Pain Pharmacotherapy in Patients with Inflammatory Arthritis and Concurrent Cardiovascular or Renal Disease: A Cochrane Systematic Review
Bibliographic record
Abstract
BACKGROUND: Pain in inflammatory arthritis (IA) is common and often multifactorial, and many different pharmacotherapeutic agents are routinely used for pain management. There are concerns that some current pain pharmacotherapies may increase the risk of adverse events in patients with concurrent cardiovascular (CV) or renal disease. METHODS: A systematic literature review was performed searching Medline, Embase, Cochrane Central Register of Controlled Trials, DARE, and Cochrane Database of Systematic Reviews. We also hand-searched conference proceedings for the American College of Rheumatology and the European League Against Rheumatism for 2008-2009. RESULTS: Our search identified 4782 studies, of which 190 were included for detailed review, but none met the inclusion criteria for our review. We identified 1 study of etoricoxib and diclofenac in non-IA populations [osteoarthritis (OA) or mixed OA and rheumatoid arthritis]. In that study, the presence of CV disease increased the likelihood of a further CV event 3-fold. Patients with 2 or more CV risk factors showed a 2-fold increased likelihood of adverse CV events. CONCLUSION: Our review has highlighted a lack of specific evidence to guide clinicians in the management of pain in patients with IA and coexistent CV or renal disease. In the absence of this evidence, we suggest clinicians use nonsteroidal antiinflammatory drugs (NSAID) with caution in patients with preexisting CV disease or ≥ 2 CV risk factors. There is currently no evidence to advise clinicians considering other pain pharmacotherapies in the context of CV comorbidities. Current guidelines regarding the use of NSAID and opioids in moderate to severe renal impairment should also be applied to the IA population.
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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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".