Online cognitive monitoring technology for people with Parkinson's disease and REM sleep behavioural disorder.

Automated online cognitive assessments are set to revolutionise clinical research and healthcare. However, their applicability for Parkinson's Disease (PD) and REM Sleep Behavioural Disorder (RBD), a strong PD precursor, is underexplored. Here, we developed an online battery to measure early cognitive changes in PD and RBD. Evaluating 19 candidate tasks showed significant global accuracy deficits in PD (0.65 SD, p = 0.003) and RBD (0.45 SD, p = 0.027), driven by memory, language, attention and executive underperformance, and global reaction time deficits in PD (0.61 SD, p = 0.001). We identified a brief 20-min battery that had sensitivity to deficits across these cognitive domains while being robust to the device used. This battery was more sensitive to early-stage and prodromal deficits than the supervised neuropsychological scales. It also diverged from those scales, capturing additional cognitive factors sensitive to PD and RBD. This technology offers an economical and scalable method for assessing these populations that can complement standard supervised practices.

Identifying definite patterns of unmet needs in patients with multiple sclerosis using unsupervised machine learning.

INTRODUCTION: People with multiple sclerosis (PwMS) exhibit a spectrum of needs that extend beyond solely disease-related determinants. Investigating unmet needs from the patient perspective may address daily difficulties and optimize care. Our aim was to identify patterns of unmet needs among PwMS and their determinants. METHODS: We conducted a cross-sectional multicentre study. Data were collected through an anonymous, self-administered online form. To cluster PwMS according to their main unmet needs, we performed agglomerative hierarchical clustering algorithm. Principal component analysis (PCA) was applied to visualize cluster distribution. Pairwise comparisons were used to evaluate demographics and clinical distribution among clusters. RESULTS: Out of 1764 mailed questionnaires, we received 690 responses. Access to primary care was the main contributor to the overall unmet need burden. Four patterns were identified: cluster C1, 'information-seekers with few unmet needs'; cluster C2, 'high unmet needs'; cluster C3, 'socially and assistance-dependent'; cluster C4, 'self-sufficient with few unmet needs'. PCA identified two main components in determining the patterns: the 'public sphere' (access to information and care) and the 'private sphere' (need for assistance and social life). Older age, lower education, longer disease duration and higher disability characterized clusters with more unmet needs in the private sphere. However, demographic and clinical factors failed in explaining the four identified patterns. CONCLUSION: Our study identified four unmet need patterns among PwMS, emphasizing the importance of personalized care. While clinical and demographic factors provide some insight, additional variables warrant further investigation to fully understand unmet needs in PwMS.

The impact of healthcare systems on the clinical diagnosis and disease-modifying treatment usage in relapse-onset multiple sclerosis: a real-world perspective in five registries across Europe.

Introduction: Prescribing guidance for disease-modifying treatment (DMT) in multiple sclerosis (MS) is centred on a clinical diagnosis of relapsing-remitting MS (RRMS). DMT prescription guidelines and monitoring vary across countries. Standardising the approach to diagnosis of disease course, for example, assigning RRMS or secondary progressive MS (SPMS) diagnoses, allows examination of the impact of health system characteristics on the stated clinical diagnosis and treatment access. Methods: We analysed registry data from six cohorts in five countries (Czech Republic, Denmark, Germany, Sweden and United Kingdom) on patients with an initial diagnosis of RRMS. We standardised our approach utilising a pre-existing algorithm (DecisionTree, DT) to determine patient diagnoses of RRMS or secondary progressive MS (SPMS). We identified five global drivers of DMT prescribing: Provision, Availability, Funding, Monitoring and Audit, data were analysed against these concepts using meta-analysis and univariate meta-regression. Results: In 64,235 patients, we found variations in DMT use between countries, with higher usage in RRMS and lower usage in SPMS, with correspondingly lower usage in the UK compared to other registers. Factors such as female gender (p = 0.041), increasing disability via Expanded Disability Status Scale (EDSS) score (p = 0.004), and the presence of monitoring (p = 0.029) in SPMS influenced the likelihood of receiving DMTs. Standardising the diagnosis revealed differences in reclassification rates from clinical RRMS to DT-SPMS, with Sweden having the lowest rate Sweden (Sweden 0.009, range: Denmark 0.103 - UK portal 0.311). Those with higher EDSS at index (p < 0.03) and female gender (p < 0.049) were more likely to be reclassified from RRMS to DT-SPMS. The study also explored the impact of diagnosis on DMT usage in clinical SPMS, finding that the prescribing environment and auditing practices affected access to treatment. Discussion: This highlights the importance of a healthcare system's approach to verifying the clinical label of MS course in facilitating appropriate prescribing, with some flexibility allowed in uncertain cases to ensure continued access to treatment.

