Should We Use Clinical Tools to Identify Disease Progression?

Modeling and simulation for prediction of multiple sclerosis progression

In light of extensive work that has created a wide range of techniques for predicting the course of multiple sclerosis (MS) disease, this paper attempts to provide an overview of these approaches and put forth an alternative way to predict the disease progression. For this purpose, the existing methods for estimating and predicting the course of the disease have been categorized into clinical, radiological, biological, and computational or artificial intelligence-based markers. Weighing the weaknesses and strengths of these prognostic groups is a profound method that is yet in need and works directly at the level of diseased connectivity. Therefore, we propose using the computational models in combination with established connectomes as a predictive tool for MS disease trajectories. The fundamental conduction-based Hodgkin-Huxley model emerged as promising from examining these studies. The advantage of the Hodgkin-Huxley model is that certain properties of connectomes, such as neuronal connection weights, spatial distances, and adjustments of signal transmission rates, can be taken into account. It is precisely these properties that are particularly altered in MS and that have strong implications for processing, transmission, and interactions of neuronal signaling patterns. The Hodgkin-Huxley (HH) equations as a point-neuron model are used for signal propagation inside a small network. The objective is to change the conduction parameter of the neuron model, replicate the changes in myelin properties in MS and observe the dynamics of the signal propagation across the network. The model is initially validated for different lengths, conduction values, and connection weights through three nodal connections. Later, these individual factors are incorporated into a small network and simulated to mimic the condition of MS. The signal propagation pattern is observed after inducing changes in conduction parameters at certain nodes in the network and compared against a control model pattern obtained before the changes are applied to the network. The signal propagation pattern varies as expected by adapting to the input conditions. Similarly, when the model is applied to a connectome, the pattern changes could give an insight into disease progression. This approach has opened up a new path to explore the progression of the disease in MS. The work is in its preliminary state, but with a future vision to apply this method in a connectome, providing a better clinical tool.

Understanding the Symptoms and Impacts Experienced by People with Relapsing-Remitting MS: A Qualitative Investigation Using Semi-Structured Interviews

INTRODUCTION

Multiple sclerosis (MS) is a disabling disease with unpredictable clinical manifestations. As clinical assessments may not fully capture the impact of MS on quality of life, they can be complemented by patient-reported outcome (PRO) measures to provide a more comprehensive picture of MS disease state and impact. The objectives of this study were to explore the experiences of people with relapsing-remitting MS, including symptoms and impacts on daily life, and to provide a conceptual model of MS outcomes.

METHODS

A literature review of studies that evaluated the experiences of people with MS was completed and combined with semi-structured concept elicitation interviews conducted with 14 people with relapsing-remitting MS in the USA.

RESULTS

The average age of the 14 participants was 43.9 (range 25-64) years, most were White (78.6%) and female (78.6%), and the mean duration since diagnosis was 6.6 (2-10) years. The most bothersome symptoms identified included fatigue (n = 9), cognitive dysfunction (n = 5), mobility/difficulty with walking (n = 3), and vision problems (n = 3). The most commonly reported impacts on daily life were balance problems/instability (n = 13), work life/productivity (n = 12), difficulty walking (n = 11), daily activities/household chores (n = 11), and leisure activities (n = 10).

CONCLUSION

There was a high frequency of concepts associated with physical function, fatigue, and sensory-motor actions. A conceptual model was developed that captures the disease symptoms, impairments, and impacts identified in the interviews as well as known processes and symptoms identified in the literature search. This model underpins the appropriateness of PRO instruments, such as the PROMIS Fatigue (MS) 8a and PROMIS Physical Function (MS) 15a, which evaluate symptoms and impacts that matter most to people with MS.

The administration of the paper and electronic versions of the Manual Ability Measure-36 (MAM-36) and Fatigue Severity Scale (FSS) is equivalent in people with multiple sclerosis

BACKGROUND

The mobile device diffusion has increasingly highlighted the opportunity to collect patient-reported outcomes (PROs) through electronic patient-reported outcomes measurements (ePROMs) during the clinical routine. Despite the ePROMs promises and advantages, the equivalence when a PRO measure is moved from the original paper-and-pencil to the electronic version is still little investigated. This study aims at evaluating equivalence between PROMs and ePROMs self-administration in people with multiple sclerosis (PwMS); in addition, preference of self-administration type was evaluated.

