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Background: Multiple sclerosis (MS) cognitive tests are resource intensive and limited by practice effects that prevent frequent retesting. Brief, reliable and valid monitoring tools are urgently needed to detect subtle, subclinical cognitive changes in people with MS. Cognitive monitoring over time could contribute to a new definition of disease progression, supplementing routine clinical monitoring. Methods: MSReactor is a web-based battery that measures psychomotor (processing) speed, visual attention and working memory, using simple reaction time tasks. Clinic-based tasks were completed at baseline and 6 monthly with home testing 1–3 monthly. Acceptability, quality of life, depression and anxiety surveys were completed. We studied its correlation with the Symbol Digit Modalities Test, practice effects, test–retest reliability and the discriminative ability of MSReactor. Results: A total of 450 people with MS were recruited over 18 months, with 81% opting to complete home-based testing. Most participants (96%) would be happy (or neutral) to repeat the tasks again and just four reported the tasks made them ‘very anxious’. Persistence of home testing was high and practice effects stabilized within three tests. MSReactor tasks correlated with Symbol Digit Modalities Test scores and participants with MS performed slower than healthy controls. Conclusion: MSReactor is a scalable and reliable cognitive screening tool that can be used in the clinic and remotely. MSReactor task performance correlated with another highly validated cognitive test, was sensitive to MS and baseline predictors of cognitive performance were identified.

Background Cognitive impairment is one of the most common and debilitating symptoms of relapsing–remitting multiple sclerosis (RRMS). Digital cognitive biomarkers require less time and resources and are rapidly gaining popularity in clinical settings. We examined the longitudinal trajectory of the iPad‐based Processing Speed Test (PST) and predictors of PST scores. Methods We prospectively enrolled RRMS patients between 2017 and 2021 across six Australian MS centres. Longitudinal data was analysed with mixed effect modelling and latent class mixed models. We then examined whether latent class group membership predicted confirmed decrease in correct PST responses. Results We recruited a total of 1093 participants, of which 724 had complete baseline data with a median follow up duration of 2 years. At a population level, PST trajectory was stable. A small practice effect was present up to the 4th visit. Age, baseline disability, T2 lesion volume, male sex and depression were associated with lower correct PST responses, whilst years of education and full/part‐time employment were associated with more correct PST responses. We identified four latent class trajectories of PST. The worst latent class was typified by low baseline PST and lack of a practice effect. Being in the worst latent class was associated with a greater hazard of time to sustained 5% decrease in PST (HR 2.84, 95% CI 1.16–6.94, p = 0.02). Conclusion Worse baseline cognitive performance and lack of a practice effect predicted future cognitive decline in RRMS.

BackgroundUpper limb dysfunction is a common debilitating feature of relapsing remitting multiple sclerosis (RRMS). We aimed to examine the longitudinal trajectory of the iPad-based Manual Dexterity Test (MDT) and predictors of change over time.MethodsWe prospectively enrolled relapsing-remitting multiple sclerosis (RRMS) patients with an EDSS score of less than four. Longitudinal data was analysed with mixed effect modelling and latent class mixed models. We examined whether group membership in latent classes were predictive of a confirmed decrease in MDT.ResultsAt a population level, MDT remained stable over time. No practice effect was seen. Baseline disability and T2 lesion volume were the strongest predictors of longitudinal MDT performance.Latent class analysis identified 2 classes of MDT trajectories. In the slower trajectory, greater variability and a weak association with sustained worsening of MDT was present. Group trajectory based on latent class analysis and baseline MDT was different, signifying the importance of latent processes in upper limb function in pwMS.ConclusionIn this cohort of mild to moderate RRMS, MDT scores remained stable over time with no evidence of a practice effect at a population level. Trajectory analysis based on latent class identified a cohort with greater variability and risk of sustained worsening. Our findings show the importance of latent processes in determining upper limb function trajectories in pwMS.

BackgroundCognitive impairment is one of the most common and debilitating symptoms of relapsing remitting multiple sclerosis (RRMS). Digital cognitive biomarkers require less time and resources and are rapidly gaining popularity in clinical settings. We examined the longitudinal trajectory of the iPad-based Processing Speed Test (PST) and predictors of change over time.MethodsWe prospectively enrolled relapsing-remitting multiple sclerosis (RRMS) patients with an EDSS score of less than four. Longitudinal data was analysed with mixed effect modelling and latent class mixed models.ResultsAt a population level, PST trajectory was stable. A small practice effect was present up to the 4th visit. Age, baseline disability, T2 lesion volume, male gender and depression were associated with less correct PST responses, whilst years of education and full/part-time employment were associated with more correct PST responses.We identified four trajectories of processing speed with latent class analysis. The lowest latent class was typified by the lack of a practice effect and was associated with a greater hazard of time to sustained 5% decrease in PST (HR 2.84, 95%CI 1.16–6.94, p=0.02).ConclusionIn this cohort of mild to moderate RRMS, PST scores remained largely stable over time. Membership in the worst latent class trajectory was associated with a sustained 5% PST decrease. Poor cognitive performance at baseline and the lack of a practice effect is a predictor of future cognitive decline and should prompt early intervention for maximising cognitive function such as treatment escalation.

