Tramadol and the risk of adverse cardiovascular events for patients with non-cancer pain
Bibliographic record
Abstract
Tramadol and codeine are both weak opioids indicated for the treatment of acute and chronic moderate to moderately severe pain, though their pharmacologic profiles differ substantially. Due to the perceived low risk of abuse of tramadol compared to other opioid, prescriptions for tramadol have increased by 30% in Canada and 65% in the United States over the last decade. Aside from acting on the mu-opioid receptor, tramadol also exerts its analgesic activity through inhibiting the reuptake of serotonin and norepinephrine in the central nervous system. Excess amounts of both neurotransmitters can have pro-arrhythmic effects and can stimulate the sympathetic nervous system, resulting in vasoconstriction and blood pressure elevation. It can also cause platelet aggregation and coagulation. These physiological adverse effects, which have been demonstrated in animal and human models, could potentially result in increased risks of myocardial infarction, ischemic stroke, and arrhythmia. Evidence on the effect of tramadol and cardiovascular safety is limited and requires further investigation. In this thesis, I conducted a retrospective, population-based cohort study to examine the rates of myocardial infarction, unstable angina, coronary revascularization, ischemic stroke, cardiovascular death, and all-cause mortality with the use of tramadol compared to those with the use of codeine among patients with non-cancer pain. Using data from the United Kingdom's Clinical Practice Research Datalink (CPRD), linked to hospitalization and vital statistics data, I identified new users of tramadol or codeine who were 18 years or older with at least one year of enrolment in the CPRD database prior to cohort entry. Cohort entry was defined by the date of new prescription of either tramadol or codeine, with exposure defined using an approach analogous to an intention-to-treat. Hazard ratio (HR) and corresponding 95% confidence interval (CI) were estimated using Cox Proportional hazards models, adjusted for high-dimensional propensity score to minimize potential confounding. Our final cohort included 1,037,727 new users (123,394 tramadol and 914,333 codeine) from April 1st, 1998 to March 31st, 2017. Most baseline characteristics were similar between the tramadol and codeine groups (standardized differences < 0.1). The mean age at cohort entry was 54.4+/-17.7 years for the tramadol group and 52.4+/-19.0 years for the codeine group. Compared with the use of codeine, the use of tramadol was not associated with an increased risk of myocardial infarction (adjusted HR: 1.003, 95% CI: 0.81, 1.24). There was also no evidence of increased risk of the secondary outcomes of unstable angina, ischemic stroke, coronary revascularization, cardiovascular death, and all-cause mortality. The use of tramadol was not found to increase risk of myocardial infarction and other atherosclerotic events compared with the use of codeine. Nonetheless, prescriptions for both medications should be used judiciously based on the risks and benefits of current treatment in the presence of the ongoing opioid epidemic. Future studies are required to further investigate the association of arrhythmia and sudden cardiac death due to tramadol's effect on the QT interval and its propensity for serotonin syndrome.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".