Is opioid therapy for chronic non‐cancer pain associated with a greater risk of all‐cause mortality compared to non‐opioid analgesics? A systematic review of propensity score matched observational studies
Bibliographic record
Abstract
BACKGROUND: The many risks associated with opioid therapy for chronic non-cancer pain (CNCP) have led to questions about use. This is particularly relevant for risk of increased mortality. However, underlying medical conditions of those using opioids may influence mortality findings due to confounding by indication. Similarly, non-opioid analgesics are also associated with an increased risk of mortality, too. METHODS: We have conducted a systematic review of propensity score matched observational studies comparing mortality associated with opioid use compared to non-opioid analgesics. Clinicaltrials.gov, Google Scholar, MEDLINE and Scopus were searched from inception to July 2020. Propensity score matched observational studies comparing opioids to non-opioid analgesics in real-world settings were analysed. Primary outcome was pooled adjusted hazard ratio (aHR) of all-cause death. Effects were summarized by a random effects model. RESULTS: Four studies with seven study arms and 120,186 patients were analysed. Pooled aHR for all-cause death was 1.69 (95% confidence interval [CI] 1.47, 1.95). When mortality risk was confined to out-of-hospital deaths, the pooled aHR was 2.12 (95% CI 1.46, 3.09). The most frequent cause of death was cardiovascular death. Before matching, patients with opioids were older and had more somatic diseases than patients with non-opioids. Despite extensive propensity score matchings and sensitivity analyses, all studies could not fully exclude confounding by indication. CONCLUSIONS: Possibly, opioids are associated with an increased all-cause mortality risk compared to non-opioid analgesics. When considering treatment options for patients with CNCP, the possible risk of increased all-cause mortality with opioids should be discussed. SIGNIFICANCE: An increased all-cause mortality associated with opioid use compared to non-opioid analgesics for CNCP was identified by a systematic review of four propensity score matched cohort studies in real-world settings. The number needed to harm for an additional excess death per 10,000 person-years was 116. Despite extensive propensity score matchings and sensitivity analyses, all studies could not fully exclude confounding by indication. The potential risk of increased all-cause mortality with opioids should be discussed with patients when considering opioid treatment.
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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 | 0.019 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.015 |
| Bibliometrics | 0.010 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".