Electronic cigarettes and cardiovascular diseases: An updated systematic review and network meta-analysis
Bibliographic record
Abstract
INTRODUCTION: The association between electronic cigarettes (e-cigarettes) and the risk of cardiovascular disease (CVD) remains inconclusive. This study aims to compare CVD risk from the use of e-cigarettes, cigarettes, combined cigarette and e-cigarette use, and non-use. METHODS: This study is a systematic review and network meta-analysis (NMA). MEDLINE and Scopus databases (through February 2024) were used to identify eligible studies. Observational studies that investigated the effect of e-cigarettes on the risk of composite CVD, myocardial infarction (MI), or stroke, compared to cigarette, dual use, or non-use, were included. NMA was applied to estimate relative effects (i.e. adjusted odds ratio, AOR) of e-cigarette, cigarette, and dual use, on composite CVD, MI, and stroke outcomes. Risk of bias was assessed using the Joanna Briggs Institute tool for surveys and the Newcastle-Ottawa scale for cohort studies. RESULTS: Eleven adult population studies were eligible for review. E-cigarette, cigarette, and dual use were significantly associated with composite CVD outcomes. Pooled AORs (95% CI) were 1.31 (1.05-1.62) for e-cigarette, 1.57 (1.30-1.88) for cigarette, and 1.67 (1.37-2.03) for dual use. Additionally, former cigarette and former dual use significantly increased the risk of composite CVD outcomes, compared to non-use. The pooled AORs (95% CI) were 1.29 (1.05-1.59) for former cigarette, and 1.46 (1.03-2.08) for former dual use, while former e-cigarette use was not significantly associated with composite CVD endpoints. For MI and stroke outcomes, only cigarette and dual use were significantly associated with these events. CONCLUSIONS: Current e-cigarette, cigarette, and dual use were significantly associated with increased risk of composite CVD outcomes, while only cigarette and dual use significantly increased the risk of MI and stroke, compared to non-use. However, these findings were primarily based on cross-sectional data limiting the temporality of effect; additional prospective cohort studies are needed to confirm our findings.
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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 | 0.016 | 0.037 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.031 |
| Bibliometrics | 0.011 | 0.011 |
| 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".