Trends in Smoking Cessation Counseling: Experience From American Heart Association‐Get With The Guidelines
Notice bibliographique
Résumé
Abstract Background: Successful smoking cessation in stroke and coronary artery disease (CAD) patients is important, as smoking contributes to significant morbidity and mortality. The American Heart Association developed Get With The Guidelines (GWTG) to improve compliance with national guideline recommendations for cardiovascular care. Using data from GWTG, we examined trends associated with the smoking‐cessation counseling (SCC) performance measure. Hypothesis: Implementation of a systematic quality improvement program will increase compliance with the SCC performance measure. Methods: We evaluated compliance with SCC in current or recent smokers identified from 224 671 CAD admissions between 2002 and 2008 in the GWTG‐CAD database, and from 405 681 stroke admissions between 2002 and 2007 in the GWTG‐Stroke database. Additionally, we examined adherence to other performance and quality measures related to CAD and stroke care. Results: Overall, 55 904 GWTG‐CAD and 58 865 GWTG‐Stroke admissions were used for the analysis. Rates of SCC improved in each successive year during the study, from 67.6% to 97.4% (P < 0.001) in GWTG‐CAD and from 40.1% to 90.7% (P < 0.001) in GWTG‐Stroke. Compliance with SCC was up to 34.7% lower (P < 0.0001) in GWTG‐Stroke compared with GWTG‐CAD, but this difference decreased to 6.7% (P < 0.0001) by the end of the study period. Compliance with many other performance and quality measures was significantly lower among patients not receiving SCC. Conclusions: Get With The Guidelines has improved compliance with the SCC performance measure among patients with CAD and stroke. Although the initial disparity in rates of SCC between CAD and stroke patients gradually improved, the difference remained significant. All authors had access to the data and participated in the preparation of this manuscript. GWTG‐CAD is a program of the American Heart Association (Dallas, TX) and is supported in part by an unrestricted educational grant from Merck/Schering‐Plough Pharmaceutical (White House Station, NJ) and Pfizer (New York, NY). The analysis of registry data was performed at Duke Clinical Research Institute (Durham, NC), which receives funding from the American Heart Association. The sponsors were not involved in the design, analysis, preparation, review, or approval of this manuscript. Pei‐Hsiu Huang, Charles X. Kim, Amir Lerman, David Dai, Warren Laskey, W. Frank Peacock, Eric D. Peterson, Eric E. Smith, Gregg C. Fonarow, and Lee H. Schwamm have no relevant disclosures. Christopher P. Cannon reports receiving research grants and support from Accumetrics, AstraZeneca, GlaxoSmithKline, Merck, Essentialis, and Takeda; involvement in the advisory board for Bristol‐Myers Squibb/Sanofi, Novartis, and Alnylam; honoraria for development of independent educational symposia from Pfizer and AstraZeneca; and is a clinical advisor having equity in Automedics Medical Systems. Adrian F. Hernandez reports receiving research grants from Johnson & Johnson and Amylin, and honoraria from AstraZeneca, Corthera, and Sanofi‐Aventis. Deepak L. Bhatt reports the following associations and memberships: advisory board, Medscape Cardiology; board of directors, Boston VA Research Institute, Society of Chest Pain Centers; chair, American Heart Association Get With The Guidelines Science Subcommittee; honoraria, American College of Cardiology (editor, Clinical Trials, Cardiosource), Duke Clinical Research Institute (clinical trial steering committees), Slack Publications (chief medical editor, Cardiology Today Intervention), and WebMD (CME steering committees); research grants, Amarin, AstraZeneca, Bristol‐Myers Squibb, Eisai, Ethicon, Medtronic, Sanofi Aventis, and The Medicines Company; and unfunded research, PLx Pharma and Takeda. The authors have no other funding, financial relationships, or conflicts of interest to disclose.
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 | 0,002 | 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 ».