The Prognostic Impact of Smoking Status, Cessation, and Anticoagulation-Interaction on Adverse Outcomes in Patients with Atrial Fibrillation: A Systematic Review
Bibliographic record
Abstract
Introduction: Atrial fibrillation (AF) and tobacco smoking represent two of the most significant and concurrent global health burdens. While smoking is an established risk factor for the development of incident AF, its prognostic impact following an AF diagnosis has remained controversial, particularly regarding thromboembolic risk (Zhu, Guo, & Hong, 2016). This systematic review synthesizes the evidence on the association between smoking status (current, former, and cessation) and a comprehensive range of adverse outcomes in patients with established AF. Methods: A systematic review was conducted adhering to PRISMA guidelines. The Cochrane Library, PubMed, and Embase databases were searched for observational studies (cohort or case-control) and meta-analyses evaluating the prognostic impact of smoking in patients with a confirmed AF diagnosis. Methodological quality and risk of bias for all included non-randomized studies were rigorously assessed using the 9-star Newcastle-Ottawa Scale (NOS) (Wells et al., 2000). Results: A total of 17 high-quality observational studies, including large national cohorts and one key meta-analysis, were included. The evidence was consistent and significant that persistent smoking is associated with increased all-cause mortality (Relative Risk 1.82, 95% CI 1.33–2.49) (Zhu, Guo, & Hong, 2016) and cardiovascular death (RR 1.54, 95% CI 1.31–1.81) (Zhu, Guo, & Hong, 2016). Smoking was also a significant predictor of major bleeding (RR 1.93, 95% CI 1.08–3.47) (Zhu, Guo, & Hong, 2016) and AF recurrence post-catheter ablation (RR 3.19, 95% CI 1.23–8.27) (Okutucu et al., 2010). The association with stroke was contradictory (the "stroke paradox"); a major meta-analysis found no significant link (RR 1.19, 95% CI 0.97–1.46) (Zhu, Guo, & Hong, 2016), while large cohort studies, particularly those in Vitamin K Antagonist (VKA)-treated populations, reported a significant risk (Adjusted Hazard Ratio 1.64–1.66) (Lee et al., 2021; Nakagawa et al., 2015). Critically, smoking cessation after AF diagnosis was associated with a rapid and significant risk reduction for ischemic stroke (aHR 0.702, 95% CI 0.595–0.827) and all-cause death (aHR 0.842, 95% CI 0.748–0.948) compared to persistent smokers (Lee et al., 2021). Discussion: The data confirm that persistent smoking is a major driver of mortality, major bleeding, and interventional failure in AF patients. The "stroke paradox" is likely not a true null effect but a signal of confounding, specifically an interaction with VKA (e.g., warfarin) therapy, where smoking is known to disrupt anticoagulation control (Nakagawa et al., 2015). This risk may be attenuated in the modern era of Direct Oral Anticoagulants (DOACs). Conclusion: Persistent smoking is unequivocally associated with a severe adverse prognostic profile in patients with AF. Smoking cessation provides a rapid, substantial, and quantifiable prognostic benefit—reducing stroke and mortality risk—and must be considered a critical, non-negotiable therapeutic intervention on par with anticoagulation and rhythm control.
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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 | 0.006 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.011 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".