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Abstract While Parkinson's disease (PD)-related neurodegeneration is associated with structural changes in the brain, conventional magnetic resonance imaging (MRI) has proven less effective for clinical diagnosis due to its inability to reliably identify subtle changes early in the disease course. In this study, we aimed to develop a structural MRI-based biomarker to predict the rate of progression of motor symptoms in the early stages of PD. The study included 88 patients with PD and 120 healthy controls from the Parkinson's Progression Markers Initiative database; MRI at baseline and motor symptom scores assessed using the MDS-UPDRS-III at two time points (baseline and 48 months) were selected. Group-level volumetric analyses at baseline were not associated with the decline in motor functioning. Then, we developed a patient-specific multivariate gray matter volumetric distance and demonstrated that it could significantly predict changes in motor symptom scores (P

Electronic Health Record (EHR) systems are often configured to address challenges and improve patient safety for persons with Parkinson’s disease (PWP). For example, EHR systems can help identify Parkinson’s disease (PD) patients across the hospital by flagging a patient’s diagnosis in their chart, preventing errors in medication and dosing through the use of clinical decision support, and supplementing staff education through care plans that provide step-by-step road maps for disease-based care of a specific patient population. However, most EHR-based solutions are locally developed and, thus, difficult to scale widely or apply uniformly across hospital systems. In 2020, the Parkinson’s Foundation, a national and international leader in PD research, education, and advocacy, and Epic, a leading EHR vendor with more than 35% market share in the United States, launched a partnership to reduce risks to hospitalized PWP using standardized EHR-based solutions. This article discusses that project which included leadership from physician informaticists, movement disorders specialists, hospital quality officers, the Parkinson’s Foundation and members of the Parkinson’s community. We describe the best practice solutions developed through this project. We highlight those that are currently available as standard defaults or options within the Epic EHR, discuss the successes and limitations of these solutions, and consider opportunities for scalability in environments beyond a single EHR vendor. The Parkinson’s Foundation and Epic launched a partnership to develop best practice solutions in the Epic EHR system to improve safety for PWP in the hospital. The goal of the partnership was to create the EHR tools that will have the greatest impact on outcomes for hospitalized PWP.

Individuals with Parkinson’s disease (PD) are vulnerable during hospitalizations due to the underlying complexities o1f symptoms, and acute illness or medication changes often lead to decompensation. Complications during hospitalizations are often due to worsening motor and nonmotor symptoms and commonly result from inaccurate medication regimens. Although the accuracy of medication administration relies on an interplay of factors, including patient status, transitions of care, coordination between the hospital prescriber and outpatient neurologist, etc., hospital pharmacists play an integral role in pharmacotherapy. The main aspects of pharmacy strategies aim to achieve timely administration of levodopa-containing medications, reduction of substitution and omissions of antiparkinsonian medications, and avoidance of antidopaminergic medications. This paper highlights critical areas for improvement and recommendations to minimize the impact of other factors from the pharmacy standpoint.

BackgroundTowards the end of life (EOL), persons with parkinsonism (PwP) have complex needs and can present with unique palliative care (PC) challenges. There are no widely accepted guidelines to aid neurologists, hospitalists, or PC clinicians in managing the symptoms of PwP at EOL. We examined a population of PwP at EOL, aiming to describe trends of in-hospital management and utilization of PC services.MethodsAll PwP admitted to two hospitals during 2018 (N = 727) were examined retrospectively, assessing those who died in hospital or were discharged with hospice (EOL group, N = 35) and comparing them to the main cohort. Their demographics, clinical data, engagement of multidisciplinary and palliative services, code status changes, invasive care, frequency of admissions, and medication administration were assessed.ResultsAmong the EOL group, 8 expired in hospital, and 27 were discharged to hospice. Forty-six percent of EOL patients received a PC consultation during their admission. The median interval from admission to death was 37 days. Seventy-seven percent had a full code status on admission. Compared to hospice patients, those who expired in hospital had higher rates of invasive procedures and intensive care unit transfers (41% vs. 75%, in both variables), and lower rates of PC involvement (52% vs. 25%). The transition of code status change for the EOL group from Full code to Do Not Resuscitate (DNR) occurred at a median 4–5 days from admission. For patients that passed in the hospital, the median days from transition of code status to death was 0(IQR 0–1). Levodopa dose deviations were frequent in both EOL and non-EOL group, but contraindicated medications were infrequently administered (11% in EOL group vs. 9% in non-EOL group).ConclusionOur data suggest a low utilization of PC services and delayed discussions of goals of care. More work is needed to raise awareness of inpatient teams managing PwP regarding the unique but common challenges facing PwP with advanced disease. A brief narrative review summarizing the suggested management of symptoms common to hospitalized PwP near EOL is provided.

Utilizing technology to precisely quantify Parkinson’s disease motor symptoms has evolved over the past 50 years from single point in time assessments using traditional biomechanical approaches to continuous monitoring of performance with wearables. Despite advances in the precision, usability, availability and affordability of technology, the “gold standard” for assessing Parkinson’s motor symptoms continues to be a subjective clinical assessment as none of these technologies have been fully integrated into routine clinical care of Parkinson’s disease patients. To facilitate the integration of technology into routine clinical care, the Develop with Clinical Intent (DCI) model was created. The DCI model takes a unique approach to the development and integration of technology into clinical practice by focusing on the clinical problem to be solved by technology rather than focusing on the technology and then contemplating how it could be integrated into clinical care. The DCI model was successfully used to develop the Parkinson’s disease Waiting Room of the Future (WROTF) within the Center for Neurological Restoration at the Cleveland Clinic. Within the WROTF, Parkinson’s disease patients complete the self-directed PD-Optimize application on an iPad. The PD-Optimize platform contains cognitive and motor assessments to quantify PD symptoms that are difficult and time-consuming to evaluate clinically. PD-Optimize is completed by the patient prior to their medical appointment and the results are immediately integrated into the electronic health record for discussion with the movement disorder neurologist. Insights from the clinical use of PD-Optimize has spurred the development of a virtual reality technology to evaluate instrumental activities of daily living in PD patients. This new technology will undergo rigorous assessment and validation as dictated by the DCI model. The DCI model is intended to serve as a health enablement roadmap to formalize and accelerate the process of bringing the advantages of cutting-edge technology to those who could benefit the most: the patient.

Augmented reality (AR) may be a useful tool for the delivery of dual-task training. This manuscript details the development of the Dual-task Augmented Reality Treatment (DART) platform for individuals with Parkinson’s disease (PD) and reports initial feasibility, usability, and efficacy of the DART platform in provoking dual-task interference in individuals with PD. The DART platform utilizes the head-mounted Microsoft HoloLens2 AR device to deliver concurrent motor and cognitive tasks. Biomechanical metrics of gait and cognitive responses are automatically computed and provided to the supervising clinician. To assess feasibility, individuals with PD (N = 48) completed a bout of single-task and dual-task walking using the DART platform. Usability was assessed by the System Usability Scale (SUS). Dual-task interference was assessed by comparing single-task walking and walking during an obstacle course while performing a cognitive task. Average gait velocity decreased from 1.06 to 0.82 m/s from single- to dual-task conditions. Mean SUS scores were 81.3 (11.3), which placed the DART in the “good” to “excellent” category. To our knowledge, the DART platform is the first to use a head-mounted AR system to deliver a dual-task paradigm and simultaneously provide biomechanical data that characterize cognitive and motor performance. Individuals with PD were able to successfully use the DART platform with satisfaction, and dual-task interference was provoked. The DART platform should be investigated as a platform to treat dual-task declines associated with PD.