ObjectivesTo identify and validate longitudinal reaction time trajectories in relapsing remitting multiple sclerosis using a computerised cognitive battery and latent class mixed modelling, and to assess the association between reaction time trajectories and disability progression.MethodsParticipants serially completed web-based computerised reaction time tasks measuring psychomotor speed, visual attention and working memory. Testing sessions were completed 6-monthly with the option of additional home based testing. Participants who completed at least three testing sessions over a minimum of 180 days were included in the analysis. Longitudinal reaction times were modelled using Latent Class Mixed Models to group individuals sharing similar latent characteristics. Models were tested for consistency using a cross-validation approach. Inter-class differences in the probability of reaction time worsening and the probability of 6-month confirmed disability progression were assessed using survival analysis.ResultsA total of 460 relapsing remitting multiple sclerosis patients were included. For each task of the MSReactor computerised cognitive battery, the optimal model comprised of 3 latent classes. All tasks could identify a group with high probability of reaction time slowing. The visual attention and working memory tasks could identify a group of participants who were 3.7 and 2.6 times more likely to experience a 6-month confirmed disability progression, respectively. Participants could be classified into predicted cognitive trajectories after just 5 tests with between 64% and 89% accuracy.ConclusionLatent class modelling of longitudinal cognitive data collected by the MSReactor battery identified a group of patients with worsening reaction times and increased risk of disability progression.

Survival analysis holds a crucial role across diverse disciplines, such as economics, engineering and healthcare. It empowers researchers to analyze both time-invariant and time-varying data, encompassing phenomena like customer churn, material degradation and various medical outcomes. Given the complexity and heterogeneity of such data, recent endeavors have demonstrated successful integration of deep learning methodologies to address limitations in conventional statistical approaches. However, current methods typically involve cluttered probability distribution function (PDF), have lower sensitivity in censoring prediction, only model static datasets, or only rely on recurrent neural networks for dynamic modelling. In this paper, we propose a novel survival regression method capable of producing high-quality unimodal PDFs without any prior distribution assumption, by optimizing novel Margin-Mean-Variance loss and leveraging the flexibility of Transformer to handle both temporal and non-temporal data, coined UniSurv . Extensive experiments on several datasets demonstrate that UniSurv places a significantly higher emphasis on censoring compared to other methods.

BackgroundTo compare the effectiveness and treatment persistence of ocrelizumab, cladribine and natalizumab in patients with relapsing–remitting multiple sclerosis switching from fingolimod.MethodsUsing data from MSBase registry, this multicentre cohort study included subjects who had used fingolimod for ≥6 months and then switched to ocrelizumab, cladribine or natalizumab within 3 months after fingolimod discontinuation. We analysed relapse and disability outcomes after balancing covariates using an inverse-probability-treatment-weighting method. Propensity scores for the three treatments were obtained using multinomial-logistic regression. Due to the smaller number of cladribine users, comparisons of disability outcomes were limited to natalizumab and ocrelizumab.ResultsOverall, 1045 patients switched to ocrelizumab (n=445), cladribine (n=76) or natalizumab (n=524) after fingolimod. The annualised relapse rate (ARR) for ocrelizumab was 0.07, natalizumab 0.11 and cladribine 0.25. Compared with natalizumab, the ARR ratio (95% confidence interval [CI]) was 0.67 (0.47 to 0.96) for ocrelizumab and 2.31 (1.30 to 4.10) for cladribine; the hazard ratio (95% CI) for time to first relapse was 0.57 (0.40 to 0.83) for ocrelizumab and 1.18 (0.47 to 2.93) for cladribine. Ocrelizumab users had an 89% lower discontinuation rate (95% CI, 0.07 to 0.20) than natalizumab, but also a 51% lower probability of confirmed disability improvement (95% CI, 0.32 to 0.73). There was no difference in disability accumulation.ConclusionAfter fingolimod cessation, ocrelizumab and natalizumab were more effective in reducing relapses than cladribine. Due to the low ARRs in all three treatment groups, additional observation time is required to determine if statistical difference in ARRs results in long-term disability differences.

