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• B-cell suppressor treatments are associated with the risk of breakthrough COVID-19 in our cohort. • We did not observe any case of severe COVID-19: vaccination fully protected our cohort of people with multiple sclerosis against severe breakthrough disease. • B-cell suppressor treatments and EDSS were factors associated with low antibody titers after vaccination. Disease modifying treatments (DMTs) for multiple sclerosis (MS) are effective in preventing both relapses and disability progression. Highly effective treatments (HETs) are more effective than platform therapy in preventing confirmed disability progression (CDP), when used early. Infections may complicate HETs administration, and their prevention through vaccination is crucial in order to assure the safety of people with MS (pwMS). The aim of the present study is to describe the effect of MS DMTs on COVID-19 vaccination and the risk of breakthrough infection in a cohort of pwMS. This is a monocentric retrospective observational study conducted at the MS center of the Guglielmo da Saliceto Hospital in Piacenza, Italy. One hundred and fifty-seven (157) pwMS who received two doses of the SARS-CoV-2 vaccine (with 80.3 % receiving a booster dose) were included in the study. fifty-six pwMS (35.7 %) were females, the mean age was 48.6 (SD: 12.87) years, and 59 (37.6 %) had at least one comorbidity. Twenty-five (15.9 %) breakthrough infections were observed, with 17 (68.0 %) classified as mild and 8 (32.0 %) as moderate. A multivariable linear regression model confirmed that B-cell suppressor DMTs and EDSS were factors associated with the latest antibody titre. Patients treated with B-cell suppressors exhibited a risk almost four times higher for breakthrough infections compared to other patients, with a hazard ratio (HR) of 3.72 (95 % CI: 1.50 – 9.27) (p = 0.005). B-cell suppressor DMTs are associated with the risk of breakthrough COVID-19 in our cohort, but vaccination fully protected pwMS against severe breakthrough disease. [ABSTRACT FROM AUTHOR]

• Cognitive profile and physician-based and patients-reported outcomes could have a considerable impact on the disease management and on treatment decision making. • According to the Control Preference Scale, more than 50% of patients who needed to change therapy chose a "collaborative" role in making treatment decision. • Cognitive profile with SDMT seems to influence patients' preference on treatment decision. Clinicians are increasingly recognizing the importance of shared decision-making in complex treatment choices, highlighting the importance of the patient's rationale and motivation for switching therapies. This study aimed to evaluate the association between different modalities of changing multiple sclerosis (MS) treatments, cognitive profile and attitude and preferences of patients concerning treatment choice. This multicenter cross-sectional study was conducted at 28 Italian MS centers in the period between June 2016 and June 2017. We screened all MS patients treated with any DMT, with a treatment compliance of at least 80% of therapy administered during the 3 last months who needed to modify MS therapy because of efficacy, safety or other reasons during a follow-up visit. At the time of switching the symbol digit modalities test (SDMT) and the Control Preference Scale (CPS) were evaluated. According to the CPS, patients were classified as "active" (i.e. who prefer making the medical decision themselves), "collaborative" (i.e. who prefer decisions be made jointly with the physician), or "passive" (i.e. who prefer the physician make the decision). Out of 13,657 patients recorded in the log, 409 (3%) changed therapy. Of these, 336 (2.5%) patients, 69.6% were female and with mean age 40.6 ± 10.5 years, were enrolled. According to the CPS score evaluation, a significant high percentage of patients (51.1%) were considered collaborative, 74 patients (22.5%) were passive, and 60 (18.2%) patients were active. Stratifying according to CPS results, we found a higher SDMT score among collaborative patients compared to active and passive ones (45.8 ± 12.3 versus 41.0 ± 13.2 versus 41.7 ± 12.8, p < 0.05). In this study, the CPS evaluation showed that more than 50% of patients who needed to change therapy chose a "collaborative" role in making treatment decision. Cognitive profile with SDMT seems to correlate with patients' preference on treatment decision, showing better scores in collaborative patients. [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]