METHODS

The Manual Ability Measure-36 (MAM-36) and Fatigue Severity Scale (FSS) were selected for the equivalence test. The app ABOUTCOME was developed through a user-centered design approach to administer the questionnaires on tablet. Both paper-and-pencil and electronic versions were randomly self-administered. Intrarater reliability between both versions was evaluated through the intraclass correlation coefficient (ICC, excellent for values ≥ 0.75).

RESULTS

Fifty PwMS (35 females) participated to the study (mean age: 54.7±11.0 years, disease course: 27 relapsing-remitting and 23 progressive; mean EDSS: 4.7±1.9; mean disease duration: 13.3±9.5 years). No statistically significant differences were found for the means total scores of MAM-36 (p = 0.61) and FSS (p = 0.78). The ICC value for MAM-36 and FSS was excellent (0.98 and 0.94, respectively). Most of participants preferred the tablet version (84%).

CONCLUSION

The results of the study provide evidence about the equivalence between the paper-and-pencil and electronic versions of PROs administration. In addition, PwMS prefer electronic methods rather than paper because the information can be provided more efficiently and accurately. The results could be easily extended to other MS PROs.

Current knowledge on multiple sclerosis pathophysiology, disability progression assessment and treatment options, and the role of autologous hematopoietic stem cell transplantation

Multiple sclerosis (MS) is an autoimmune disease of the central nervous system (CNS) that affects nearly 2.8 million people each year. MS distinguishes three main types: relapsing-remitting MS (RRMS), secondary progressive MS (SPMS) and primary progressive MS (PPMS). RRMS is the most common type, with the majority of patients eventually progressing to SPMS, in which neurological development is constant, whereas PPMS is characterized by a progressive course from disease onset. New or additional insights into the role of effector and regulatory cells of the immune and CNS systems, Epstein-Barr virus (EBV) infection, and the microbiome in the pathophysiology of MS have emerged, which may lead to the development of more targeted therapies that can halt or reverse neurodegeneration. Depending on the type and severity of the disease, various disease-modifying therapies (DMTs) are currently used for RRMS/SPMS and PPMS. As a last resort, and especially in highly active RRMS that does not respond to DMTs, autologous hematopoietic stem cell transplantation (AHSCT) is performed and has shown good results in reducing neuroinflammation. Nevertheless, the question of its potential role in preventing disability progression remains open. The aim of this review is to provide a comprehensive update on MS pathophysiology, assessment of MS disability progression and current treatments, and to examine the potential role of AHSCT in preventing disability progression.

Building a monitoring matrix for the management of multiple sclerosis

Multiple sclerosis (MS) has a longitudinal and heterogeneous course, with an increasing number of therapy options and associated risk profiles, leading to a constant increase in the number of parameters to be monitored. Even though important clinical and subclinical data are being generated, treating neurologists may not always be able to use them adequately for MS management. In contrast to the monitoring of other diseases in different medical fields, no target-based approach for a standardized monitoring in MS has been established yet. Therefore, there is an urgent need for a standardized and structured monitoring as part of MS management that is adaptive, individualized, agile, and multimodal-integrative. We discuss the development of an MS monitoring matrix which can help facilitate data collection over time from different dimensions and perspectives to optimize the treatment of people with MS (pwMS). In doing so, we show how different measurement tools can combined to enhance MS treatment. We propose to apply the concept of patient pathways to disease and intervention monitoring, not losing track of their interrelation. We also discuss the use of artificial intelligence (AI) to improve the quality of processes, outcomes, and patient safety, as well as personalized and patient-centered care. Patient pathways allow us to track the patient's journey over time and can always change (e.g., when there is a switch in therapy). They therefore may assist us in the continuous improvement of monitoring in an iterative process. Improving the monitoring process means improving the care of pwMS.