Mapping the sociodemographic distribution and self-reported justifications for non-compliance with COVID-19 guidelines in the United Kingdom.

Which population factors have predisposed people to disregard government safety guidelines during the COVID-19 pandemic and what justifications do they give for this non-compliance? To address these questions, we analyse fixed-choice and free-text responses to survey questions about compliance and government handling of the pandemic, collected from tens of thousands of members of the UK public at three 6-monthly timepoints. We report that sceptical opinions about the government and mainstream-media narrative, especially as pertaining to justification for guidelines, significantly predict non-compliance. However, free text topic modelling shows that such opinions are diverse, spanning from scepticism about government competence and self-interest to full-blown conspiracy theories, and covary in prevalence with sociodemographic variables. These results indicate that attempts to counter non-compliance through argument should account for this diversity in peoples' underlying opinions, and inform conversations aimed at bridging the gap between the general public and bodies of authority accordingly.

Patient-reported outcomes in multiple sclerosis: a prospective registry cohort study.

Registries have the potential to tackle some of the current limitations in determining the long-term impact of multiple sclerosis. Online assessments using patient-reported outcomes can streamline follow-up enabling large-scale, long-term, cost-effective, home-based, and patient-focused data collection. However, registry data are sparsely sampled and the sensitivity of patient-reported outcomes relative to clinician-reported scales is unknown, making it hard to fully leverage their unique scope and scale to derive insights. This retrospective and prospective cohort study over 11 years involved 15 976 patients with multiple sclerosis from the United Kingdom Multiples Sclerosis Register. Primary outcomes were changes in two patient-reported outcomes: Multiple Sclerosis Impact Scale motor component, and Multiple Sclerosis Walking Scale. First, we investigated their validity in measuring the impact of physical disability in multiple sclerosis, by looking at their sensitivity to disease subtype and duration. We grouped the available records (91 351 for Multiple Sclerosis Impact Scale motor and 68 092 for Multiple Sclerosis Walking Scale) by these two factors, and statistically compared the resulting groups using a novel approach based on Monte Carlo permutation analysis that was designed to cope with the intrinsic sparsity of registry data. Next, we used the patient-reported outcomes to draw novel insights into the developmental time course of subtypes; in particular, the period preceding the transition from relapsing to progressive forms. We report a robust main effect of disease subtype on the patient-reported outcomes and interactions of disease subtype with duration (all P < 0.0001). Specifically, patient-reported outcomes worsen with disease duration for all subtypes (all P < 0.0001) apart from benign multiple sclerosis (Multiple Sclerosis Impact Scale motor: P = 0.796; Multiple Sclerosis Walking Scale: P = 0.983). Furthermore, the patient-reported outcomes of each subtype are statistically different from those of the other subtypes at all time bins (Multiple Sclerosis Impact Scale motor: all P < 0.05; Multiple Sclerosis Walking Scale: all P < 0.01) except when comparing relapsing-remitting multiple sclerosis with benign multiple sclerosis and primary progressive multiple sclerosis with secondary progressive multiple sclerosis. Notably, there were statistically significant differences between relapsing-remitting and progressive subtypes at disease onset. Critically, the patient-reported outcomes are sensitive to future transitions to progressive subtypes, with individuals who transition presenting with higher patient-reported outcomes in their relapsing-remitting phase compared to individuals who don't transition since onset (all P < 0.0001). Patient-reported outcomes capture different patterns of physical worsening over disease length and across subtypes; therefore, they are a valid tool to measure the physical impact of multiple sclerosis over the long-term and cost-effectively. Furthermore, more advanced physical disability manifests years before clinical detection of progressive subtypes, adding evidence to the presence of a multiple sclerosis prodrome.