The effect of timing of high-efficacy therapy on processing speed performance in multiple sclerosis

Longitudinal Trajectories of Digital Cognitive Biomarkers for Multiple Sclerosis

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.

Uncovering heterogeneous cognitive trajectories in relapsing-remitting multiple sclerosis: a longitudinal study

BACKGROUND

Cognitive impairment (CI) frequently occurs in relapsing-remitting multiple sclerosis (RRMS) and is assumed to be irreversible. Recent longitudinal studies highlighted the heterogeneity of CI in RRMS, challenging the traditional view of inevitable progression. Longitudinal studies exploring the baseline determinants of future cognitive decline are limited, and none yet explored the predictive value of patient-reported outcome measures (PROMs).

OBJECTIVE

To explore the evolutionary patterns of cognitive status in a cohort of RRMS patients initiating a new disease modifying treatment, and to determine whether PROMs may have a predictive value for future cognitive decline.

METHODS

This prospective study is a 36-month follow-up of 59 RRMS patients who underwent yearly a comprehensive, multiparametric assessment combining clinical, neuropsychological, MRI-derived metrics and a set of self-reported questionnaires. Lesion load and brain volumes were analyzed and processed by the automated MSmetrix® software (Icometrix®, Leuven, Belgium). A longitudinal logistic regression analysis was performed to investigate baseline predictors of future cognitive decline.

RESULTS

A total of 33 (56%) and 17 (35%) patients were defined as cognitively impaired at baseline and at the end of the study, respectively. Of these 33 patients, 20 showed either improvement and/or impairment in fewer cognitive domains at 36-month follow-up. Baseline physical disability as measured by EDSS was the best predictor for future cognitive decline (OR: 2.17; p = 0.03, 95% confidence interval = 1.07-4.38). None of the PROMs variables contributed to predict further cognitive decline.

CONCLUSIONS

Our findings highlight the importance of considering the evolution of CI in MS as a dynamic phenomenon with a substantial heterogeneity.

Attitudes of Neurologists Toward Serum Neurofilament Light-Chain Testing in the Management of Relapsing-Remitting Multiple Sclerosis with Cognitive Impairment

Background: Cognitive impairment has an impact upon the function and quality of life of patients with multiple sclerosis (MS). High-serum neurofilament light-chain (sNfL) levels predict disease progression and are also associated with impaired cognitive performance. This study aimed to assess the attitudes of neurologists toward sNfL testing as regards making therapeutic decisions in clinically and radiologically stable patients experiencing cognitive decline. Methods: A web-based observational study was conducted among neurologists caring for patients with MS. The role of sNfL in therapeutic decisions was assessed through a simulated case scenario describing a 31-year-old woman with relapsing-remitting MS for four years on glatiramer acetate. Her partner reported increased distractibility and difficulties in organizing daily activities over the past 18 months. There was no history of new relapses, and a follow-up brain MRI scan showed no new lesions. Her performance in the Symbol Digit Modalities Test decreased by 8 points from the previous year, with 46 correct answers. The patient had an sNfL level of 21 pg/mL, with no other identified factors that could have altered this value. The participants were tasked with deciding to either escalate treatment or to continue the current treatment and schedule the patient for reassessment in 6-12 months (defined as decisions misaligned with emerging evidence [DMEE]). Multivariate regression analysis was conducted to determine factors associated with DMEE. Results: One hundred and sixteen neurologists participated in the study. Almost 50% of the participants (n = 57) opted not to escalate treatment despite high sNfL levels. This was more common among neurologists not fully dedicated to MS care (60.5% vs. 43.6%). The multivariate analysis showed that being a neurologist not fully dedicated to MS (odds ratio [OR] = 2.35, 95% confidence interval [CI] 1.01-5.50; p = 0.04) and having a poor perception of sNfL benefits (OR = 1.02, 95% CI 1.00-1.04; p = 0.01) were associated with DMEE. Conclusions: Neurologists' lack of full dedication to MS care and limited perception of sNfL's clinical utility were key factors associated with suboptimal therapeutic decisions in a simulated case of cognitive decline with elevated sNfL. These findings underscore the need for increased education on the role of sNfL to improve evidence-based decision-making in MS management.

Cognitive changes in patients with relapse-free MS treated with high efficacy therapies: the predictive value of paramagnetic rim lesions

BACKGROUND

High-efficacy disease-modifying therapies (HETs) have substantially improved multiple sclerosis (MS) management, yet ongoing cognitive decline remains a concern. This study aims to assess Symbol Digit Modalities Test (SDMT) changes in patients with stable relapsing-remitting MS (RRMS) treated with HETs and to evaluate the role of baseline MRI biomarkers as predictors of SDMT changes.

