ABSTRACT
Case Diagnosis: A 65-year-old woman who developed multiple system atrophy as a sequelae of COVID-19 infection Case Description or Program Description: The patient developed progressive dizziness, blurriness, and unsteady gait immediately following hospitalization for COVID-19 infection. Formal evaluation noted rightward nystagmus, mild resting tremor of the right hand, slowed finger tapping test bilaterally, overshooting on right finger to nose, and shuffling gait. MRI of the brain revealed moderate cerebellar and pontine volume loss with crossed hyperintensity of the pons, or "hot cross bun sign", raising suspicion for degenerative disease. Lumbar puncture analysis was normal. The patient was diagnosed with multiple system atrophy with parkinsonism features and started on a trial of amantadine and carbidopa/levodopa. Videonystagmography confirmed cerebellar etiology of her symptoms. Vestibular rehabilitation and meclizine was initiated with subsequent improvement in dizziness and functionality. Setting(s): Acute inpatient rehabilitation facility. Assessment/Results: After 20 days of acute inpatient rehabilitation including vestibular therapy, amantadine, carbidopa/levodopa, and meclizine, there was improvement of dizziness, dysarthria, tremor, weakness, and gait. The therapy team noted good progress since admission regarding performance of self-care tasks as well as transfers and mobility. The patient was discharged home with outpatient vestibular therapy. Discussion (relevance): This is a case of newly diagnosed multiple system atrophy associated with "hot cross buns sign" on MRI with COVID19 infection as the implicated etiology. To our knowledge, this is the first case of central cerebellopontine degeneration associated with COVID19. Conclusion(s): New onset multiple system atrophy may be a sequelae of COVID19 infection.
ABSTRACT
BACKGROUND: Parkinson disease (PD) is a common, multifaceted neurodegenerative disorder profoundly impacting patients' autonomy and quality of life. Assessment in real-life conditions of subjective symptoms and objective metrics of mobility and nonmotor symptoms such as sleep disturbance is strongly advocated. This information would critically guide the adaptation of antiparkinsonian medications and nonpharmacological interventions. Moreover, since the spread of the COVID-19 pandemic, health care practices are being reshaped toward a more home-based care. New technologies could play a pivotal role in this new approach to clinical care. Nevertheless, devices and information technology tools might be unhandy for PD patients, thus dramatically limiting their widespread employment. OBJECTIVE: The goals of the research were development and usability evaluation of an application, SleepFit, for ecological momentary assessment of objective and subjective clinical metrics at PD patients' homes, and as a remote tool for researchers to monitor patients and integrate and manage data. METHODS: An iterative and user-centric strategy was employed for the development of SleepFit. The core structure of SleepFit consists of (1) an electronic finger-tapping test; (2) motor, sleepiness, and emotional subjective scales; and (3) a sleep diary. Applicable design, ergonomic, and navigation principles have been applied while tailoring the application to the specific patient population. Three progressively enhanced versions of the application (alpha, v1.0, v2.0) were tested by a total of 56 patients with PD who were asked to perform multiple home assessments 4 times per day for 2 weeks. Patient compliance was calculated as the proportion of completed tasks out of the total number of expected tasks. Satisfaction on the latest version (v2.0) was evaluated as potential willingness to use SleepFit again after the end of the study. RESULTS: From alpha to v1.0, SleepFit was improved in graphics, ergonomics, and navigation, with automated flows guiding the patients in performing tasks throughout the 24 hours, and real-time data collection and consultation were made possible thanks to a remote web portal. In v2.0, the kiosk-mode feature restricts the use of the tablet to the SleepFit application only, thus preventing users from accidentally exiting the application. A total of 52 (4 dropouts) patients were included in the analyses. Overall compliance (all versions) was 88.89% (5707/6420). SleepFit was progressively enhanced and compliance increased from 87.86% (2070/2356) to 89.92% (2899/3224; P=.04). Among the patients who used v2.0, 96% (25/26) declared they would use SleepFit again. CONCLUSIONS: SleepFit can be considered a state-of-the-art home-based system that increases compliance in PD patients, ensures high-quality data collection, and works as a handy tool for remote monitoring and data management in clinical research. Thanks to its user-friendliness and modular structure, it could be employed in other clinical studies with minimum adaptation efforts. TRIAL REGISTRATION: ClinicalTrials.gov NCT02723396; https://clinicaltrials.gov/ct2/show/NCT02723396.