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 extrasegmentally by the use of 4 standardized quantitative sensory testing parameters reflecting sensitization (mechanical pain threshold, mechanical pain sensitivity, dynamic mechanical allodynia, and 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 3 modalities was 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 extrasegmental 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.
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