METHODS

Consecutive patients with RRMS treated with HETs underwent clinical, SDMT and MRI assessment at baseline with SDMT and clinical re-evaluation after 24 months. Patients presenting relapses or MRI activity (new T2 and/or gadolinium-enhancing lesions) during follow-up were excluded. Cognitive changes were defined using the 90% CI regression-based reliable change index methodology accounting for sex, age, education and baseline score. Baseline MRI examination included three-dimensional-sagittal Fluid Attenuated Inversion Recovery (FLAIR), T1-Magnetization Prepared - RApid Gradient Echo (T1-MPRAGE) and quantitative susceptibility mapping (QSM) for paramagnetic rim lesions (PRLs) and QSM-isointense lesions (ISO) assessment. Univariate and multivariable regression analyses were performed to predict SDMT changes.

RESULTS

90 patients (mean age: 40.3 years, median Expanded Disability Status Scale: 2.0) were included. PRLs were present in 46 (51.1%) patients. After 24 months, 13 (14.4%) patients showed SDMT decline and 8 (8.9%) showed improvement. At multivariable analyses, PRLs were associated with higher risk of SDMT decline (β: 2.70, p: 0.02, OR: 14.82) while higher ISO lesion volumes were weakly associated with SDMT improvement (β: 0.07, p: 0.01, OR: 1.07).

CONCLUSIONS

SDMT decline and improvement are detectable in patients with RRMS without clinical or MRI activity over 2 years. PRLs seem to predict SDMT decline in MS, underscoring the critical role of compartmentalised chronic inflammation in disease progression.

Brief international cognitive assessment for MS (BICAMS) and NEDA maintenance in MS patients: A 2-year follow-up longitudinal study

BACKGROUND

The Brief International Cognitive Assessment for Multiple Sclerosis (BICAMS) has been validated in many cross-sectional studies. However, longitudinal data on BICAMS subset trajectories and their correlation with disease activity during follow-up are scarce.

OBJECTIVES

We aimed to (i) assess BICAMS changes in MS patients initiating high-efficacy disease-modifying-treatments (DMTs), (ii) compare these changes based on maintenance of "no-evidence-of-disease-activity" (NEDA-3) status over 24 months, and (iii) determine baseline clinical parameters predictive of cognitive changes.

METHODS

We enrolled 101 MS patients (mean age:40,45 ± 11; Relapsing-Remitting-MS:81%) initiating highly-effective-DMTs. Patients underwent Expanded Disability Status Scale (EDSS), BICAMS, and Hospital Anxiety and Depression Scale (HADS), at baseline and after 24 months. Regression-based change index (RB-CI) had been used for cognitive change evaluation over follow-up.

RESULTS

During follow-up, 78 (77.3%) patients maintained NEDA-3 status. Considering a 90% of confidence levels for RB-CI, 12 (11.9%) improved at SDMT, 13 (12.9%) at CVLT-II and 13 (12.9%) at BVMT-R; while 7 (6.9%) were classified as worsened at SDMT, 11 (10.9%) at CVLT-II and 8 (7.9%) at BVMT-R. SDMT scores significantly improved at follow-up for the entire group (p = 0.003) and in patients maintaining NEDA-3 (p < 0.001). The multivariable regression model assessing the SDMT improvement (n = 12; z = 1.65), was significant and explained 21% of the variance (p = 0.038; Nagelkerke R2 = 0.212). Lower EDSS proved to be an independent predictor of SDMT reliable improvement (p = 0.027) in our sample.

CONCLUSIONS

Our findings showed that early disease activity control-especially in patients with low baseline disability-may yield significant benefits even in terms of cognitive performance.

Cognitive outcomes in late-onset versus adult-onset Multiple Sclerosis

INTRODUCTION

Recent studies show that cognitive impairment is more prevalent in older patients with Multiple Sclerosis (MS). However, whether this is the result of several years of a chronic disease or specific age-related changes is still unclear. Therefore, we aim to assess the outcomes in both classic and social cognition in late-onset MS (LOMS) and compare them to adult-onset MS (AOMS) when accounting for age and disease duration.

METHODS

In this cross-sectional study, a group of 27 LOMS patients (age of disease onset >50 years) was compared with patients with AOMS (age of disease onset between 18 and 50 years). Patients with AOMS were grouped based on age (AOAMS, n = 27) and on disease duration (AODMS, n = 27) in order that these variables are matched with LOMS. Their cognitive performance was evaluated using the Brief International Cognitive Assessment for Multiple Sclerosis (BICAMS) and the Revised "Reading the Mind in the Eyes" Test (RMET). Clinical and demographic variables were collected and analysed.

RESULTS

In general, both classic and social cognitive performance was inferior in the LOMS group when accounting for age and disease duration. We found a statistically significant negative correlation between age of disease onset and performance in all cognitive domains except for verbal memory. The presence of at least one vascular risk factor (VRF) was associated with slower information-processing speed (SDMT) (p = 0.006) and poorer RMET performance (p = 0.020).

DISCUSSION

A later age of MS is associated with worse cognitive functioning possibly due to the loss of neuroplasticity in an already aged brain.

