Pain Trajectories in Knee Osteoarthritis-A Systematic Review and Best Evidence Synthesis on Pain Predictors

Davide Previtali, Luca Andriolo, Giorgio Di Laura Frattura, Angelo Boffa, Christian Candrian, Stefano Zaffagnini, Giuseppe Filardo

Research output: Contribution to journalReview articlepeer-review


Different profiles of pain progression have been reported in patients with knee osteoarthritis (OA), but the determinants of this heterogeneity are still to be sought. The aim of this systematic review was to analyze all studies providing information about knee OA pain trajectories to delineate, according to patients' characteristics, an evidence-based evolution pattern of this disabling disease, which is key for a more personalized and effective management of knee OA. A literature search was performed on PubMed, Web of Science, Cochrane Library, and grey literature databases. The Cochrane Collaboration's tool for assessing risk of bias was used, and a best-evidence synthesis was performed to define the predictors of pain evolution. Seven articles on 7747 patients affected by knee OA (mainly early/moderate) were included. Daily knee OA pain trajectories were unstable in almost half of the patients. In the mid-term, knee OA had a steady pain trajectory in 85% of the patients, 8% experienced pain reduction, while 7% experienced pain worsening. Low education, comorbidities, and depression were patient-related predictors of severe/worsening knee OA pain. Conversely, age, alcohol, smoking, pain coping strategies, and medications were unrelated to pain evolution. Conflicting/no evidence was found for all joint-related factors, such as baseline radiographic severity.

Original languageEnglish
Pages (from-to)1-13
Number of pages13
JournalJournal of Clinical Medicine
Issue number9
Publication statusPublished - Sep 1 2020


  • evolution
  • knee
  • osteoarthritis
  • pain
  • predictors
  • trajectories


Dive into the research topics of 'Pain Trajectories in Knee Osteoarthritis-A Systematic Review and Best Evidence Synthesis on Pain Predictors'. Together they form a unique fingerprint.

Cite this