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Background and purposeClinical outcomes of multiple sclerosis (MS) patients affected by coronavirus disease 2019 (COVID-19) have been thoroughly investigated, but a further analysis on main signs and symptoms and their risk factors still needs attention. The objective of this study was to group together and describe based on similarity the most common signs and symptoms of COVID-19 in MS patients and identify all factors associated with their manifestation.MethodLogistic and linear regression models were run to recognize factors associated with each pooled group of symptoms and their total number.ResultsFrom March 2020 to November 2021, data were collected from 1354 MS patients with confirmed infection of COVID-19. Ageusia and anosmia was less frequent in older people (odds ratio [OR] 0.98; p = 0.005) and more in smoker patients (OR 1.39; p = 0.049). Smoke was also associated with an incremental number of symptoms (OR 1.24; p = 0.031), substance abuse (drugs or alcohol), conjunctivitis and rash (OR 5.20; p = 0.042) and the presence of at least one comorbidity with shortness of breath, tachycardia or chest pain (OR 1.24; p = 0.008). Some disease-modifying therapies were associated with greater frequencies of certain COVID-19 symptoms (association between anti-CD20 therapies and increment in the number of concomitant symptoms: OR 1.29; p = 0.05). Differences in frequencies between the three waves were found for flu-like symptoms (G1, p = 0.024), joint or muscle pain (G2, p = 0.013) and ageusia and anosmia (G5, p

Background and purposeSome studies have shown that air pollution, often assessed by thin particulate matter with diameter below 2.5 µg/m3 (PM2.5), may contribute to severe COVID-19 courses, as well as play a role in the onset and evolution of multiple sclerosis (MS). However, the impact of air pollution on COVID-19 has never been explored specifically amongst patients with MS (PwMS). This retrospective observational study aims to explore associations between PM2.5 and COVID-19 severity amongst PwMS.MethodsData were retrieved from an Italian web-based platform (MuSC-19) which includes PwMS with COVID-19. PM2.5 2016-2018 average concentrations were provided by the Copernicus Atmospheric Monitoring Service. Italian patients inserted in the platform from 15 January 2020 to 9 April 2021 with a COVID-19 positive test were included. Ordered logistic regression models were used to study associations between PM2.5 and COVID-19 severity.ResultsIn all, 1087 patients, of whom 13% required hospitalization and 2% were admitted to an intensive care unit or died, were included. Based on the multivariate analysis, higher concentrations of PM2.5 increased the risk of worse COVID-19 course (odds ratio 1.90; p = 0.009).ConclusionsEven if several other factors explain the unfavourable course of COVID-19 in PwMS, the role of air pollutants must be considered and further investigated.

ObjectiveThis study was undertaken to assess the impact of immunosuppressive and immunomodulatory therapies on the severity of coronavirus disease 2019 (COVID-19) in people with multiple sclerosis (PwMS).MethodsWe retrospectively collected data of PwMS with suspected or confirmed COVID-19. All the patients had complete follow-up to death or recovery. Severe COVID-19 was defined by a 3-level variable: mild disease not requiring hospitalization versus pneumonia or hospitalization versus intensive care unit (ICU) admission or death. We evaluated baseline characteristics and MS therapies associated with severe COVID-19 by multivariate and propensity score (PS)-weighted ordinal logistic models. Sensitivity analyses were run to confirm the results.ResultsOf 844 PwMS with suspected (n = 565) or confirmed (n = 279) COVID-19, 13 (1.54%) died; 11 of them were in a progressive MS phase, and 8 were without any therapy. Thirty-eight (4.5%) were admitted to an ICU; 99 (11.7%) had radiologically documented pneumonia; 96 (11.4%) were hospitalized. After adjusting for region, age, sex, progressive MS course, Expanded Disability Status Scale, disease duration, body mass index, comorbidities, and recent methylprednisolone use, therapy with an anti-CD20 agent (ocrelizumab or rituximab) was significantly associated (odds ratio [OR] = 2.37, 95% confidence interval [CI] = 1.18-4.74, p = 0.015) with increased risk of severe COVID-19. Recent use (

