Development of a bedside tool kit for assessing sensitization in patients with chronic osteoarthritis knee pain or chronic knee pain after total knee replacement

Juliane Sachau, Jan Carl Otto, Viktoria Kirchhofer, Jesper Bie Larsen, Lieven Nils Kennes, Philipp Hüllemann, Lars Arendt-Nielsen, Ralf Baron

Research output: Contribution to journalJournal articleResearchpeer-review

Abstract

Different pathophysiological mechanisms contribute to the pain development in osteoarthritis (OA). Sensitization mechanisms play an important role in the amplification and chronification of pain and may predict the therapeutic outcome. Stratification of patients according to their pain mechanisms could help to target pain therapy. This study aimed at developing an easy-to-use, bedside tool-kit to assess sensitization in patients with chronic painful knee OA or chronic pain after total knee replacement (TKR). In total, 100 patients were examined at the most affected knee and extra-segmentally by use of four standardized quantitative sensory testing parameters reflecting sensitization (mechanical pain threshold, mechanical pain sensitivity, dynamic mechanical allodynia, pressure pain threshold), a bedside testing battery of equivalent parameters including also temporal summation and conditioned pain modulation, and pain questionnaires. Machine learning techniques were applied to identify an appropriate set of bedside screening tools. Approximately half of the patients showed signs of sensitization (46%). Based on machine learning techniques a composition of tests consisting of three modalities were developed. The most adequate bedside tools to detect sensitization were pressure pain sensitivity (pain intensity at 4 ml pressure using a 10 ml blunted syringe), mechanical pinprick pain sensitivity (pain intensity of a 0.7 mm nylon-filament) over the most affected knee, and extra-segmental pressure pain sensitivity (pain threshold). This pilot study presents a first attempt to develop an easy-to-use bedside test to probe sensitization in patients with chronic OA knee pain or chronic pain after TKR. This tool may be used to optimize individualized, mechanism-based pain therapy. Corresponding author: Juliane Sachau, Sektion Neurologische Schmerzforschung und -therapie, Klinik für Neurologie, Universitätsklinikum Schleswig-Holstein, Campus Kiel, Arnold-Heller-Straße 3 (Haus D), 24105 Kiel, Tel: +49 431 500 23920, Fax: +49 431 400 23914, E-mail: juliane.sachau@uksh.de * contributed equally © 2021 International Association for the Study of Pain
Original languageEnglish
JournalPain
ISSN0304-3959
DOIs
Publication statusE-pub ahead of print - 10 May 2021

Keywords

  • osteoarthritis
  • neuropathic pain
  • bed-side tool
  • machine learning
  • sensitization
  • quantitative sensory testing

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