Adverse Effects of Antidepressants for Chronic Pain: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
BACKGROUND: Antidepressants are widely used in the treatment of chronic pain. Applied doses are lower than those needed to unfold an antidepressive effect. While efficacy of antidepressants for chronic pain has been reported in large randomized-controlled trials (RCT), there is inconsistent data on adverse effects and tolerability. We aimed at synthesizing data from RCT to explore adverse effect profiles and tolerability of antidepressants for treatment of chronic pain. METHODS: Systematic literature research and meta-analyses were performed regarding side effects and safety of different antidepressants in the treatment of chronic pain according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. The National Center for Biotechnology Information library and MEDLINE were searched. Randomized placebo-controlled trials were included in quantitative data synthesis. RESULTS: Out of 1,975 screened articles, 33 papers published between 1995 and 2015 were included in our review and 23 studies were included in the meta-analyses. A higher risk for adverse effects compared to placebo was observed in all antidepressants included in our analyses, except nortriptyline. The most prevalent adverse effects were dry mouth, dizziness, nausea, headache, and constipation. Amitriptyline, mirtazapine, desipramine, venlafaxine, fluoxetine, and nortriptyline showed the highest placebo effect-adjusted risk of adverse effects. Risk for withdrawal due to adverse effects was highest in desipramine (risk ratio: 4.09, 95%-confidence interval [1.31; 12.82]) followed by milnacipran, venlafaxine, and duloxetine. The most common adverse effects under treatment with antidepressants were dry mouth, dizziness, nausea, headache, and constipation followed by palpitations, sweating, and drowsiness. However, overall tolerability was high. Each antidepressant showed distinct risk profiles of adverse effects. CONCLUSION: Our synthesized data analysis confirmed overall tolerability of low-dose antidepressants for the treatment of chronic pain and revealed drug specific risk profiles. This encompassing characterization of adverse effect profiles might be useful in defining multimodal treatment regimens for chronic pain which also consider patients' comorbidities and co-medication.
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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 | 0.017 | 0.039 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.047 |
| Bibliometrics | 0.009 | 0.009 |
| 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".