1.44, 95% CI 1.30–1.58), chronic kidney disease (OR 1.79, 95% CI 1.44–2.23), and depression (OR 1.25, 95% CI 1.11–1.40). Later year of presentation (OR 1.04, 95% CI 1.03–1.05), older age at presentation (OR 1.03, 95% CI 1.02–1.04) and residing in urban areas vs rural (OR 1.36, 95% CI 1.16–1.58) were also positively associated with LLT. Male sex (OR 0.88, 95% CI 0.78–0.98) and smoking (OR 0.68, 95% CI 0.62–0.76) were associated with lower likelihood of treatment. Obesity, hypothyroidism, chronic inflammatory conditions, and malignancies were not significantly associated with LLT. Conclusion Despite the rising prevalence of CVRFs, high eligibility for lipid screening, and availability of care, rates of LLT initiation in patients who go on to develop premature CAD remain very low, representing a major missed opportunity for cardiovascular disease prevention. Many of the major CVRFs and risk enhancers endorsed in current guidelines were not associated with treatment initiation, suggesting that new approaches may be required to increase appropriate use LLT in these patients. Funding Acknowledgement Type of funding sources: Foundation. Main funding source(s): St. Pauls Hospital Foundation, Vancouver, BC, CanadaVancouver General Hospital Foundation, Vancouver, BC, Canada
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 | 0,000 | 0,002 |
| 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 | 0,000 |
| Intégrité de la recherche | 0,000 | 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 ».