MétaCan
Menu
← Back to cohort

POS1356 ASSESSMENT OF THE EFFICACY OF COMBINATION OF ORAL ACETAMINOPHEN AND TOPICAL DICLOFENAC IN OSTEOARTHRITIS PAIN: INSIGHTS FROM A MODEL-BASED META-ANALYSIS

2023· article· en· W4379798706 on OpenAlexaboutno aff
Vivek Sood, Liang Qin, Eline G. M. Cox, Iñaki F. Trocóniz, Oscar Della Pasqua

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicPain Mechanisms and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineAcetaminophenDiclofenacOsteoarthritisClinical trialAnalgesicRandomized controlled trialCombination therapyAnesthesiaInternal medicineAlternative medicine

Abstract

fetched live from OpenAlex

Background Osteoarthritis (OA) is a major cause of chronic pain and disability in older adults and currently affects approximately 300 million people worldwide [1]. In the absence of curative therapy, symptomatic drugs comprise the backbone of pain management in OA. However, acetaminophen provides inadequate relief and oral non-steroidal anti-inflammatory drugs (NSAIDs) exhibit significant gastrointestinal and cardiovascular toxicity which prohibit their long-term use in the elderly [2,3]. Although opioids can be an effective alternative in patients experiencing insufficient pain relief with other analgesics, concerns have been raised about the risk of side effects, addiction, and overdose deaths [4]. Therefore, there is a significant unmet need for effective and well-tolerated treatments. Acetaminophen and topical diclofenac exhibit complementary mechanisms of action targeting pain and inflammation, respectively, and are therefore attractive candidates for use in combination analgesia in OA pain [5,6,7]. Although ample clinical evidence exists on the monotherapy of acetaminophen or topical diclofenac in OA, there is a data gap for evidence on their combination. Objectives The present study aims to assess the effect of the combination of acetaminophen and topical diclofenac in OA and compare its performance to acetaminophen and diclofenac monotherapy using a model-based meta-analysis (MBMA) leveraging published summary-level data on the combination from OA as well as other acute pain indications [8]. Methods Randomized controlled trials (RCTs) investigating the combination of acetaminophen and diclofenac in OA and acute pain settings were identified through systematic literature searches. MBMA was implemented to infer the efficacy of the combination in the population of interest. Pain score reduction on numerical rating scale (NRS), visual analogue scale (VAS) and Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) pain subscale along with opioid sparing effect (defined as reduced opioid dose without loss of analgesic efficacy) were selected as the clinical endpoints. Results In the absence of RCTs on the combination in OA, MBMA was implemented in conjunction with extrapolation principles on trials in acute pain setting (11 RCTs, n=1396 patients). The combination demonstrated greater reduction in pain scores versus acetaminophen monotherapy in 8 of the 11 RCTs. Moreover, a parsimonious MBMA was developed on 5 RCTs allowing PCA, which revealed a statistically significant 32% lesser opioid use with the combination than with acetaminophen monotherapy (Figure 1). However, the combination effect was less conclusive versus diclofenac monotherapy. Conclusion The current analysis demonstrates greater pain reduction and opioid sparing efficacy for the combination versus acetaminophen monotherapy in the treatment of acute pain. Considering the overlap in pain transmission pathways between acute and chronic OA pain, the combination may be anticipated to exhibit similar performance on extrapolation to chronic OA pain. Overall, our research tries to bridge the gap in pharmacological and clinical evidence supporting the use of combination of acetaminophen and topical diclofenac in mild-to-moderate OA pain. References [1]GBD 2017 Disease and Injury Incidence and Prevalence Collaborators. Lancet. 2018;392(10159):1789-1858. [2]Bannuru, R. Osteoarthritis and Cartilage. 2010;(18), S250 [3]Cooper, C. Drugs Aging. 2019;36(Suppl 1), 15-24 [4]Deveza, LA. Osteoarthritis Cartilage. 2018;26(3):293-295. [5]Altman, RD. J Rheumatol. 2004;31(1):5-7. [6]Anderson, BJ. Paediatr Anaesth. 2008;18(10), 915-921 [7]Shah, S. Postgrad Med J. 2012; 88(1036), 73-78. [8]Mandema, JW. Clin Pharmacol Ther. 2011;90(6):766-769. Acknowledgements This study was funded by Haleon. Disclosure of Interests Vidhu Sood Employee of: Haleon, Li Qin: None declared, Eugène Cox: None declared, Iñaki Trocóniz: None declared, Oscar Della Pasqua Employee of: GlaxoSmithKline.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.022
metaresearch head score (Gemma)0.031
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.022
Threshold uncertainty score0.114

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0220.031
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0120.051
Bibliometrics0.0030.004
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0030.002
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0120.001

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.

Opus teacher head0.052
GPT teacher head0.317
Teacher spread0.266 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designMeta-analysis
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

Explore more

Same topicPain Mechanisms and Treatments→French-language works237,207→