Frequency of Parkinson disease following COVID-19 infection: A two-year retrospective cohort study.

INTRODUCTION: COVID-19 infection is known to cause various neurological symptoms, and potentially increases the risk of developing subsequent neurodegenerative conditions including parkinsonism. To our knowledge, no study to date has used a large data set in the United States to ascertain the risk of developing incident Parkinson disease in patients with history of COVID-19 infection compared to the risk amongst those without prior COVID-19 infection. METHODS: We utilized data from TriNetX electronic health records network which includes 73 healthcare organizations and over 107 million patients. We compared adult patients with and without COVID-19 infection, with health records from January 1, 2020 through July 26, 2022, to determine the relative risk of developing Parkinson disease stratified by 3-month intervals. We used propensity score matching to control for patients' age, sex, and smoking history. RESULTS: We collected data on 27,614,510 patients meeting our study criteria: 2,036,930 patients with a positive COVID-19 infection (COVID-19) and 25,577,580 without a positive COVID-19 infection (non-COVID-19). After propensity score matching, age, sex, and smoking history differences became non-significant, with 2,036,930 patients in each cohort. After propensity score matching, we found significantly increased odds of new onset Parkinson disease in the COVID-19 cohort at three, six, nine, and twelve months from the index event, with peak odds ratio at six months. After twelve months there is no significant difference between the COVID-19 group and non-COVID-19 group. CONCLUSIONS: There may be a transiently increased risk of developing Parkinson disease in the first year following COVID-19 infection.

Automated Computer Vision Assessment of Hypomimia in Parkinson Disease: Proof-of-Principle Pilot Study.

BACKGROUND: Facial expressions require the complex coordination of 43 different facial muscles. Parkinson disease (PD) affects facial musculature leading to "hypomimia" or "masked facies." OBJECTIVE: We aimed to determine whether modern computer vision techniques can be applied to detect masked facies and quantify drug states in PD. METHODS: We trained a convolutional neural network on images extracted from videos of 107 self-identified people with PD, along with 1595 videos of controls, in order to detect PD hypomimia cues. This trained model was applied to clinical interviews of 35 PD patients in their on and off drug motor states, and seven journalist interviews of the actor Alan Alda obtained before and after he was diagnosed with PD. RESULTS: The algorithm achieved a test set area under the receiver operating characteristic curve of 0.71 on 54 subjects to detect PD hypomimia, compared to a value of 0.75 for trained neurologists using the United Parkinson Disease Rating Scale-III Facial Expression score. Additionally, the model accuracy to classify the on and off drug states in the clinical samples was 63% (22/35), in contrast to an accuracy of 46% (16/35) when using clinical rater scores. Finally, each of Alan Alda's seven interviews were successfully classified as occurring before (versus after) his diagnosis, with 100% accuracy (7/7). CONCLUSIONS: This proof-of-principle pilot study demonstrated that computer vision holds promise as a valuable tool for PD hypomimia and for monitoring a patient's motor state in an objective and noninvasive way, particularly given the increasing importance of telemedicine.

COVID-19, HHV6 and MOG antibody: A perfect storm.

BACKGROUND: Serious neurological complications of SARS-CoV-2 are increasingly being recognized. CASE: We report a novel case of HHV6 myelitis with parainfectious MOG-IgG in the setting of COVID-19-induced lymphopenia and hypogammaglobulinemia. The patient experienced complete neurological recovery with gancyclovir, high dose corticosteroids, and plasma exchange. To our knowledge, this is the first case of HHV6 reactivation in the central nervous system in the setting of COVID19 infection and the first case of MOG-IgG myelitis in the setting of SARS-CoV-2 and HHV6 coinfection. CONCLUSION: Patients with neurological manifestations in the setting of COVID19-related immunodeficiency should be tested for opportunistic infections including HHV6. Viral infection is a known trigger for MOG-IgG and therefore this antibody should be checked in patients with SARS-CoV-2 associated demyelination.