Precision medicine in chronic disease management: The multiple sclerosis BioScreen

Scoping review of clinical decision support systems for multiple sclerosis management: Leveraging information technology and massive health data

BACKGROUND AND PURPOSE

Multiple sclerosis (MS) is a complex autoimmune disease of the central nervous system, with numerous therapeutic options, but a lack of biomarkers to support a mechanistic approach to precision medicine. A computational approach to precision medicine could proceed from clinical decision support systems (CDSSs). They are digital tools aiming to empower physicians through the clinical applications of information technology and massive data. However, the process of their clinical development is still maturing; we aimed to review it in the field of MS.

METHODS

For this scoping review, we screened systematically the PubMed database. We identified 24 articles reporting 14 CDSS projects and compared their technical and software development aspects.

RESULTS

The projects position themselves in various contexts of usage with various algorithmic approaches: expert systems, CDSSs based on similar patients' data visualization, and model-based CDSSs implementing mathematical predictive models. So far, no project has completed its clinical development up to certification for clinical use with global release. Some CDSSs have been replaced at subsequent project iterations. The most advanced projects did not necessarily report every step of clinical development in a dedicated article (proof of concept, offline validation, refined prototype, live clinical evaluation, comparative prospective evaluation). They seek different software distribution options to integrate into health care: internal usage, "peer-to-peer," and marketing distribution.

CONCLUSIONS

This review illustrates the potential of clinical applications of information technology and massive data to support MS management and helps clarify the roadmap for future projects as a multidisciplinary and multistep process.

Consensus quality indicators for monitoring multiple sclerosis

Multiple sclerosis (MS) as a chronic, degenerative autoimmune disease of the central nervous system has a longitudinal and heterogeneous course with increasing treatment options and risk profiles requiring constant monitoring of a growing number of parameters. Despite treatment guidelines, there is a lack of strategic and individualised monitoring pathways, including respective quality indicators (QIs). To address this, we systematically developed transparent, traceable, and measurable QIs for MS monitoring. Through literature review, expert discussions, and consensus-building, existing QIs were identified and refined. In a two-stage online Delphi process involving MS specialists (on average 53 years old and with 25 years of professional experience), the QIs were evaluated for content, clarity, and intelligibility, resulting in a set of 24 QIs and checklists to assess the quality of care. The final QIs provide a structured approach to document, monitor, and enhance the quality of care for people with MS across their treatment journey.

An Exploratory Study on the Utility of Patient-Generated Health Data as a Tool for Health Care Professionals in Multiple Sclerosis Care

BACKGROUND

Patient-generated health data (PGHD) are data collected through technologies such as mobile devices and health apps. The integration of PGHD into health care workflows can support the care of chronic conditions such as multiple sclerosis (MS). Patients are often willing to share data with health care professionals (HCPs) in their care team; however, the benefits of PGHD can be limited if HCPs do not find it useful, leading patients to discontinue data tracking and sharing eventually. Therefore, understanding the usefulness of mobile health (mHealth) solutions, which provide PGHD and serve as enablers of the HCPs' involvement in participatory care, could motivate them to continue using these technologies.

OBJECTIVE

The objective of this study is to explore the perceived utility of different types of PGHD from mHealth solutions which could serve as tools for HCPs to support participatory care in MS.

METHOD

A mixed-methods approach was used, combining qualitative research and participatory design. This study includes three sequential phases: data collection, assessment of PGHD utility, and design of data visualizations. In the first phase, 16 HCPs were interviewed. The second and third phases were carried out through participatory workshops, where PGHD types were conceptualized in terms of utility.

RESULTS

The study found that HCPs are optimistic about PGHD in MS care. The most useful types of PGHD for HCPs in MS care are patients' habits, lifestyles, and fatigue-inducing activities. Although these subjective data seem more useful for HCPs, it is more challenging to visualize them in a useful and actionable way.

CONCLUSION

HCPs are optimistic about mHealth and PGHD as tools to further understand their patients' needs and support care in MS. HCPs from different disciplines have different perceptions of what types of PGHD are useful; however, subjective types of PGHD seem potentially more useful for MS care.

Strokecopilot: a literature-based clinical decision support system for acute ischemic stroke treatment

BACKGROUND

Acute ischemic stroke (AIS) is an immediate emergency whose management is becoming more and more personalized while facing a limited number of neurologists with high expertise. Clinical decision support systems (CDSS) are digital tools leveraging information and artificial intelligence technologies. Here, we present the Strokecopilot project, a CDSS for the management of the acute phase of AIS. It has been designed to support the evidence-based medicine reasoning of neurologists regarding the indications of intravenous thrombolysis (IVT) and endovascular treatments (ET).

METHODS

Reference populations were manually extracted from the field's main guidelines and randomized clinical trials (RCT). Their characteristics were harmonized in a computerized reference database. We developed a web application whose algorithm identifies the reference populations matching the patient's characteristics. It returns the latter's outcomes in a graphical user interface (GUI), whose design has been driven by real-world practices.

