Patients with the same neuropathic pain disorder may have completely different sensory signs and symptoms yet receive the same medicinal treatment. New concepts suggest that patient stratification according to their pain mechanisms, reflected in their sensory phenotype, could be promising to implement an individualized therapy in neuropathic pain. Retrospective classification of patients according to their sensory phenotype showed predictive validity and reliability for treatment response in certain subgroups of patients. Recent prospective studies using stratification based on sensory phenotypes confirm this concept. In this article, we review the recent accomplishments towards an individualized pharmacological treatment of neuropathic pain.

Introduction: Stratification of patients according to the individual sensory phenotype has been suggested a promising method to identify responders for pain treatment. However, many state-of-the-art sensory testing procedures are expensive or time-consuming. Objectives: Therefore, this study aimed to present a selection of easy-to-use bedside devices. Methods: In total, 73 patients (39 m/34 f) and 20 controls (11 m/9 f) received a standardized laboratory quantitative sensory testing (QST) and a bedside-QST. In addition, 50 patients were tested by a group of nonexperienced investigators to address the impact of training. The sensitivity, specificity, and receiver-operating characteristics were analyzed for each bedside-QST parameter as compared to laboratory QST. Furthermore, the patients' individual sensory phenotype (ie, cluster) was determined using laboratory QST, to select bedside-QST parameters most indicative for a correct cluster allocation. Results: The bedside-QST parameters “loss of cold perception to 22°C metal,” “hypersensitivity towards 45°C metal,” “loss of tactile perception to Q-tip and 0.7 mm CMS hair,” as well as “the allodynia sum score” indicated good sensitivity and specificity (ie, ≳70%). Results of interrater variability indicated that training is necessary for individual parameters (ie, CMS 0.7). For the cluster assessment, the respective bedside quantitative sensory testing (QST) parameter combination indicated the following agreements as compared to laboratory QST stratification: excellent for “sensory loss” (area under the curve [AUC] = 0.91), good for “thermal hyperalgesia” (AUC = 0.83), and fair for “mechanical hyperalgesia” (AUC = 0.75). Conclusion: This study presents a selection of bedside parameters to identify the individual sensory phenotype as cost and time efficient as possible.

Healthy adults and neurological patients show unique mobility patterns over the course of their lifespan and disease. Quantifying these mobility patterns could support diagnosing, tracking disease progression and measuring response to treatment. This quantification can be done with wearable technology, such as inertial measurement units (IMUs). Before IMUs can be used to quantify mobility, algorithms need to be developed and validated with age and disease-specific datasets. This study proposes a protocol for a dataset that can be used to develop and validate IMU-based mobility algorithms for healthy adults (18–60 years), healthy older adults (>60 years), and patients with Parkinson’s disease, multiple sclerosis, a symptomatic stroke and chronic low back pain. All participants will be measured simultaneously with IMUs and a 3D optical motion capture system while performing standardized mobility tasks and non-standardized activities of daily living. Specific clinical scales and questionnaires will be collected. This study aims at building the largest dataset for the development and validation of IMU-based mobility algorithms for healthy adults and neurological patients. It is anticipated to provide this dataset for further research use and collaboration, with the ultimate goal to bring IMU-based mobility algorithms as quickly as possible into clinical trials and clinical routine.

Supplemental Digital Content is Available in the Text.An easy to use bedside test was developed to detect sensitization mechanisms in chronic painful knee osteoarthritis or chronic pain after total knee replacement.

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.