Previous studies have demonstrated poor knowledge of stroke among patients with established risk factors. This study aims to assess the baseline knowledge, among patients with increased risk for stroke in Oman, of warning symptoms of stroke, impending risk factors, treatment, and sources of information. In April 2005, trained family practice residents at Sultan Qaboos University Hospital Clinics (cardiology, neurology, diabetic, and lipid clinics), using a standardised, structured, pre-tested questionnaire, conducted a survey of 400 Omani patients. These patients all demonstrated potential risk factors for stroke. Only 35% of the subjects stated that the brain is the organ affected by a stroke, 68% correctly identified at least one symptom/sign of a stroke, and 43% correctly identified at least one stroke risk factor. The majority (62%) did not believe they were at increased risk for stroke, and 98% had not been advised by their attending physician that their clinical conditions were risk factors for stroke. In the multivariable logistic regression analysis, lower age and higher levels of education were associated with better knowledge regarding the organ involved in stroke, stroke symptoms, and risk factors. Because their knowledge about stroke risk factors was poor, the subjects in this study were largely unaware of their increased risk for stroke. Intensive health education is needed to improve awareness of stroke, especially among the most vulnerable groups.

Background and Objectives To evaluate the rate of return of disease activity after cessation of multiple sclerosis (MS) disease-modifying therapy. Methods This was a retrospective cohort study from 2 large observational MS registries: MSBase and OFSEP. Patients with relapsing-remitting MS who had ceased a disease-modifying therapy and were followed up for the subsequent 12 months were included in the analysis. The primary study outcome was annualized relapse rate in the 12 months after disease-modifying therapy discontinuation stratified by patients who did, and did not, commence a subsequent therapy. The secondary endpoint was the predictors of first relapse and disability accumulation after treatment discontinuation. Results A total of 14,213 patients, with 18,029 eligible treatment discontinuation epochs, were identified for 7 therapies. Annualized rates of relapse (ARRs) started to increase 2 months after natalizumab cessation (month 2-4 ARR 0.47, 95% CI 0.43–0.51). Commencement of a subsequent therapy within 2-4 months reduced the magnitude of disease reactivation (mean ARR difference: 0.15, 0.08–0.22). After discontinuation of fingolimod, rates of relapse increased overall (month 1–2 ARR: 0.80, 0.70–0.89) and stabilized faster in patients who started a new therapy within 1-2 months (mean ARR difference: 0.14, −0.01 to 0.29). The magnitude of disease reactivation for other therapies was low but reduced further by commencement of another treatment 1–10 months after treatment discontinuation. Predictors of relapse were a higher relapse rate in the year before cessation, female sex, younger age, and higher EDSS score. Commencement of a subsequent therapy reduced both the risk of relapse (HR 0.76, 95% CI 0.72–0.81) and disability accumulation (0.73, 0.65–0.80). Discussion The rate of disease reactivation after treatment cessation differs among MS treatments, with the peaks of relapse activity ranging from 1 to 10 months in untreated cohorts that discontinued different therapies. These results suggest that untreated intervals should be minimized after stopping antitrafficking therapies (natalizumab and fingolimod). Classification of Evidence This study provides Class III that disease reactivation occurs within months of discontinuation of MS disease-modifying therapies. The risk of disease activity is reduced by commencement of a subsequent therapy.

In aquaporin-4 antibody-positive neuromyelitis optica spectrum disorder (AQP4-IgG NMOSD), disability accrual is mostly attributed to relapses. This study aimed to assess the prevalence of progression independent of relapse activity (PIRA) and relapse-associated worsening (RAW) in AQP4-IgG NMOSD. This was a retrospective cohort study of patients with AQP4-IgG NMOSD enrolled in the MSBase international data registry. Patients required a minimum of 3 recorded Expanded Disability Status Scale (EDSS) scores: baseline, event, and a 6-month confirmation score. Presence and absence of relapses between the baseline and event EDSS scores determined RAW and PIRA, respectively. Descriptive statistics were used to present the results. A total of 181 patients followed for a median of 4.5 years (Q1 1.7, Q3 7.8) were included. Most patients were female (88.4%), and the median age at disease onset was 38.1 years. Overall, 4 patients (2.2%) developed 5 incidences of PIRA and 13 patients developed RAW (7.2%). This multicenter study highlights that PIRA is very rare in AQP4-IgG NMOSD. Limitations of this study include the sole focus of overall EDSS to measure disability, lack of requirement for a second EDSS score to confirm baseline EDSS, and the absence of magnetic resonance imaging information for all patients.

Background: Disability progression is a key milestone in the disease evolution of people with multiple sclerosis (PwMS). Prediction models of the probability of disability progression have not yet reached the level of trust needed to be adopted in the clinic. A common benchmark to assess model development in multiple sclerosis is also currently lacking. Methods: Data of adult PwMS with a follow-up of at least three years from 146 MS centers, spread over 40 countries and collected by the MSBase consortium was used. With basic inclusion criteria for quality requirements, it represents a total of 15, 240 PwMS. External validation was performed and repeated five times to assess the significance of the results. Transparent Reporting for Individual Prognosis Or Diagnosis (TRIPOD) guidelines were followed. Confirmed disability progression after two years was predicted, with a confirmation window of six months. Only routinely collected variables were used such as the expanded disability status scale, treatment, relapse information, and MS course. To learn the probability of disability progression, state-of-the-art machine learning models were investigated. The discrimination performance of the models is evaluated with the area under the receiver operator curve (ROC-AUC) and under the precision recall curve (AUC-PR), and their calibration via the Brier score and the expected calibration error. All our preprocessing and model code are available at https://gitlab.com/edebrouwer/ms_benchmark, making this task an ideal benchmark for predicting disability progression in MS. Findings: Machine learning models achieved a ROC-AUC of 0⋅71 ± 0⋅01, an AUC-PR of 0⋅26 ± 0⋅02, a Brier score of 0⋅1 ± 0⋅01 and an expected calibration error of 0⋅07 ± 0⋅04. The history of disability progression was identified as being more predictive for future disability progression than the treatment or relapses history. Conclusions: Good discrimination and calibration performance on an external validation set is achieved, using only routinely collected variables. This suggests machine-learning models can reliably inform clinicians about the future occurrence of progression and are mature for a clinical impact study.