• IgG HG occurred in 20% of patients treated with OCR or RTX over a mean of 28 months. • The risk of IgG HG was increased by older age and by the number of treatment cycles. • IgG HG increased the risk of severe infections. • Severe infections were infrequent (1.8 per 100 person-years). • It is important to monitor Ig levels during treatment with anti-CD20 agents. Hypogammaglobulinemia (HG) is a known side effect of treatment with anti-CD20 monoclonal antibodies, and it is associated with the risk of infections. Aim of this retrospective multicentre study was to assess the frequency of HG in Multiple Sclerosis (MS) and Neuromyelitis Optica Spectrum Disorder patients treated with Ocrelizumab or Rituximab and its association with the occurrence of severe infections (SI). Furthermore, predictors of HG and SI were sought. We included 556 patients (190M, 366F, mean age: 47 years) with a mean follow-up of 28 months (range 12-90 months). IgG HG occurred in 20% and IgM HG in 34% of patients. At multivariable analysis, the risk of IgG HG was influenced by an older age (≥50 years) (OR 1.64, 95%CI: 1.06-2.54, p=0.027), and by the number of treatment cycles (OR: 1.20, 95%CI: 1.09-1.33, p<0.001). A total of 25 SI occurred (100 person-years rate: 1.8), with a disease phenotype other than relapsing-remitting (OR 1.50, 95%CI: 1.02-2.20; p=0.039) and IgG HG (OR 2.65, 95%CI: 1.15-6.12; p=0.022) increasing its risk. IgG and IgM HG occurred in a considerable proportion of patients. IgG HG increased the risk of SI, which were, nevertheless, relatively infrequent. Our results highlight the importance of monitoring immunoglobulin levels during treatment with anti-CD20 agents, to personalize treatment strategies. [ABSTRACT FROM AUTHOR]

Background Multiple sclerosis (MS) is a chronic neurological disease impacting patients’ health-related quality of life (HRQoL). Since MS specific HRQoL questionnaire are often time consuming; thus, simple, feasible and ease administering instruments are needed to assess MS HRQoL in clinical practice and clinical trials. Hence, aim of our study was to investigated the HRQoL in a large cohort of MS patients using the Coop/Wonca charts. Methods This was multicenter, independent, non-sponsored, observational study, including patients from 40 Italian MS centers. Inclusion criteria were Expanded Disability Status Scale score between 1.0 and 5.5; stable disease at enrolment. HRQoL was assessed, using six Coop/Wonca charts for physical fitness, feelings, daily activities, social activities, changes in health, health condition, and the Composites Scores of Multiple Sclerosis Quality of Life-54 (MSQoL-54), physical health composite summary (PHCS) and the mental health composite summary (MHCS). Results Out of 648, 593 relapsing-remitting MS patients, 415 (70%) women, mean age years, 417 (70%) were finally enrolled. We found a inter-rater agreement of 0.8, ranging from 0.64 to 0.91, as expressed by the alpha coefficient. Intra-rater agreement was 0.82, ranging from 0.78 to 0.96. Coop/Wonca charts were scored with a Likert method from one to five (corresponding to best and worst HRQoL respectively). According to this scoring, study population was stratified into three categories (score 1–2 corresponding to better HRQoL; score 3 corresponding to neutral profile; score 4–5 corresponding to worse HRQoL). Coop/Wonca charts resulted significantly correlated with PHCS and with MHCS. [ABSTRACT FROM AUTHOR]

• PM2.5, PM10 and NO2 are risk factors for Covid-19 pneumonia among MS patients. • We studied the joint exposure to the three pollutants as an environmental mixture. • The most dangerous pollutants within the mixture were NO2 and PM2.5. Many studies investigated the association between air pollution and Covid-19 severity but the only study focusing on patients with Multiple Sclerosis (MS) exclusively evaluated exposure to PM2.5. We aim to study, in a sample of MS patients, the impact of long-term exposure to PM2.5, PM10 and NO2 on Covid-19 severity, described as occurrence of pneumonia. A 1:2 ratio case-control study was designed, differentiating cases and controls based on Covid-19 pneumonia. Associations between pollutants and outcome were studied using logistic regression. Weighted quantile sum (WQS) logistic regression was used to identify the individual contribution of each pollutant within the mixture; Least Absolute Shrinkage and Selection Operator (LASSO) penalized regression was performed to confirm the variable selection from WQS. All the analyses were adjusted for confounders selected a priori. Of the 615 eligible patients, 491 patients provided detailed place of exposure and were included in the principal analysis. Higher concentrations of air pollutants were associated with increased odds of developing Covid-19 pneumonia (PM2.5: 3rd vs 1st tercile OR(95% CI)=2.26(1.29;3.96); PM10: 3rd vs 1st tercile OR(95% CI)=2.12(1.22;3.68); NO2: 3rd vs 1st tercile OR(95% CI)=2.12(1.21;3.69)). Pollutants were highly correlated with each other; WQS index was associated to an increased risk of pneumonia (β=0.44; p-value=0.004) and the main contributors to this association were NO2 (41%) and PM2.5 (34%). Consistently, Lasso method selected PM2.5 and NO2. Higher long-term exposure to PM2.5, PM10 and NO2 increased the odds of Covid-19 pneumonia among MS patients and the most dangerous pollutants were NO2 and PM2.5. [ABSTRACT FROM AUTHOR]

• The aim of this study was to construct such score and to evaluate its performance. • 3852 patients were included in the study and 17% of the patients required hospitalization. • This score can be used as an additional instrument to identify high-risk patients and persuade them to take important measures to prevent Covid-19 infection. 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. 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. 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%5fonly=691814d57b564a34b3596e4fcdcf8580. 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). [ABSTRACT FROM AUTHOR]