Abstract 63: The Smoking Paradox in Patients Hospitalized with Coronary Artery Disease: Findings from Get With The Guidelines - CAD
Bibliographic record
Abstract
Introduction: Despite evidence of smoking as a potent risk factor for coronary artery disease (CAD), there have been reports of lower in-hospital mortality among smokers hospitalized for CAD events. Method: We analyzed all consecutive CAD admissions (n=158,054) without a prior history of Stroke/TIA from 2002-2008 in Get With The Guidelines (GWTG)-CAD. Categorical data were analyzed by Pearson Chi-square and continuous data by Wilcoxon test. Multivariable models with generalized estimating equations for in-hospital clustering were used to estimate odds ratios of in-hospital mortality. All significant predictors on univariate analysis were included in the multivariable model. Results: Among all CAD patients, 30.4% were current smokers, defined as any cigarette use in the past year. Smokers were substantially younger (12 years), more often male and less often had pre-existing hypertension, dyslipidemia, heart failure, renal failure and atrial fibrillation, and more often had COPD/Asthma. Smokers were more likely to be admitted to large, academic hospitals, and more often in the South. Smokers had shorter length of stay in hospital and were more often discharged home. In-hospital mortality was lower in smokers as compared to non-smokers (Table 1). The significant univariate mortality difference attenuated dramatically after adjusting for age and other covariates in the multivariable model, OR increased from 0.57 (0.53, 0.61) on univariate analysis to 0.88 (0.81, 0.95) on multivariable model. Other independent predictors of mortality were increasing age [1.51 (1.46, 1.56)], history of diabetes mellitus [1.25 (1.18, 1.33)], Asthma/COPD [1.30 (1.23, 1.38)], peripheral vascular disease [1.34 (1.24, 1.44)], heart failure [1.48 (1.38, 1.58)] and renal insufficiency [1.61 (1.48, 1.74)]. Conclusion: Smoking continues to be a major risk factor for presenting with CAD at a much younger age and with fewer risk factors. It is likely that the continued modest association with lower in-hospital mortality in smokers in this analysis after adjustment reflects residual or unmeasured confounding. This apparent smoker’s paradox in CAD should not be interpreted as a benefit of cigarette smoking.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".