ABSTRACT
While mass immunization against coronavirus disease 2019 (COVID-19) rolls out around the globe, safety concerns and adverse events that need prompt evaluation are also emerging. Neurological complications such as transverse myelitis raise concerns as cases were observed in clinical trials. Cerebrospinal fluid analysis is routine in these cases and the characteristics of the abnormalities found are of great help not only in establishing the diagnosis but also in understanding this rare condition. We present a case of extensive longitudinal transverse myelitis after vaccination with AZD1222, AstraZeneca COVID-19 vaccine, which was the first case reported in Brazil. The abnormalities found in the study of the cerebrospinal fluid in our case are reported and discussed using data from recent publications.
ABSTRACT
BACKGROUND: Many risk factors for the development of severe forms of Covid-19 have been identified, some applying to the general population and others specific to Multiple Sclerosis (MS) patients. However, a score for quantifying the individual risk of severe Covid-19 in patients with MS is not available. The aim of this study was to construct such score and to evaluate its performance. METHODS: Data on patients with MS infected with Covid-19 in Italy, Turkey and South America were extracted from the Musc-19 platform. After imputation of missing values, data were separated into training data set (70%) and validation data set (30%). Univariable logistic regression models were performed in the training dataset to identify the main risk factors to be included in the multivariable logistic regression analyses. To select the most relevant variables we applied three different approaches: (1) multivariable stepwise, (2) Lasso regression, (3) Bayesian model averaging. Three scores were defined as the linear combination of the coefficients estimated in the models multiplied by the corresponding value of the variables and higher scores were associated to higher risk of severe Covid-19 course. The performances of the three scores were compared in the validation dataset based on the area under the ROC curve (AUC) and an optimal cut-off was calculated in the training dataset for the score with the best performance. The probability of showing a severe Covid-19 course was calculated based on the score with the best performance. RESULTS: 3852 patients were included in the study (2696 in the training dataset and 1156 in the validation data set). 17% of the patients required hospitalization and risk factors for severe Covid-19 course were older age, male sex, living in Turkey or South America instead of living in Italy, presence of comorbidities, progressive MS, longer disease duration, higher Expanded Disability Status Scale, Methylprednisolone use and anti-CD20 treatment. The score with the best performance was the one derived using the Lasso selection approach (AUC= 0.72) and it was built with the following variables: age, sex, country, BMI, presence of comorbidities, EDSS, methylprednisolone use, treatment. An excel spreadsheet to calculate the score and the probability of severe Covid-19 is available at the following link: https://osf.io/ac47u/?view_only=691814d57b564a34b3596e4fcdcf8580. CONCLUSIONS: The originality of this study consists in building a useful tool to quantify the individual risk for Covid-19 severity based on patient's characteristics. Due to the modest predictive ability and to the need of external validation, this tool is not ready for being fully used in clinical practice to make important decisions or interventions. However, it can be used as an additional instrument to identify high-risk patients and persuade them to take important measures to prevent Covid-19 infection (i.e. getting vaccinated against Covid-19, adhering to social distancing, and using of personal protection equipment).
Subject(s)
COVID-19 , Multiple Sclerosis , Bayes Theorem , COVID-19/epidemiology , Humans , Male , Methylprednisolone , Multiple Sclerosis/epidemiology , Personal Protective EquipmentABSTRACT
BACKGROUND: The MuSC-19 project is an Italian cohort study open to international partners that collects data on multiple sclerosis (MS) patients with COVID-19. During the second wave of the pandemic, serological tests became routinely available. OBJECTIVE: To evaluate the seroprevalence of anti-SARS-CoV-2 antibodies according to the use of disease-modifying therapy (DMT) in a subset of patients included in the MuSC-19 data set who had undergone a serological test. METHODS: We evaluated the association between positive serological test results and time elapsed since infection onset, age, sex, Expanded Disability Status Scale score, comorbidities and DMT exposure using a multivariable logistic model. RESULTS: Data were collected from 423 patients (345 from Italy, 61 from Turkey and 17 from Brazil) with a serological test performed during follow-up. Overall, 325 out of 423 tested patients (76.8%) had a positive serological test. At multivariate analysis, therapy with anti-CD20 was significantly associated with a reduced probability of developing antibodies after COVID-19 (odds ratio (OR) = 0.20, p = 0.002). CONCLUSION: Patients with MS maintain the capacity to develop humoral immune response against SARS-COV-2, although to a lesser extent when treated with anti-CD20 drugs. Overall, our results are reassuring with respect to the possibility to achieve sufficient immunization with vaccination.