Opioid Use and the Risk of Ventricular Arrhythmias: A Systematic Review and Meta‐Analysis
Bibliographic record
Abstract
ABSTRACT Background The association between opioid use and the risk of ventricular arrhythmias (VA) is poorly understood. Aims The objective of this study was to synthesize the evidence on the risk of VA associated with opioid use. Materials & Methods We systematically searched the Cochrane Library, Embase, MEDLINE, and CINAHL databases in July 2022. Risk of bias was assessed using the Cochrane risk for bias tool for randomized controlled trials (RCTs) and ROBINS‐I for observational studies. Certainty of evidence was assessed using GRADE. Results We included 15 studies (12 observational, 2 post hoc analyses of RCTs, 1 RCT). Most studies focused on opioid use for maintenance therapy (n = 9), comparing methadone to buprenorphine (n = 13), and reported QTc prolongation (n = 13). Six observational studies had a critical risk of bias, and one RCT was at high risk of bias. Two studies could not be included in the meta‐analysis as they reported a different outcome and studied an opioid antagonist. Meta‐analysis of 13 studies indicated that the use of methadone was associated with an increased risk of VA compared to the use of buprenorphine, morphine, placebo, or levacetylmethadol (risk ratio [RR], 2.39; 95% CI, 1.31–4.35; I2 = 60%). The pooled estimate varied greatly between observational studies (RR, 2.12; 95% CI, 1.15–3.91; I2 = 62%) and RCTs (RR, 14.09; 95% CI, 1.52–130.61; I2 = 0%), but both indicated an increased risk. Conclusion In this systematic review and meta‐analysis, we found that methadone use is associated with more than twice the risk of VA compared to comparators. However, our findings should be interpreted cautiously given the limited quality of the available evidence.
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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 | 0.033 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.034 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| 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".