CONCLUSION

Patients with LOMS have worse cognitive outcomes than AOMS in both classic and social domains, especially when associated with the presence of VRF. Hence, health care providers and patients should not undervalue the importance of cognitive stimulating activities, management of VFR and socialization in this specific group of patients.

Cognitive impairment in multiple sclerosis: from phenomenology to neurobiological mechanisms

Multiple sclerosis (MS) is an autoimmune-mediated disease of the central nervous system characterized by inflammation, demyelination and chronic progressive neurodegeneration. Among its broad and unpredictable range of clinical symptoms, cognitive impairment (CI) is a common and disabling feature greatly affecting the patients' quality of life. Its prevalence is 20% up to 88% with a wide variety depending on the phenotype of MS, with highest frequency and severity in primary progressive MS. Involving different cognitive domains, CI is often associated with depression and other neuropsychiatric symptoms, but usually not correlated with motor and other deficits, suggesting different pathophysiological mechanisms. While no specific neuropathological data for CI in MS are available, modern research has provided evidence that it arises from the disease-specific brain alterations. Multimodal neuroimaging, besides structural changes of cortical and deep subcortical gray and white matter, exhibited dysfunction of fronto-parietal, thalamo-hippocampal, default mode and cognition-related networks, disruption of inter-network connections and involvement of the γ-aminobutyric acid (GABA) system. This provided a conceptual framework to explain how aberrant pathophysiological processes, including oxidative stress, mitochondrial dysfunction, autoimmune reactions and disruption of essential signaling pathways predict/cause specific disorders of cognition. CI in MS is related to multi-regional patterns of cerebral disturbances, although its complex pathogenic mechanisms await further elucidation. This article, based on systematic analysis of PubMed, Google Scholar and Cochrane Library, reviews current epidemiological, clinical, neuroimaging and pathogenetic evidence that could aid early identification of CI in MS and inform about new therapeutic targets and strategies.

Identification and management of subclinical disease activity in early multiple sclerosis: a review

IMPORTANCE

Early treatment initiation in multiple sclerosis (MS) is crucial in preventing irreversible neurological damage and disability progression. The current assessment of disease activity relies on relapse rates and magnetic resonance imaging (MRI) lesion activity, but inclusion of other early, often "hidden," indicators of disease activity may describe a more comprehensive picture of MS.

OBSERVATIONS

Early indicators of MS disease activity other than relapses and MRI activity, such as cognitive impairment, brain atrophy, and fatigue, are not typically captured by routine disease monitoring. Furthermore, silent progression (neurological decline not clearly captured by standard methods) may occur undetected by relapse and MRI lesion activity monitoring. Consequently, patients considered to have no disease activity actually may have worsening disease, suggesting a need to revise MS management strategies with respect to timely initiation and escalation of disease-modifying therapy (DMT). Traditionally, first-line MS treatment starts with low- or moderate-efficacy therapies, before escalating to high-efficacy therapies (HETs) after evidence of breakthrough disease activity. However, multiple observational studies have shown that early initiation of HETs can prevent or reduce disability progression. Ongoing randomized clinical trials are comparing escalation and early HET approaches.

CONCLUSIONS AND RELEVANCE

There is an urgent need to reassess how MS disease activity and worsening are measured. A greater awareness of "hidden" indicators, potentially combined with biomarkers to reveal silent disease activity and neurodegeneration underlying MS, would provide a more complete picture of MS and allow for timely therapeutic intervention with HET or switching DMTs to address suboptimal treatment responses.

Predictive models of multiple sclerosis-related cognitive performance using routine clinical practice predictors

BACKGROUND

The application of machine learning (ML) to predict cognitive evolution is exceptionally scarce. Computer-based self-administered cognitive tests provide the opportunity to set up large longitudinal datasets to aid in developing ML prediction models of risk for Multiple Sclerosis-related cognitive decline.

OBJECTIVE

to analyze to what extent clinically feasible models can be built with standard clinical practice features and subsequently used for reliable prediction of cognitive evolution.

METHODS

This prospective longitudinal study includes 1184 people with MS who received a Processing Speed (PS) evaluation at 12 months of follow-up measured by the iPad®-based Processing Speed Test (PST). Six of the most potent classification models built with routine clinical practice features were trained and tested to predict the 12-month patient class label (PST worsening (PSTw) versus PST stable). A rigorous scheme of all the preprocessing steps run to obtain reliable generalization performance is detailed.

RESULTS

Based on a 12-month reduction of 10% of the PST raw score, 187/1184 (15.8%) people with MS were classified as PSTw. The trees-based models (random forest and the eXtreme Gradient Boosting) achieved the best performance, with an area under the receiver operating characteristic curve (AUC) of 0.90 and 0.89, respectively. The timing of high-efficacy disease-modifying therapies (heDMTs) was identified as one of the top importance predictors in all the models evaluated.

CONCLUSION

Using trees-based machine learning models to predict individual future information processing speed deterioration in MS could become a reality in clinical practice.