Potential for Drug-Drug Interactions with Adjunctive Tramadol Use in Treatment of Obsessive-Compulsive Disorder
Bibliographic record
Abstract
In an outpatient psychiatry clinic, we recently diagnosed a 69-year-old man with obsessive-compulsive disorder (OCD) who had been prescribed the opioid analgesic tramadol for a chronic pain condition. Mindful that tramadol may interact with selective serotonin reuptake inhibitors (SSRIs), we consulted clinical practice guidelines to review OCD treatment options should switching away from tramadol be impractical. In our review of the Canadian Psychiatric Association (CPA) clinical practice guidelines for anxiety disorders 1 and the more recent Anxiety Disorders Association of Canada (ADAC) guidelines, 2 we were reminded that tramadol itself is a proposed third-line treatment for OCD. In the CPA guidelines, consideration of tramadol as an adjunctive therapy is proposed. 1(p47 S) However, neither guideline mentions potential drug-drug interactions with other OCD treatments. Tramadol is included in OCD treatment guidelines based on an uncontrolled open-label monotherapy trial in 7 treatment-resistant individuals 3 and a single case study of tramadol in combination with fluoxetine. 4 The evidence is graded as level 4 in both guidelines, reflecting its modest quality. The potential for interactions between SSRIs and tramadol arises through both pharmacodynamic and pharmacokinetic factors. 5 Beyond its actions on the opioid system, tramadol blocks serotonin reuptake and increases its basal release. It therefore increases serotonin availability, as do the recommended first-line treatments in OCD, the SSRIs, and other antidepressants considered second-line treatments in OCD, clomipramine and venlafaxine. Since tramadol is usually metabolized rapidly, problematic interactions based on pharmacodynamics alone are theoretically unlikely at standard doses in extensive metabolizers. However, the likelihood of toxicity is increased by pharmacokinetic factors. Tramadol is metabolized in the liver, with the enzyme CYP2D6 playing an important role. 6 CYP2D6 is inhibited
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".