Standard versus innovative robotic balance assessment for people with multiple sclerosis: a correlational study

INTRODUCTION

Balance disorders are common in people with Multiple Sclerosis (PwMS) and, together with other impairments and disabilities, often prevent PwMS from performing their daily living activities. Besides clinical scales and performance tests, robotic platforms can provide more sensitive, specific, and objective monitoring. Validated technologies have been adopted as gold standard, but innovative robotic solutions would represent an opportunity to detect balance impairment in PwMS.

AIM

Study's aim was to compare postural assessment of 46 PwMS with a relapsing-remitting form during static tasks performed with the novel robotic platform hunova® and the gold standard EquiTest®, METHODS: Pearson's r was run on Center of Pressure (COP)-related parameters and global static balance measures computed from hunova® and EquiTest® in eyes-open (EO) and eyes-closed (EC) conditions. In addition, agreeableness level toward the use of both devices was tested through numeric rating scale.

RESULTS

Considering COP-related parameters, correlations were significant for all measures (p < .001). Interestingly, in EO, a strong correlation was shown for sway area (r = .770), while Medio-Lateral (ML) and Anterior-Posterior (AP) oscillation range, path length, ML and AP speed, ML and AP root mean square distance had a relatively strong association (.454 ≤ r ≤ .576). In EC, except for ML oscillation range showing a relatively strong correlation (r = .532), other parameters were strongly associated (.603 ≤ r ≤ .782). Correlations between global balance indexes of hunova® and EquiTest® revealed a relatively strong association between the Somatosensory Score in EquiTest® and the Somatosensory Index in hunova® (r = - .488). While in EO Static Balance Index from hunova® was highly correlated with Equilibrium score of EquiTest® (r = .416), Static Balance Index had a relatively strong association with both the Equilibrium (r = .482) and Strategy Score (r = .583) of EquiTest® in EC. Results from agreeableness rating scale revealed that hunova® was highly appreciated compared to EquiTest® (p = .044).

CONCLUSIONS

hunova® represents an innovative adjunct to standard robotic balance evaluation for PwMS. This confirms that combining traditional and robotic assessments can more accurately detect balance impairments in MS.

Correlation and differences of patient-reported outcomes vs. Likert-Rating of MS symptoms in a real-world cohort using a digital patient app

Background

Multiple Sclerosis (MS) is a chronic and progressive neurological autoimmune disease currently affecting 250,000 individuals in Germany. Patients suffering from the disease can be severely impaired in their day-to-day activities. BRISA is a digital app specifically designed to help MS patients monitor their disease by regularly tracking symptoms. Lengthy and time-consuming questionnaires for patient-reported outcomes (PRO) are the standard method to assess the patients' current condition. Here, we examine whether simplified versions of these questionnaires can provide comparable information regarding individual symptom presentations in BRISA users.

Methods

828 users were included in the analysis. Patients who provided onboarding information and answered at least one questionnaire and the corresponding simplified smiley symptoms assessment were included. Correlation of questionnaire and symptom scores was calculated using Pearson's correlation.

Results

Our analysis cohort predominantly consisted of female, 26-55-year-olds. Relapsing-remitting MS (RRMS) was the most common MS type recorded. Most patients were diagnosed 2-5 years ago. Questionnaires regarding fatigue and vision impairment were among the most answered, those regarding bowel movement and sexual satisfaction received fewest responses. Overall, the scores from questionnaires and symptoms correlated positively. Scoring correlation could also be shown across the subgroups divided by gender, age groups, type of MS, and time since diagnosis of the disease.

Conclusion

Scores recorded from traditional PRO questionnaires can be reflected more easily as a trend in a simplified scale using smileys. Nevertheless, traditional questionnaires are needed to also maintain a more objective assessment. In conclusion, the patient will benefit most from an adaptive combination of regular traditional PRO questionnaire assessments and simplified symptom recording.

Development and usability testing of your MS questionnaire: A patient-based digital tool to monitor symptoms of multiple sclerosis

Objectives

We describe the development of Your Multiple Sclerosis Questionnaire and present the real-world usability testing results of Your Multiple Sclerosis Questionnaire.

Methods

The Your Multiple Sclerosis Questionnaire tool was developed in four stages to collect feedback from people living with MS (plwMS), patient organizations, and clinicians on content, format, and applicability. To assess its usability, 13 clinicians across 7 countries completed an online survey after using the tool with plwMS in a total of 261 consultations from September, 2020 to July, 2021.

