The Parkinson disease pain classification system: Results from an international mechanism-based classification approach

Pain is a common nonmotor symptom in patients with Parkinson disease (PD) but the correct diagnosis of the respective cause remains difficult because suitable tools are lacking, so far. We developed a framework to differentiate PD- from non-PD-related pain and classify PD-related pain into 3 groups based on validated mechanistic pain descriptors (nociceptive, neuropathic, or nociplastic), which encompass all the previously described PD pain types. Severity of PD-related pain syndromes was scored by ratings of intensity, frequency, and interference with daily living activities. The PD-Pain Classification System (PD-PCS) was compared with classic pain measures (ie, brief pain inventory and McGill pain questionnaire [MPQ], PDQ-8 quality of life score, MDS-UPDRS scores, and nonmotor symptoms). 159 nondemented PD patients (disease duration 10.2 6 7.6 years) and 37 healthy controls were recruited in 4 centers. PDrelated pain was present in 122 patients (77%), with 24 (15%) suffering one or more syndromes at the same time. PD-related nociceptive, neuropathic, or nociplastic pain was diagnosed in 87 (55%), 25 (16%), or 35 (22%), respectively. Pain unrelated to PD was present in 35 (22%) patients. Overall, PD-PCS severity score significantly correlated with pain’s Brief Pain Inventory and MPQ ratings, presence of dyskinesia and motor fluctuations, PDQ-8 scores, depression, and anxiety measures. Moderate intrarater and interrater reliability was observed. The PD-PCS is a valid and reliable tool for differentiating PD-related pain from PD-unrelated pain. It detects and scores mechanistic pain subtypes in a pragmatic and treatment-oriented approach, unifying previous classifications of PD-pain.Fil: Mylius, Veit. Universitat Phillips; Alemania. Center for Neurorehabilitation; Suiza. Kantonsspital St; SuizaFil: Perez Lloret, Santiago. Universidad Abierta Interamericana. Secretaría de Investigación. Centro de Altos Estudios En Ciencias Humanas y de la Salud - Sede Buenos Aires.; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Pontificia Universidad Católica Argentina "Santa María de los Buenos Aires"; ArgentinaFil: Cury, Rubens G.. Universidade de Sao Paulo; BrasilFil: Teixeira, Manoel J.. Universidade de Sao Paulo; BrasilFil: Barbosa, Victor R.. Universidade de Sao Paulo; BrasilFil: Barbosa, Egberto R.. Universidade de Sao Paulo; BrasilFil: Moreira, Larissa I.. Universidade de Sao Paulo; BrasilFil: Listik, Clarice. Universidade de Sao Paulo; BrasilFil: Fernandes, Ana M.. Universidade de Sao Paulo; BrasilFil: de Lacerda Veiga, Diogo. Universidade de Sao Paulo; BrasilFil: Barbour, Julio. Universidade de Sao Paulo; BrasilFil: Hollenstein, Nathalie. Universidade de Sao Paulo; BrasilFil: Oechsner, Matthias. Center for Neurological Rehabilitation; SuizaFil: Walch, Julia. Kantonsspital St; SuizaFil: Brugger, Florian. Kantonsspital St; SuizaFil: Hägele Link, Stefan. Kantonsspital St; SuizaFil: Beer, Serafin. Center for Neurorehabilitation; SuizaFil: Rizos, Alexandra. King's College Hospital; Reino UnidoFil: Chaudhuri, Kallol Ray. The Maurice Wohl Clinical Neuroscience Institute; Reino Unido. King's College Hospital; Reino UnidoFil: Bouhassira, Didier. Université Versailles-Saint-Quentin; Francia. Hôpital Ambroise Paré; FranciaFil: Lefaucheur, Jean Pascal. Université Paris-Est-Créteil; FranciaFil: Timmermann, Lars. Universitat Phillips; AlemaniaFil: Gonzenbach, Roman. Center for Neurorehabilitation; SuizaFil: Kägi, Georg. Kantonsspital St; SuizaFil: Möller, Jens Carsten. Universitat Phillips; Alemania. Center for Neurological Rehabilitation; SuizaFil: Ciampi de Andrade, Daniel. Universidade de Sao Paulo; Brasi

Clinical application of robotics and technology in restoration of walking

Second editionRobots for neurorehabilitation have been designed principally to automate repetitive labor-intensive training and to support therapists and patients during different stages of rehabilitation. Devices designed for body weight-supported treadmill training are promising task-oriented tools intended to assist in the restoration of gait. In early rehabilitation, robots provide a safe environment through the use of a suspension harness and assistance in achieving a more physiological gait pattern while promoting a high number of repetitions. In the later stages of rehabilitation, more sophisticated control strategies, virtual environment scenarios, or the possibility to address specific gait deficits by modulating different parameters extends their application. Scientific and clinical evidence for the effectiveness, safety, and tolerability of these devices exists; however documentation of their comparative advantages to conventional therapies is limited. This might be due to the lack of appropriate selection parameters of locomotor training interventions based on functional impairments. Despite this shortcoming, robotic devices are being integrated into clinical settings with promising results. Appropriate use is dependent on the clinicians’ knowledge of different robotic devices as well as the ability to utilize the devices’ technical features, thereby allowing patients to benefit from robot-aided gait training throughout the rehabilitation continuum with the ultimate goal of safe and efficient overground walking. This chapter will provide an overview on the rationales of introducing robots into the clinic and discuss their value in various neurological diagnoses. In addition, recommendations for goal setting and practice of robot-assisted training based on disease- related symptoms and functional impairment are summarized

The Parkinson`s disease pain classification system (PDPCS): results from an international mechanism-based classification approach

Abstract: Pain is a common non-motor symptom in patients with Parkinson’s disease (PD) but the correct diagnosis of the respective cause remains difficult because suitable tools are lacking, so far. We developed a framework to differentiate PD- from non-PD-related pain and classify PD-related pain into three groups based on validated mechanistic pain descriptors (nociceptive, neuropathic, or nociplastic), which encompasses the previously described PD pain types. Severity of PD-related pain syndromes was scored by ratings of intensity, frequency, and interference with daily living activities. The PD-Pain Classification System (PD-PCS) was compared with classic pain measures (ie, brief pain inventory (BPI) and McGill pain questionnaire (MPQ), PDQ-8 quality of life score, MDS-UPDRS scores, and non-motor symptoms). 159 non-demented PD patients (disease duration 10.2±7.6 years) and 37 healthy controls were recruited in four centers. PD-related pain was present in 122 patients (77%), with 24 (15%) suffering one or more syndromes at the same time. PD-related nociceptive, neuropathic, or nociplastic pain was diagnosed in 87 (55%), 25 (16%), or 35 (22%), respectively. Pain unrelated to PD was present in 35 (22%) patients. Overall, PD-PCS severity score significantly correlated with pain’s BPI and MPQ ratings, presence of dyskinesia and motor fluctuations, PDQ-8 scores, depression and anxiety measures. Moderate intra- and inter-rater reliability were observed. The PD-PCS is a valid and reliable tool for differentiating PD-related pain from PD-unrelated pain. It detects and scores mechanistic pain subtypes in a pragmatic and treatment-oriented approach, unifying previous classifications of PD-pain