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- Gonzalez-Robles, Cristina, et al.
(author)
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Embedding Patient Input in Outcome Measures for Long-Term Disease-Modifying Parkinson Disease Trials
- 2024
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In: Movement Disorders. - 0885-3185 .- 1531-8257. ; 39:2, s. 433-438
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Journal article (peer-reviewed)abstract
- Background: Clinical trials of disease-modifying therapies in PD require valid and responsive primary outcome measures that are relevant to patients. Objectives: The objective is to select a patient-centered primary outcome measure for disease-modification trials over three or more years. Methods: Experts in Parkinson's disease (PD), statistics, and health economics and patient and public involvement and engagement (PPIE) representatives reviewed and discussed potential outcome measures. A larger PPIE group provided input on their key considerations for such an endpoint. Feasibility, clinimetric properties, and relevance to patients were assessed and synthesized. Results: Although initial considerations favored the Movement Disorder Society-sponsored revision of the Unified Parkinson's Disease Rating Scale (MDS-UPDRS) Part III in Off, feasibility, PPIE input, and clinimetric properties supported the MDS-UPDRS Part II. However, PPIE input also highlighted the importance of nonmotor symptoms, especially in the longer term, leading to the selection of the MDS-UPDRS Parts I + II sum score. Conclusions: The MDS-UPDRS Parts I + II sum score was chosen as the primary outcome for large 3-year disease-modification trials. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.
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- Gonzalez-Robles, Cristina, et al.
(author)
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Outcome Measures for Disease-Modifying Trials in Parkinson's Disease: Consensus Paper by the EJS ACT-PD Multi-Arm Multi-Stage Trial Initiative
- 2023
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In: JOURNAL OF PARKINSONS DISEASE. - 1877-7171 .- 1877-718X. ; 13:6, s. 1013-1035
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Journal article (peer-reviewed)abstract
- Background: Multi-arm, multi-stage (MAMS) platform trials can accelerate the identification of disease-modifying treatments for Parkinson's disease (PD) but there is no current consensus on the optimal outcome measures (OM) for this approach. Objective: To provide an up-to-date inventory of OM for disease-modifying PD trials, and a framework for future selection of OM for such trials. Methods: As part of the Edmond J Safra Accelerating Clinical Trials in Parkinson Disease (EJS ACT-PD) initiative, an expert group with Patient and Public Involvement and Engagement (PPIE) representatives' input reviewed and evaluated available evidence on OM for potential use in trials to delay progression of PD. Each OM was ranked based on aspects such as validity, sensitivity to change, participant burden and practicality for a multi-site trial. Review of evidence and expert opinion led to the present inventory. Results: An extensive inventory ofOMwas created, divided into: general, motor and non-motor scales, diaries and fluctuation questionnaires, cognitive, disability and health-related quality of life, capability, quantitative motor, wearable and digital, combined, resource use, imaging and wet biomarkers, and milestone-based. A framework for evaluation of OM is presented to update the inventory in the future. PPIE input highlighted the need for OM which reflect their experience of disease progression and are applicable to diverse populations and disease stages. Conclusion: We present a range of OM, classified according to a transparent framework, to aid selection of OM for disease-modifying PD trials, whilst allowing for inclusion or re-classification of relevant OM as new evidence emerges.
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- Martinez-Martin, Pablo, et al.
(author)
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Relationship of Nocturnal Sleep Dysfunction and Pain Subtypes in Parkinson's Disease
- 2019
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In: Movement Disorders Clinical Practice. - : Wiley. - 2330-1619. ; 6:1, s. 57-64
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Journal article (peer-reviewed)abstract
- Background: Little research has been conducted regarding the relationship between sleep disorders and different pain types in Parkinson's disease (PD). Objective: To explore the influence of the various pain subtypes experienced by PD patients on sleep. Methods: Three hundred consecutive PD patients were assessed with the PD Sleep Scale-Version 2 (PDSS-2), King's PD Pain Scale (KPPS), King's PD Pain Questionnaire (KPPQ), Visual Analog Scales for Pain (VAS-Pain), and Hospital Anxiety and Depression Scale. Results: According to the PDSS-2, 99.3% of our sample suffered from at least one sleep issue. Those who reported experiencing any modality of pain suffered significantly more from sleep disorders than those who did not (all, P < 0.003). The PDSS-2 showed moderate-to-high correlations with the KPPS (rS = 0.57), KPPQ (0.57), and VAS-Pain (0.35). When PDSS-2 items 10 to 12 (pain-related) were excluded, the correlation values decreased to 0.50, 0.51, and 0.28, respectively, while these items showed moderate-to-high correlations with KPPS (0.56), KPPQ (0.54), and VAS-Pain (0.42). Among the variables analyzed, multiple linear regression models suggested that KPPS and KPPQ were the most relevant predictors of sleep disorders (as per the PDSS-2), although following exclusion of PDSS-2 pain items, depression was the relevant predictor. Depression and anxiety were the most relevant predictors in the analysis involving the VAS-Pain. Regression analysis, considering only the KPPS domains, showed that nocturnal and musculoskeletal pains were the best predictors of overall nocturnal sleep disorder. Conclusions: Pain showed a moderate association with nocturnal sleep dysfunction in PD. Some pain subtypes had a greater effect on sleep than others.
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- van Wamelen, Daniel J., et al.
(author)
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Digital health technology for non-motor symptoms in people with Parkinson's disease : Futile or future?
- 2021
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In: Parkinsonism and Related Disorders. - : Elsevier BV. - 1353-8020. ; 89, s. 186-194
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Research review (peer-reviewed)abstract
- Introduction: There is an ongoing digital revolution in the field of Parkinson's disease (PD) for the objective measurement of motor aspects, to be used in clinical trials and possibly support therapeutic choices. The focus of remote technologies is now also slowly shifting towards the broad but more “hidden” spectrum of non-motor symptoms (NMS). Methods: A narrative review of digital health technologies for measuring NMS in people with PD was conducted. These digital technologies were defined as assessment tools for NMS offered remotely in the form of a wearable, downloadable as a mobile app, or any other objective measurement of NMS in PD that did not require a hospital visit and could be performed remotely. Searches were performed using peer-reviewed literature indexed databases (MEDLINE, Embase, PsycINFO, Cochrane Database of Systematic Reviews, Cochrane CENTRAL Register of Controlled Trials), as well as Google and Google Scholar. Results: Eighteen studies deploying digital health technology in PD were identified, for example for the measurement of sleep disorders, cognitive dysfunction and orthostatic hypotension. In addition, we describe promising developments in other conditions that could be translated for use in PD. Conclusion: Unlike motor symptoms, non-motor features of PD are difficult to measure directly using remote digital technologies. Nonetheless, it is currently possible to reliably measure several NMS and further digital technology developments are underway to offer further capture of often under-reported and under-recognised NMS.
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