Results

The initial Your Multiple Sclerosis Questionnaire version was based on findings from previous research developing MSProDiscuss™, a clinician-completed tool. Subsequently, insights from plwMS obtained during cognitive debriefing, patient councils and advisory boards led to changes including the addition of mood and sexual problems and the definition of relapse. All 13 clinicians completed the individual survey, whereas 10 clinicians completed the final survey. Clinicians "strongly agreed" or "agreed" that Your Multiple Sclerosis Questionnaire was easy to use and understand (98.5%; 257/261 patient consultations). The clinicians were willing to use the tool again with the same patient (98.1%; 256/261 patient consultations). All clinicians who completed the final survey (100%; 10/10) reported the tool to have a positive influence on their clinical practice, helped patients engage with their MS, facilitated discussion with patients, and complemented neurological assessment.

Conclusion

Your Multiple Sclerosis Questionnaire benefits both plwMS and clinicians by facilitating a structured discussion and engaging the plwMS to self-monitor and self-manage. Your Multiple Sclerosis Questionnaire is compatible with telemedicine practice and integration of the tool into electronic health records would enable tracking of the disease evolution and individual monitoring of MS symptoms over time.

MSProDiscuss™ Clinical Decision Support Tool for Identifying Multiple Sclerosis Progression

This article describes the rationale for the development of the MSProDiscuss™ clinical decision support (CDS) tool, its development, and insights into how it can help neurologists improve care for patients with multiple sclerosis (MS). MS is a progressive disease characterized by heterogeneous symptoms and variable disease course. There is growing consensus that MS exists on a continuum, with overlap between relapsing–remitting and secondary progressive phenotypes. Evidence demonstrates that neuroaxonal loss occurs from the outset, that progression can occur independent of relapse activity, and that continuous underlying pathological processes may not be reflected by inflammatory activity indicative of the patient’s immune response. Early intervention can benefit patients, and there is a need for a tool that assists physicians in rapidly identifying subtle signs of MS progression. MSProDiscuss, developed with physicians and patients, facilitates a structured approach to patient consultations. It analyzes multidimensional data via an algorithm to estimate the likelihood of progression (the MSProDiscuss score), the contribution of various symptoms, and the impact of symptoms on daily living, enabling a more personalized approach to treatment and disease management. Data from CDS tools such as MSProDiscuss offer new insights into disease course and facilitate informed decision-making and a holistic approach to MS patient care.

Difficulty in identification of patients with active secondary progressive multiple sclerosis by clinical classification tools

BACKGROUND

The transition from relapsing-remitting (RRMS) to secondary progressive (SPMS) multiple sclerosis is not well defined. Different definitions and tools to identify SPMS have been proposed. Meanwhile, early diagnosis of "active" SPMS is getting progressively more important as pharmaceutical treatment options are being developed. In this study, we compared different classification methods regarding their accuracy to reliably identify "active SPMS".

METHODS

Independent from previous diagnostic classification, we descriptively analyzed the disease course (regarding relapses, progression and MR-activity) in 208 consecutive multiple sclerosis patients treated in our MS outpatient clinic in 2018. Patients were reclassified according to different SPMS criteria and tools. Diagnostic accuracy to identify patients with "active SPMS" was determined.

RESULTS

Comparing the tools to each other, significant variability in the number of patients identified as having SPMS as well as in the proportion of these patients having "active SPMS" was noted. Applying both diagnostic criteria "SPMS" and "active disease" reduced the sensitivity to identify patients with active progressive disease in all approaches.

CONCLUSION

We propose to lessen the emphasis on the label "SPMS" in favor of the more open term "active progressive disease" to simplify the process of identification of patients who may benefit from immune therapy.

Multiple Sclerosis Progression Discussion Tool Usability and Usefulness in Clinical Practice: Cross-sectional, Web-Based Survey

Background

A digital tool, Multiple Sclerosis Progression Discussion Tool (MSProDiscuss), was developed to facilitate discussions between health care professionals (HCPs) and patients in evaluating early, subtle signs of multiple sclerosis (MS) disease progression.

