A Literature Review of Cannabis and Myocardial Infarction—What Clinicians May Not Be Aware Of
Bibliographic record
Abstract
Increasing legalization and expanding medicinal use have led to a significant rise in global cannabis consumption. With this development, we have seen a growing number of case reports describing adverse cardiovascular events, specifically, cannabis-induced myocardial infarction (MI). However, there are considerable knowledge gaps on this topic among health care providers. This review aims to provide an up-to-date review of the current literature, as well as practical recommendations for clinicians. We also focus on proposed mechanisms implicating cannabis as a risk factor for MI. We performed a comprehensive literature search using the MEDLINE, Cochrane, Cumulative Index to Nursing and Allied Health Literature (CINAHL), and Turning Research into Practice (TRIP) PRO databases for articles published between 2000 and 2018. A total of 92 articles were included. We found a significant number of reports describing cannabis-induced MI. This was especially prevalent among young healthy patients, presenting shortly after use. The most commonly proposed mechanisms included increased autonomic stimulation, altered platelet function, vasospasm, and direct toxic effects of smoke constituents. However, it is likely that the true pathogenesis is multifactorial. We should increase our pretest probability for MI in young patients presenting with chest pain. We also recommend against cannabis use in patients with known coronary artery disease, especially if they have stable angina. Finally, if patients are adamant about using cannabis, health care providers should recommend against smoking cannabis, avoidance of concomitant tobacco use, and use of the lowest delta-9-tetrahydrocannabinol dose possible. Data quality is limited to that of observational studies and case report data. Therefore, more clinical trials are needed to determine a definitive cause-and-effect relationship.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.015 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".