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1.
Brain Commun ; 3(3): fcab135, 2021.
Article in English | MEDLINE | ID: covidwho-1360337

ABSTRACT

A variety of neuropsychiatric complications has been described in association with COVID-19 infection. Large scale studies presenting a wider picture of these complications and their relative frequency are lacking. The objective of our study was to describe the spectrum of neurological and psychiatric complications in patients with COVID-19 seen in a multidisciplinary hospital centre over 6 months. We conducted a retrospective, observational study of all patients showing neurological or psychiatric symptoms in the context of COVID-19 seen in the medical and university neuroscience department of Assistance Publique Hopitaux de Paris-Sorbonne University. We collected demographic data, comorbidities, symptoms and severity of COVID-19 infection, neurological and psychiatric symptoms, neurological and psychiatric examination data and, when available, results from CSF analysis, MRI, EEG and EMG. A total of 249 COVID-19 patients with a de novo neurological or psychiatric manifestation were included in the database and 245 were included in the final analyses. One-hundred fourteen patients (47%) were admitted to the intensive care unit and 10 (4%) died. The most frequent neuropsychiatric complications diagnosed were encephalopathy (43%), critical illness polyneuropathy and myopathy (26%), isolated psychiatric disturbance (18%) and cerebrovascular disorders (16%). No patients showed CSF evidence of SARS-CoV-2. Encephalopathy was associated with older age and higher risk of death. Critical illness neuromyopathy was associated with an extended stay in the intensive care unit. The majority of these neuropsychiatric complications could be imputed to critical illness, intensive care and systemic inflammation, which contrasts with the paucity of more direct SARS-CoV-2-related complications or post-infection disorders.

2.
Nature Machine Intelligence ; 2(6):293-294, 2020.
Article | Web of Science | ID: covidwho-786671

ABSTRACT

The COVID-19 pandemic poses a historical challenge to society. The profusion of data requires machine learning to improve and accelerate COVID-19 diagnosis, prognosis and treatment. However, a global and open approach is necessary to avoid pitfalls in these applications.

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