Objective

The aim of this study is to report the findings on the usability and usefulness of MSProDiscuss in a real-world clinical setting.

Methods

In this cross-sectional, web-based survey, HCPs across 34 countries completed an initial individual questionnaire (comprising 7 questions on comprehensibility, usability, and usefulness after using MSProDiscuss during each patient consultation) and a final questionnaire (comprising 13 questions on comprehensibility, usability, usefulness, and integration and adoption into clinical practice to capture the HCPs’ overall experience of using the tool). The responses were provided on a 5-point Likert scale. All analyses were descriptive, and no statistical comparisons were made.

Results

In total, 301 HCPs tested the tool in 6974 people with MS, of whom 77% (5370/6974) had relapsing-remitting MS, including those suspected to be transitioning to secondary progressive MS. The time taken to complete MSProDiscuss was reported to be in the range of 1 to 4 minutes in 97.3% (6786/6974; initial) to 98.2% (269/274; final) of the cases. In 93.54% (6524/6974; initial) to 97.1% (266/274; final) of the cases, the HCPs agreed (4 or 5 on the Likert scale) that patients were able to comprehend the questions in the tool. The HCPs were willing to use the tool again in the same patient, 90.47% (6310/6974; initial) of the cases. The HCPs reported MSProDiscuss to be useful in discussing MS symptoms and their impact on daily activities (6121/6974, 87.76% initial and 252/274, 92% final) and cognitive function (5482/6974, 78.61% initial and 271/274, 79.2% final), as well as in discussing progression in general (6102/6974, 87.49% initial and 246/274, 89.8% final). While completing the final questionnaire, 94.9% (260/274) of the HCPs agreed that the questions were similar to those asked in regular consultation, and the tool helped to better understand the impact of MS symptoms on daily activities (249/274, 90.9%) and cognitive function (220/274, 80.3%). Overall, 92% (252/274) of the HCPs reported that they would recommend MSProDiscuss to a colleague, and 85.8% (235/274) were willing to integrate it into their clinical practice.

Conclusions

MSProDiscuss is a usable and useful tool to facilitate a physician-patient discussion on MS disease progression in daily clinical practice. Most of the HCPs agreed that the tool is easy to use and were willing to integrate MSProDiscuss into their daily clinical practice.

Developing a Digital Solution for Remote Assessment in Multiple Sclerosis: From Concept to Software as a Medical Device

There is increasing interest in the development and deployment of digital solutions to improve patient care and facilitate monitoring in medical practice, e.g., by remote observation of disease symptoms in the patients’ home environment. Digital health solutions today range from non-regulated wellness applications and research-grade exploratory instruments to regulated software as a medical device (SaMD). This paper discusses the considerations and complexities in developing innovative, effective, and validated SaMD for multiple sclerosis (MS). The development of SaMD requires a formalised approach (design control), inclusive of technical verification and analytical validation to ensure reliability. SaMD must be clinically evaluated, characterised for benefit and risk, and must conform to regulatory requirements associated with device classification. Cybersecurity and data privacy are also critical. Careful consideration of patient and provider needs throughout the design and testing process help developers overcome challenges of adoption in medical practice. Here, we explore the development pathway for SaMD in MS, leveraging experiences from the development of Floodlight™ MS, a continually evolving bundled solution of SaMD for remote functional assessment of MS. The development process will be charted while reflecting on common challenges in the digital space, with a view to providing insights for future developers.

Drug and Neurofilament Levels in Serum and Breastmilk of Women With Multiple Sclerosis Exposed to Natalizumab During Pregnancy and Lactation

Objective

To determine the transfer of the monoclonal antibody natalizumab into breastmilk and to evaluate drug and serum neurofilament light chain ((s)NfL) levels in natalizumab exposed pregnancies and lactation periods.

Methods

Eleven women with relapsing remitting multiple sclerosis treated with natalizumab during pregnancy and lactation were included in this study. Breastmilk samples were collected up to 302 days after delivery and analyzed for natalizumab concentration and NfL. Additionally, maternal drug levels and sNfL were determined preconceptually, in each trimester, at delivery and postpartum. Clinical and radiological disease activity was systemically assessed across pregnancy and postpartum period.