BackgroundOngoing controversy exists regarding optimal management of disease modifying therapy (DMT) in older people with multiple sclerosis (pwMS). There is concern that the lower relapse rate, combined with a higher risk of DMT-related infections and side effects, may alter the risk-benefit balance in older pwMS. Given the lack of pwMS above age 60 in randomised controlled trials, the comparative efficacy of high-efficacy DMTs such as ocrelizumab has not been shown in older pwMS. We aimed to evaluate the comparative effectiveness of ocrelizumab, a high-efficacy DMT, versus interferon/glatiramer acetate (IFN/GA) in pwMS over the age of 60.MethodsUsing data from MSBase registry, this multicentre cohort study included pwMS above 60 who switched to or started on ocrelizumab or IFN/GA. We analysed relapse and disability outcomes after balancing covariates using an inverse probability treatment weighting (IPTW) method. Propensity scores were obtained based on age, country, disease duration, sex, baseline Expanded Disability Status Scale, prior relapses (all-time, 12 months and 24 months) and prior DMT exposure (overall number and high-efficacy DMTs). After weighting, all covariates were balanced. Primary outcomes were time to first relapse and annualised relapse rate (ARR). Secondary outcomes were 6-month confirmed disability progression (CDP) and confirmed disability improvement (CDI).ResultsA total of 248 participants received ocrelizumab, while 427 received IFN/GA. The IPTW-weighted ARR for ocrelizumab was 0.01 and 0.08 for IFN/GA. The IPTW-weighted ARR ratio was 0.15 (95% CI 0.06 to 0.33, p<0.001) for ocrelizumab compared with IFN/GA. On IPTW-weighted Cox regression models, HR for time to first relapse was 0.13 (95% CI 0.05 to 0.26, p<0.001). The hazard of first relapse was significantly reduced in ocrelizumab users after 5 months compared with IFN/GA users. However, the two groups did not differ in CDP or CDI over 3.57 years.ConclusionIn older pwMS, ocrelizumab effectively reduced relapses compared with IFN/GA. Overall relapse activity was low. This study adds valuable real-world data for informed DMT decision making with older pwMS. Our study also confirms that there is a treatment benefit in older people with MS, given the existence of a clear differential treatment effect between ocrelizumab and IFN/GA in the over 60 age group.

Background and purpose The validity, reliability, and longitudinal performance of the Patient‐Determined Disease Steps (PDDS) scale is unknown in people with multiple sclerosis (MS) with mild to moderate disability. We aimed to examine the psychometric properties and longitudinal performance of the PDDS. Methods We included relapsing–remitting MS patients with an Expanded Disability Status Scale (EDSS) score of less than 4. Validity and test–retest reliability was examined. Longitudinal data were analysed with mixed‐effect modelling and Cohen's kappa for concordance in confirmed disability progression (CDP). Results We recruited a total of 1093 participants, of whom 904 had complete baseline data. The baseline correlation between PDDS and EDSS was weak (ρ = 0.45, p < 0.001). PDDS had stronger correlations with patient‐reported outcomes (PROs). Conversely, EDSS had stronger correlations with age, disease duration, Kurtzke's functional systems and processing speed test. PDDS test–retest reliability was good to excellent (concordance correlation coefficient = 0.73–0.89). Longitudinally, PDDS was associated with EDSS, age and depression. A higher EDSS score was associated with greater PDSS progression. The magnitude of these associations was small. There was no concordance in CDP as assessed by PDDS and EDSS. Conclusion The PDDS has greater correlation with other PROs but less correlation with other MS‐related outcome measures compared to the EDSS. There was little correlation between PDDS and EDSS longitudinally. Our findings suggest that the PDDS scale is not interchangeable with the EDSS.

Background Upper limb dysfunction is a common debilitating feature of relapsing‐remitting multiple sclerosis (RRMS). We aimed to examine the longitudinal trajectory of the iPad®‐based Manual Dexterity Test (MDT) and predictors of change over time. Methods We prospectively enrolled RRMS patients (limited to Expanded Disability Status Scale (EDSS) < 4). Longitudinal data was analysed using mixed‐effect modelling and latent class mixed models. We then examined whether group membership in latent classes predicted confirmed slowing in MDT. Results Seven hundred and twenty‐one participants had complete data for analysis. At a population level, MDT remained stable over time. No practice effect was seen. Baseline disability and T2 lesion volume were the strongest predictors of longitudinal MDT performance. We identified two latent class trajectories of MDT. The slower latent class was typified by greater variability and a weak association with confirmed worsening of MDT and EDSS. When compared to trajectory analysis stratified by baseline MDT, latent class analysis (LCA) was able to identify those at greater risk of confirmed slowing, signifying the importance of latent processes in upper limb function in pwMS. Conclusion In this cohort of mild to moderate RRMS, MDT scores remained stable over time with no evidence of a practice effect at a population level. Trajectory analysis based on LCA identified a cohort with greater variability and risk of disability progression and domain specific worsening. Our findings demonstrate the importance of latent processes in determining upper limb function in pwMS.