The effect of nonsteroidal anti-inflammatory medications on pain chronification
Bibliographic record
Abstract
Background and objective: Analgesics are among the most commonly used over-the-counter medications and NSAIDs or acetaminophen are used as the first-line treatment for chronic pain in Canada. While their pain relief efficacy has been studied, the longer-term effect of taking these medications on pain outcomes, particularly on the transition from acute to chronic pain, remains unclear. We have previously shown that the risk of chronification for acute back pain is particularly enhanced by taking NSAIDs during the acute phase, explained by the dampening of pain resolution through an inflammatory response. To determine the generalizability of the chronification effect of NSAIDs, we investigated the effect of a range of analgesics on body-site specific pain in the Canadian Longitudinal Study on Aging (CLSA) including back pain. Significant findings were tested for replication in another cohort, the UK Biobank (UKB).Design and Methods: Based on the CLSA Comprehensive cohort of 27,765 individuals using both baseline and first follow-up (FU1) data (3 years interval), analyses were conducted on back pain, jaw pain, and knee pain. Individuals at baseline were asked about their experience of pain at each site; for back pain and jaw pain, the referral period was the prior 12 months, while for knee pain, this was during the last 4 weeks and on most of the days, (5,323/2,215/4,862) responded “Yes” respectively. Each pain type was analyzed in separate logistic regression models with site-specific pain as the outcome. Cases were defined as those with site-specific pain at baseline still reporting pain at the same site at FU1(3 years interval) (N= 2,957/946/2,517) and controls as those who had recovered (no pain) (N= 2,366/1,269/2,345). In this study, chronic pain is defined as site-specific pain present at both baseline and FU1. We considered five analgesic classes (NSAIDs, paracetamol, opioids, anti-depressants, and gabapentinoids) as predictors in logistic regression models for each site-specific pain. We tested for association between taking medications and the development of chronic pain, adjusting for age, sex, ethnicity, intensity of pain, and BMI.We used the nominal p-value threshold of 0.05 to define statistical significance in the CLSA and tested significant findings for replication in the UKB. Specifically, knee pain models were tested for replication. Cases and controls were defined for the CLSA: 7,110 UKB subjects with knee pain who answered “Yes” for having pain that interfered with their usual activities in the last month were included. Individuals who reported knee pain at any of the next visits were considered as cases (3,331), while others who did not report any pain, were considered as controls (recovered) (3,779).Results: In a full model including all medication classes, chronic back pain showed a strong association with taking analgesics for all classes. Back pain subjects taking NSAIDs are at 1.29 times greater risk of developing chronic pain than those not taking NSAIDs (OR = 1.29; P = 0.0035). For knee pain patients, NSAIDs (and no other class) were identified as a risk factor for developing chronic knee pain (OR = 1.35; P = 0.0004). For jaw pain patients, the number of cases was very small. Opioids and antidepressants are associated with chronicity. Replication of knee pain results in the UKB showed that NSAIDs (and no other class) were identified as significant in the full model (OR = 1.15; P = 0.01).Conclusion: Individuals taking NSAIDs for pain are at a higher risk of having chronic pain 2-3 years later, compared to individuals taking other analgesics. These results imply that the detrimental effect of NSAIDs on pain chronicity is independent of reported pain bodily site and stage of pain. Modifications to NSAID indications are warranted
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.004 | 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".