Trends in Smoking Cessation Counseling: Experience From American Heart Association‐Get With The Guidelines
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".