Access and Attitudes Toward Palliative Care Among Movement Disorders Clinicians.

BACKGROUND: Neuropalliative care is an emerging field for those with neurodegenerative illnesses, but access to neuropalliative care remains limited. OBJECTIVE: We sought to determine Movement Disorder Society (MDS) members' attitudes and access to palliative care. METHODS: A quantitative and qualitative survey instrument was developed by the MDS Palliative Care Task Force and e-mailed to all members for completion. Descriptive statistics and qualitative analysis were triangulated. RESULTS: Of 6442 members contacted, 652 completed the survey. Completed surveys indicating country of the respondent overwhelmingly represented middle- and high-income countries. Government-funded homecare was available to 54% of respondents based on patient need, 25% limited access, and 21% during hospitalization or an acute defined event. Eighty-nine percent worked in multidisciplinary teams. The majority endorsed trigger-based referrals to palliative care (75.5%), while 24.5% indicated any time after diagnosis was appropriate. Although 66% referred patients to palliative care, 34% did not refer patients. Barriers were identified by 68% of respondents, the most significant being available workforce, financial support for palliative care, and perceived knowledge of palliative care physicians specific to movement disorders. Of 499 respondents indicating their training in palliative care or desire to learn these skills, 55% indicated a desire to gain more skills. CONCLUSIONS: The majority of MDS member respondents endorsed a role for palliative care in movement disorders. Many members have palliative training or collaborate with palliative care physicians. Although significant barriers exist to access palliative care, the desire to gain more skills and education on palliative care is an opportunity for professional development within the MDS. © 2021 International Parkinson and Movement Disorder Society.

Substantia Nigra Neuromelanin as an Imaging Biomarker of Disease Progression in Parkinson's Disease.

BACKGROUND: A specific T1-weighted magnetic resonance imaging (MRI) sequence has been shown to detect substantia nigra (SN) neuromelanin (NM) signal changes that accurately discriminate Parkinson's disease (PD) patients from controls, even in early disease stages. However, it is unclear what happens to these SN changes in later disease stages and if they can be a marker of disease progression. OBJECTIVE: to investigate the pattern of SN-NM area loss and contrast ratio (CR) intensity changes in late-stage PD (LSPD) compared to earlier disease stages. METHODS: A comparative cross-sectional study was performed, analyzing SN-NM MRI signal in LSPD (Schwab and England Activities of Daily Living Scale score <50 or Hoehn Yahr Stage [HY] >3), comparing this group with de novo, 2-5 year PD and controls. SN-NM signal area and CR values for the internal and lateral SN regions were obtained with semi-automated methods. RESULTS: 13 LSPD, 12 de novo patients with PD, 10 PD patients with a 2-5 year disease duration, and 10 controls were included. NM signal area was significantly decreased in LSPD compared to de novo PD (P-value = 0.005; sensitivity: 75%; specificity 92% and AUC: 0.86). In the lateral SN region, a decrease in the CR was detected in all PD groups compared to controls; despite not reaching statistical significance, a slight increment was observed comparing LSPD to 2-5 year PD. NM signal area significantly correlated with HY (R = -0.37; P < 0.05) and Movement disorder Society Unified Parkinson's Disease Rating Scale part II (MDS-UPDRS) (R = -0.4; P < 0.05) while a weak correlation was found with MDS-UPDRS part III (R = -0.26; P: 0.1). CONCLUSION: SN area evaluated by NM-sensitive MRI may be a promising biomarker of nigral degeneration and disease progression in PD patients.

A Perspective on Wearable Sensor Measurements and Data Science for Parkinson's Disease.

Miniaturized and wearable sensor-based measurements enable the assessment of Parkinson's disease (PD) motor-related features like never before and hold great promise as non-invasive biomarkers for early and accurate diagnosis, and monitoring the progression of PD. High-fidelity human movement reconstruction and simulation can already be conducted in a clinical setting with increasingly precise and affordable motion technology enabling access to high-quality labeled data on patients' subcomponents of movement (kinematics and kinetics). At the same time, body-worn sensors now allow us to extend some quantitative movement-related measurements to patients' daily living activities. This era of patient movement "cognification" is bringing us previously inaccessible variables that encode patients' movement, and that, together with measures from clinical examinations, poses new challenges in data analysis. We present herein examples of the application of an unsupervised methodology to classify movement behavior in healthy individuals and patients with PD where no specific knowledge on the type of behaviors recorded is needed. We are most certainly leaving the early stage of the exponential curve that describes the current technological evolution and soon will be entering its steep ascent. But there is already a benefit to be derived from current motion technology and sophisticated data science methods to objectively measure parkinsonian impairments.