Results

The mean average natalizumab concentration in breast milk was low at 0.06 µg/ml [standard deviation (SD) 0.05] in the eight patients who provided serial breastmilk samples with an estimated mean absolute infant dose of 0.007 mg/kg/d (SD 0.005). The relative infant dose (RID), a metric comparing the infant with maternal drug exposure was low as well with a mean of 0.04% (SD=0.03). Most patients had a maximum concentration in breast milk at one to eight days after infusion. Pregnancy was associated with a non-significant decline of the median natalizumab serum concentration. All patients exposed to natalizumab prior (n=10) and during pregnancy (n=11) kept free of disease activity during gestation. While pregnancy was associated with low sNfL levels in patients treated with natalizumab prior and during pregnancy, the postpartum period was linked to a transient sNfL increase in some patients without any evidence of clinical or radiological disease activity. NfL was detectable in the majority of breastmilk samples with a median concentration of 1.7 pg/ml (range 0.004-18.1).

Conclusion

We determined transfer of natalizumab into breastmilk with an RID far below the threshold of concern of 10%. Studies including childhood development assessment are needed in order to gain safety data about natalizumab-exposed breastfeeding. SNfL assessment might be a useful adjunct to monitor silent disease activity and therapeutic response during pregnancy and postpartum period. However, further investigations regarding transient postpartum sNfL increases are required to determine its association to parturition per se or to a silent disease activity in people with multiple sclerosis.

Using Machine Learning Algorithms for Identifying Gait Parameters Suitable to Evaluate Subtle Changes in Gait in People with Multiple Sclerosis

In multiple sclerosis (MS), gait impairment is one of the most prominent symptoms. For a sensitive assessment of pathological gait patterns, a comprehensive analysis and processing of several gait analysis systems is necessary. The objective of this work was to determine the best diagnostic gait system (DIERS pedogait, GAITRite system, and Mobility Lab) using six machine learning algorithms for the differentiation between people with multiple sclerosis (pwMS) and healthy controls, between pwMS with and without fatigue and between pwMS with mild and moderate impairment. The data of the three gait systems were assessed on 54 pwMS and 38 healthy controls. Gaussian Naive Bayes, Decision Tree, k-Nearest Neighbor, and Support Vector Machines (SVM) with linear, radial basis function (rbf) and polynomial kernel were applied for the detection of subtle walking changes. The best performance for a healthy-sick classification was achieved on the DIERS data with a SVM rbf kernel (κ = 0.49 ± 0.11). For differentiating between pwMS with mild and moderate disability, the GAITRite data with the SVM linear kernel (κ = 0.61 ± 0.06) showed the best performance. This study demonstrates that machine learning methods are suitable for identifying pathologic gait patterns in early MS.

Digital Twins for Multiple Sclerosis

An individualized innovative disease management is of great importance for people with multiple sclerosis (pwMS) to cope with the complexity of this chronic, multidimensional disease. However, an individual state of the art strategy, with precise adjustment to the patient's characteristics, is still far from being part of the everyday care of pwMS. The development of digital twins could decisively advance the necessary implementation of an individualized innovative management of MS. Through artificial intelligence-based analysis of several disease parameters - including clinical and para-clinical outcomes, multi-omics, biomarkers, patient-related data, information about the patient's life circumstances and plans, and medical procedures - a digital twin paired to the patient's characteristic can be created, enabling healthcare professionals to handle large amounts of patient data. This can contribute to a more personalized and effective care by integrating data from multiple sources in a standardized manner, implementing individualized clinical pathways, supporting physician-patient communication and facilitating a shared decision-making. With a clear display of pre-analyzed patient data on a dashboard, patient participation and individualized clinical decisions as well as the prediction of disease progression and treatment simulation could become possible. In this review, we focus on the advantages, challenges and practical aspects of digital twins in the management of MS. We discuss the use of digital twins for MS as a revolutionary tool to improve diagnosis, monitoring and therapy refining patients' well-being, saving economic costs, and enabling prevention of disease progression. Digital twins will help make precision medicine and patient-centered care a reality in everyday life.