Background: Understanding the regional needs and available healthcare resources to treat Parkinson’s disease (PD) is essential to plan appropriate future priorities. The International Parkinson and Movement Disorder Society (MDS) Task Force for the Middle East was established to raise awareness and promote education across the region on PD and other movement disorders. Broadly, the task force encompasses the countries of the Middle East but has included North Africa and South Asia as well (MENASA). Objective: To create a list of needs and priorities in the advancement of PD in MENASA countries based on consensuses generated by the MDS task force for the Middle East. Methods: A Strengths Weaknesses-Opportunities-Threats (SWOT) analysis was conducted by the task force members to generate consensus about PD care this region. Results: Eight overarching principles emerged for the consensus statement on current needs: more movement disorders specialists, multidisciplinary care, accurate epidemiologic data, educational programs, availability of drugs, and availability of more advanced therapy, enhanced health care resources and infrastructure, and greater levels of awareness within the general population and among health care professionals. Conclusion: This pilot study sheds light on unmet needs for providing care to people with PD in the MENASA region. These data offer directions on priorities to increase awareness of PD, to develop better infrastructure for research and management of PD, to foster healthcare policy discussions for PD and to provide educational opportunities within these countries.
Background The Movement Disorder Society‐Unified Parkinson's Disease Rating Scale (MDS‐UPDRS) has become the gold standard for evaluating different domains in Parkinson's disease (PD), and it is commonly used in clinical practice, research, and clinical trials. Objectives The objectives are to validate the Arabic‐translated version of the MDS‐UPDRS and to assess its factor structure compared with the English version. Methods The study was carried out in three phases: first, the English version of the MDS‐UPDRS was translated into Arabic and subsequently back‐translated into English by independent translation team; second, cognitive pretesting of selected items was performed; third, the Arabic version was tested in over 400 native Arabic‐speaking PD patients. The psychometric properties of the translated version were analyzed using confirmatory factor analysis (CFA) as well as exploratory factor analysis (EFA). Results The factor structure of the Arabic version was consistent with that of the English version based on the high CFIs for all four parts of the MDS‐UPDRS in the CFA (CFI ≥0.90), confirming its suitability for use in Arabic. Conclusions The Arabic version of the MDS‐UPDRS has good construct validity in Arabic‐speaking patients with PD and has been thereby designated as an official MDS‐UPDRS version. The data collection methodology among Arabic‐speaking countries across two continents of Asia and Africa provides a roadmap for validating additional MDS rating scale initiatives and is strong evidence that underserved regions can be energically mobilized to promote efforts that apply to better clinical care, education, and research for PD. © 2022 International Parkinson and Movement Disorder Society
Clinical responses to dopamine replacement therapy for individuals with Parkinson’s disease (PD) are often difficult to predict. We characterized changes in MDS-UPDRS motor factor scores resulting from a short-duration L-Dopa response (SDR), and investigated how the inter-subject clinical differences could be predicted from motor cortical magnetoencephalography (MEG). MDS-UPDRS motor factor scores and resting-state MEG recordings were collected during SDR from twenty individuals with a PD diagnosis. We used a novel subject-specific strategy based on linear support vector machines to quantify motor cortical oscillatory frequency profiles that best predicted medication state. Motor cortical profiles differed substantially across individuals and showed consistency across multiple data folds. There was a linear relationship between classification accuracy and SDR of lower limb bradykinesia, although this relationship did not persist after multiple comparison correction, suggesting that combinations of spectral power features alone are insufficient to predict clinical state. Factor score analysis of therapeutic response and novel subject-specific machine learning approaches based on subject-specific neuroimaging provide tools to predict outcomes of therapies for PD.
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