Association between persistent smoking after a diagnosisof heart failure and adverse health outcomes: A systematicreview and meta-analysis
Bibliographic record
Abstract
INTRODUCTION: Heart failure (HF) is associated with increased mortality worldwide. Adverse health outcomes in HF are commonly attributed to poor adherence to self-care, including smoking cessation. Smoking is the major modifiable risk factor for HF. Patients have been observed to continue smoking even after diagnosis with HF. Despite the possible association between persistent smoking and adverse health outcomes among HF populations, no consensus has been reached. We aimed to review the literature to determine the association between smoking status after HF diagnosis and adverse health outcomes. METHODS: A systematic literature search was performed in PubMed, PsycINFO, Web of Science, and Embase. Hand searching was also performed. In total, 9 articles (n=70461) were included in the review for meta-analysis, including seven cohort studies and two cross-sectional studies. Quality was assessed using the modified version of the Newcastle-Ottawa Scale. RESULTS: Approximately 16% of HF patients continued smoking after HF diagnosis. Persistent smoking increased the hazard ratio (HR) of mortality by 38.4% (HR=1.384; 95% CI: 1.139-1.681) and readmission by 44.8% (HR=1.448; 95% CI: 1.086-1.930). Our review also found that persistent smoking was associated with poor health status, ventricular tachycardia, and arterial stiffness. CONCLUSIONS: This review highlights the importance of assessment for any history of smoking before and after HF diagnosis. There is a need for smoking cessation programs to be established as crucial components of care for patients with HF. More studies are needed to investigate the possible mechanisms underlying relations among smoking patterns and health consequences.
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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 | 0.014 | 0.044 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.024 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".