Association of peri‐operative prescription of non‐steroidal anti‐inflammatory drugs with continued prescription of opioids after total knee arthroplasty: a retrospective claims‐based cohort study
Bibliographic record
Abstract
Non-steroidal anti-inflammatory drugs (NSAIDs) are one of the mainstays of multimodal pain management. While effective for acute pain control, recent pre-clinical evidence has raised concerns regarding an association between NSAIDs and chronic pain and potential opioid use. Our objective was to explore the association between peri-operative use of prescription NSAIDs and the need for continued opioid prescriptions lasting 90-180 days in previously opioid-naïve patients undergoing total knee arthroplasty. A database of health claims in the USA was used to identify all opioid-naïve adult patients who underwent primary knee arthroplasty between January 2010 and October 2021. We evaluated the magnitude of association between peri-operative prescription NSAID claims and claims for opioids at 90 days postoperatively using multivariable logistic regression models. Secondary outcomes included: the magnitude of association between peri-operative NSAID prescription and claims for opioids at 180 days postoperatively; and identifying other potential factors associated with opioid claims at 90 days postoperatively. After risk adjustment using multivariable logistic regression models in the 789,736-patient cohort, the adjusted odds ratio (95%CI) for a continuous claim of opioids at 90 and 180 days postoperatively among patients with a peri-operative NSAID prescription within 30 days was 1.32 (1.30-1.35), p < 0.001; and 1.12 (1.10-1.15), p < 0.001, respectively. This estimate of effect remained robust at 90 days after accounting for known potential confounders, including pre-existing knee pain and acute postoperative pain severity. Similar analysis of other pain medications (e.g. paracetamol) did not detect such an association. This population-based cohort study suggests that peri-operative prescription NSAID use may be associated with continued opioid prescription claims at 90 and 180 days after knee arthroplasty, even after adjusting for other observed covariates for continuous opioid claims. These novel findings can inform clinical decision-making for post-surgical pain management, risk-benefit discussions with patients and future research.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".