Covid-19: Involvement of the nervous system. Identifying neurological predictors defining the course of the disease.
J Neurol Sci
; 425: 117438, 2021 06 15.
Article
in English
| MEDLINE | ID: covidwho-1174388
ABSTRACT
The main objective of this study was to analyse neurological symptoms during a Covid-19 infection and determine the pattern of symptoms by comparing outpatients with inpatients. A further goal was to identify possible predictors, such as pre-existing conditions and neurological symptoms. We recorded the clinical data of 40 inpatients and 42 outpatients in this retrospective, cross sectional study. Of them, 68 patients (83%), evenly distributed between the two groups, suffered from neurological symptoms. We identified the onset of neurological symptoms and the related time ranges in 41 patients (36 outpatients and 5 inpatients). Of these, 63.4% reported neurological symptoms on the first or second day of illness. 49 patients (72%) showed combinations of at least two to a maximum of seven different neurological symptoms. A more severe course of disease was correlated with age and male sex, but age was not identified as a predictor for the occurrence of neurological symptoms. Women suffered from central and neuromuscular symptoms more often than men (p = 0,004). The most common symptoms were fatigue (54%), headache (31%), loss of taste (31%), and loss of smell (27%). Pre-existing dementia was associated with increased lethality; similarly, pre-existing stroke was associated with a more severe course of Covid-19 infection. Hallucinations and confusion were related to an increased likelihood of death. The present data demonstrate the importance of comprehensive neurological support of inpatients and outpatients affected by Covid-19.
Keywords
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
COVID-19
Type of study:
Experimental Studies
/
Observational study
/
Prognostic study
/
Randomized controlled trials
Limits:
Female
/
Humans
/
Male
Language:
English
Journal:
J Neurol Sci
Year:
2021
Document Type:
Article
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