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Anti\u2010inflammatory therapy is recommended, but efficacy varies among patients. This study aimed to utilise machine learning to predict the analgesic responses of 3\u2010week NSAID plus paracetamol therapy using pre\u2010treatment assessments of pain sensitivity, inflammation, microRNA, and psychological factors.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      Patients (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u2009101) underwent 3\u2010week combined NSAID plus paracetamol therapy. Pain sensitivity using cuff algometry, Hospital Anxiety and Depression Scale, Pain Catastrophizing Scale, EQ\u20105D\u20103L 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\u2010Generation RNA Sequencing. Data Integration Analysis for Biomarker discovery using Latent cOmponents (DIABLO) was utilised to integrate the pre\u2010treatment data and explain the analgesic effect.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>DIABLO model identified 30 significant variables across the 4 domains. After cross\u2010validation, model performance showed an area under the precision\u2010recall curve of 85%, sensitivity of 83%, specificity of 87%, and balanced accuracy of 85%.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>This 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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Significance Statement<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Trial Registration<\/jats:title>\n                    <jats:p>\n                      <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"http:\/\/clinicaltrials.gov\">ClinicalTrials.gov<\/jats:ext-link>\n                      identifier: NCT02967744\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/ejp.70140","type":"journal-article","created":{"date-parts":[[2025,9,29]],"date-time":"2025-09-29T13:31:13Z","timestamp":1759152673000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Multifactorial Machine Learning Algorithm Integration of Pain Mechanisms Can Predict the Efficacy of 3\u2010Week\n                    <scp>NSAID<\/scp>\n                    Plus Paracetamol in Patients With Painful Knee Osteoarthritis"],"prefix":"10.1002","volume":"29","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1331-129X","authenticated-orcid":false,"given":"Rocco","family":"Giordano","sequence":"first","affiliation":[{"name":"Center for Neuroplasticity and Pain (CNAP), Department of Health Science and Technology Aalborg University  Aalborg 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