Objectives: To assess whether COVID-19 could be a concurrent factor in the genesis and/or worsening of stroke and to provide data on COVID-19 -associated stroke patients during the first pandemic wave and comparative data on COVID-19 negative stroke patients in the same period.Materials and Methods: This is a retrospective, observational, case-control, single centre study, carried out in a General Hospital in northern Italy. Sixty-three consecutive stroke patients were included, COVID-19-associated stroke was classified as cases and non COVID-19-associated stroke as controls.Results: A total of 19/63 (28.8%) had a COVID-19-associated stroke, 11 /63 (17.5%) were haemorrhagic and 52/63 (82.5%) ischaemic. COVID-19-associated strokes were more severe (p-value 0.019) and had a higher risk of severe disability and/or death (OR 3.79, CI 95%: 1.21-11.93, p-value 0.19). The COVID-19-associated stroke patients with onset during hospitalization for COVID-19 had a more severe stroke than patients with COVID-19 onset during hospitalization for stroke (p-value 0.019).Conclusion: Although no relationship was observed between the stroke aetiology and COVID-19, intriguingly, COVID-associated stroke turned out to be more severe and disabling. Hopefully, further studies will provide more data and help in the management of this emerging population. [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]

Background and Purpose: an estimated 40-80% of acute ischemic stroke patients have dysphagia and about 14% develop stroke-associated pneumonia. However, it may be difficult to detect swallowing problems at admission. Moreover, there might not be an on-duty specialist skilled in the diagnosis of this condition. This study aimed at developing a user-friendly bedside examination to identify the risk of dysphagia in stroke patients at hospital admission.Methods: a diagnostic accuracy study was carried out to assess the concurrent validity of a simple Bedside Screening Tool for Dysphagia (BSTD) in acute stroke. All the consecutive stroke patients admitted between January and April 2018 were enrolled. Sensitivity, specificity, positive (PPV), negative predictive values (NPV) and the Cohen K concordance index scores, reported by nurses and speech-pathologists, were assessed.Results: a total of 67/120 patients (55.8%) were male; overall average age was 67.4 (range 45-91) and 80.8% of the whole population had a history of ischemic stroke. The nursing staff identified 33.3% of dysphagia cases at admission and the speech pathologists 30%. The Cohen K was 0.92 (optimal concordance when K was > 0.8), sensitivity was 100%, specificity 95.2%, PPV 90% and NPV 100%.Conclusions: our BSTD had a 100% negative predictive value, indicating that this screening test is very useful in ruling out/confirming dysphagia in acute stroke patients. [ABSTRACT FROM AUTHOR]

• The introduction of increasingly effective treatments has changed the MS scenario. • This study investigated the reasons that brought about modification of treatment. • In our study, out of 13,657 patients, 336 (3%) modified treatment. • 90.2% switched, 8.9% temporarily discontinued, and 0.9% permanently discontinued. • Efficacy remains the main driving force behind switching in 58.4% of patients. clinical factors and frequency of disease-modifying therapy (DMT) changes/interruptions in relapsing-remitting multiple sclerosis (RRMS) patients have not been well defined. The aim of this study was to describe reasons of MS treatment modifications in a large cohort of Italian MS patients. this multicenter, cross-sectional non interventional study (SWITCH) conducted at 28 Italian MS centers, screened, by visit/telephone contact between June 2016 and June 2017, all RRMS patients receiving stable DMT treatment and enrolled patients with change in DMT treatment. out of 13,657 recorded in the log, 409 (3%) changed therapy. Of these, 336 (2.5%), met the study criteria and were considered eligible. Among 303 (90.2% of 336) patients switching, the most common reason was "lack of efficacy" (58.4% of 303). Among 30 (8.9%) patients who interrupted treatment temporarily, the most common reason was pregnancy (40.0% of 30). Out of 3 (0.9%) patients who discontinued treatment permanently, 2 (66.7%) had as first reason as "patient decision". Multivariate analysis showed that EDSS was the only variable with statistically significant effect on changing treatments (r = 8.33; p -value of Type III Sum of Squares = 0.016). in our study, 303 (90.2% of eligible patients) switched treatment, 30 (8.9%) interrupted treatment temporarily, and 3 (0.9%) discontinued treatment permanently. Efficacy remains the main driving force behind switching behavior, as the primary aim of treatment is to be disease free or reduce disease activity. [ABSTRACT FROM AUTHOR]