Evidence map›Paper›PMID 41020650›Full record

ArticleEuropean journal of pain (London, England)2025

Multifactorial Machine Learning Algorithm Integration of Pain Mechanisms Can Predict the Efficacy of 3-Week NSAID Plus Paracetamol in Patients With Painful Knee Osteoarthritis.

Rocco Giordano, Lars Arendt-Nielsen, Emma Hertel, Anne Estrup Olesen, Kristian Kjær-Staal Petersen

Registry-linked trialAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in European journal of pain (London, England), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT02967744 (BEVAR), which is not on this map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

NCT02967744 nacompletednot on this map

BEVAR: Patientspecifik Behandling Ved Artrose - Et "Proof-of-concept"- Kvalitetssikringsstudie

TypeinterventionalSponsorAalborg UniversityRan2016 to 2018Enrolled162ConditionsOsteoarthritis,KneeArmsNSAID
3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. The complexity of pain in osteoarthritis.Nature reviews. Rheumatology · 2026
    Review
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Rocco GiordanoCenter for Neuroplasticity and Pain (CNAP), Department of Health Science and Technology, Aalborg University, Aalborg, Denmark.ORCID https://orcid.org/0000-0003-1331-129X
Lars Arendt-NielsenCenter for Neuroplasticity and Pain (CNAP), Department of Health Science and Technology, Aalborg University, Aalborg, Denmark.
Emma HertelCenter for Neuroplasticity and Pain (CNAP), Department of Health Science and Technology, Aalborg University, Aalborg, Denmark.
Anne Estrup OlesenDepartment of Clinical Medicine, Aalborg University, Aalborg, Denmark.
Kristian Kjær-Staal PetersenCenter for Neuroplasticity and Pain (CNAP), Department of Health Science and Technology, Aalborg University, Aalborg, Denmark.ORCID https://orcid.org/0000-0003-4506-000X

Funding

Danish National Research Foundation DNRF121Danish Rheumatism Association A7645Novo Nordisk Foundation NNF21OC0065373
6 · The paper itself

Abstract

backgroundStudies demonstrate that pain sensitization, epigenetic mechanisms, inflammation, and psychological factors might be predictive of treatment outcomes. Anti-inflammatory therapy is recommended, but efficacy varies among patients. This study aimed to utilise machine learning to predict the analgesic responses of 3-week NSAID plus paracetamol therapy using pre-treatment assessments of pain sensitivity, inflammation, microRNA, and psychological factors.

methodsPatients (n = 101) underwent 3-week combined NSAID plus paracetamol therapy. Pain sensitivity using cuff algometry, Hospital Anxiety and Depression Scale, Pain Catastrophizing Scale, EQ-5D-3L scale, and blood samples were collected before therapy. Pain relief was assessed by the Knee Injury and Osteoarthritis Outcome Score pain subscale, before and after therapy. Inflammatory biomarkers were analysed using Olink, and microRNA using Next-Generation RNA Sequencing. Data Integration Analysis for Biomarker discovery using Latent cOmponents (DIABLO) was utilised to integrate the pre-treatment data and explain the analgesic effect.

resultsDIABLO model identified 30 significant variables across the 4 domains. After cross-validation, model performance showed an area under the precision-recall curve of 85%, sensitivity of 83%, specificity of 87%, and balanced accuracy of 85%.

conclusionsThis study utilises a machine learning algorithm, based on pain sensitization, epigenetics, inflammatory response, and psychological factors, to predict analgesic response in osteoarthritis patients. The study demonstrates that incorporating multiple factors into a model enhances its performance, enabling the identification of patients who will benefit from therapy, advancing personalised pain management. SIGNIFICANCE STATEMENT: In this study, a machine learning algorithm, based on pain sensitization, epigenetic mechanisms, inflammatory response, and psychological factors, predicts analgesic response in osteoarthritis patients with 84% accuracy.

trial registrationClinicalTrials.gov identifier: NCT02967744.

Indexed as

AcetaminophenAnalgesics, Non-NarcoticAnti-Inflammatory Agents, Non-SteroidalMachine LearningOsteoarthritis, KneeAgedAlgorithmsDrug Therapy, CombinationFemaleHumansMaleMiddle AgedPain MeasurementTreatment OutcomeAcetaminophenAnalgesics, Non-NarcoticAnti-Inflammatory Agents, Non-Steroidal

Identifiers

PMID41020650
PMCPMC12478294

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Registered trials

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.