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

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 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.

A Novel Algorithm to Identify Predictors of Treatment Response: Tapentadol Monotherapy or Tapentadol/Pregabalin Combination Therapy in Chronic Low Back Pain?

Purpose: High dose monotherapies or drug combinations are used to achieve sufficient analgesia for the treatment of severe chronic low back pain, before invasive therapy options are considered. In order to demonstrate an alternative for an empirical treatment approach, the authors' primary aim was to present an algorithm for the objective identification of treatment predictors. Additionally, the study identified baseline-characteristics in chronic low back pain patients prior to tapentadol PR treatment, as well as scrutinized those patients, either benefitting from a medium/high dose tapentadol PR monotherapy or a combination therapy (medium dose tapentadol PR + pregabalin). Patients and Methods: The statistical approach included data of a previously published randomized, double blind, phase 3b study which compared the effectiveness and safety of tapentadol PR vs. a combination of tapentadol PR and pregabalin. In total, 46 clinical parameters were included in the statistical prediction models which were applied separately either to 50 patients who already responded well during the titration period (i.e., medium dose tapentadol PR) or to 261 patients with in the comparative treatment period [i.e., monotherapy (high dose tapentadol PR) or combination therapy (medium dose tapentadol PR/pregabalin)]. Results: The first statistical model identified three co-variables (NRS-3, PDQ, SQ) with predictive effects on patients responding well ("optimal responders") to a medium dose tapentadol PR titration. Those patients presented low baseline pain intensity scores, good sleep quality and high painDETECT scores. The second statistical model identified eight co-variables (PDQ, numbness, SF-12 MCS, SF-12 PCS, VAS, HADS-A, HADS-D, SQ) with predictive effects on patients responding to high dose tapentadol PR monotherapy vs. a combination therapy (tapentadol PR + pregabalin). The high dose tapentadol PR responders indicated high painDETECT scores, little numbness and a good mental health status. Whereas, the combination therapy (tapentadol PR + pregabalin) responders were characterized by severe sleep disturbances and little anxiety. Conclusion: The statistical analysis characterized chronic low back pain patients and identified factors contributing to a treatment response. Thus, this retrospective statistical algorithm represents an elegant method, which may contribute to future strategies toward a more individualized and improved mechanism based pain therapy.