RESULTS

Strokecopilot has been released at www.digitalneurology.net . The reference database includes 25 reference populations from 2 guidelines and 15 RCTs. After a request, the reference populations matching the patient characteristics are displayed with a summary and a meta-analysis of their results. The status regarding IVT and ET indications are presented as "in guidelines", "in literature", or "outside literature references". The GUI is updated to provide several levels of explanation. Strokecopilot may be updated as the literature evolves by loading a new version of the reference populations' database.

CONCLUSION

Strokecopilot is a literature-based CDSS, developed to support neurologists in the management of the acute phase of AIS.

Improving the Automatic Classification of Brain MRI Acquisition Contrast with Machine Learning

Automated quantification of data acquired as part of an MRI exam requires identification of the specific acquisition of relevance to a particular analysis. This motivates the development of methods capable of reliably classifying MRI acquisitions according to their nominal contrast type, e.g., T1 weighted, T1 post-contrast, T2 weighted, T2-weighted FLAIR, proton-density weighted. Prior studies have investigated using imaging-based methods and DICOM metadata-based methods with success on cohorts of patients acquired as part of a clinical trial. This study compares the performance of these methods on heterogeneous clinical datasets acquired with many different scanners from many institutions. RF and CNN models were trained on metadata and pixel data, respectively. A combined RF model incorporated CNN logits from the pixel-based model together with metadata. Four cohorts were used for model development and evaluation: MS research (n = 11,106 series), MS clinical (n = 3244 series), glioma research (n = 612 series, test/validation only), and ADNI PTSD (n = 477 series, training only). Together, these cohorts represent a broad range of acquisition contexts (scanners, sequences, institutions) and subject pathologies. Pixel-based CNN and combined models achieved accuracies between 97 and 98% on the clinical MS cohort. Validation/test accuracies with the glioma cohort were 99.7% (metadata only) and 98.4 (CNN). Accurate and generalizable classification of MRI acquisition contrast types was demonstrated. Such methods are important for enabling automated data selection in high-throughput and big-data image analysis applications.

Artificial Intelligence and Precision Medicine: A New Frontier for the Treatment of Brain Tumors

Brain tumors are a widespread and serious neurological phenomenon that can be life- threatening. The computing field has allowed for the development of artificial intelligence (AI), which can mimic the neural network of the human brain. One use of this technology has been to help researchers capture hidden, high-dimensional images of brain tumors. These images can provide new insights into the nature of brain tumors and help to improve treatment options. AI and precision medicine (PM) are converging to revolutionize healthcare. AI has the potential to improve cancer imaging interpretation in several ways, including more accurate tumor genotyping, more precise delineation of tumor volume, and better prediction of clinical outcomes. AI-assisted brain surgery can be an effective and safe option for treating brain tumors. This review discusses various AI and PM techniques that can be used in brain tumor treatment. These new techniques for the treatment of brain tumors, i.e., genomic profiling, microRNA panels, quantitative imaging, and radiomics, hold great promise for the future. However, there are challenges that must be overcome for these technologies to reach their full potential and improve healthcare.

Multiple sclerosis clinical decision support system based on projection to reference datasets

OBJECTIVE

Multiple sclerosis (MS) is a multifactorial disease with increasingly complicated management. Our objective is to use on-demand computational power to address the challenges of dynamically managing MS.

METHODS

A phase 3 clinical trial data (NCT00906399) were used to contextualize the medication efficacy of peg-interferon beta-1a vs placebo on patients with relapsing-remitting MS (RRMS). Using a set of reference patients (PORs), selected based on adequate features similar to those of an individual patient, we visualize disease activity by measuring the percentage of relapses, accumulation of new T2 lesions on MRI, and worsening EDSS during the clinical trial.

RESULTS

We developed MS Vista, a functional prototype of clinical decision support system (CDSS), with a user-centered design and distributed infrastructure. MS Vista shows the medication efficacy of peginterferon beta-1a versus placebo for each individual patient with RRMS. In addition, MS Vista initiated the integration of a longitudinal magnetic resonance imaging (MRI) viewer and interactive dual physician-patient data display to facilitate communication.

INTERPRETATION

The pioneer use of PORs for each individual patient enables personalized analytics sustaining the dialog between neurologists, patients and caregivers with quantified evidence.

Addressing the Challenges and Barriers to the Integration of Machine Learning into Clinical Practice: An Innovative Method to Hybrid Human-Machine Intelligence

Machine learning (ML) models have proven their potential in acquiring and analyzing large amounts of data to help solve real-world, complex problems. Their use in healthcare is expected to help physicians make diagnoses, prognoses, treatment decisions, and disease outcome predictions. However, ML solutions are not currently deployed in most healthcare systems. One of the main reasons for this is the provenance, transparency, and clinical utility of the training data. Physicians reject ML solutions if they are not at least based on accurate data and do not clearly include the decision-making process used in clinical practice. In this paper, we present a hybrid human-machine intelligence method to create predictive models driven by clinical practice. We promote the use of quality-approved data and the inclusion of physician reasoning in the ML process. Instead of training the ML algorithms on the given data to create predictive models (conventional method), we propose to pre-categorize the data according to the expert physicians' knowledge and experience. Comparing the results of the conventional method of ML learning versus the hybrid physician-algorithm method showed that the models based on the latter can perform better. Physicians' engagement is the most promising condition for the safe and innovative use of ML in healthcare.

Building a Precision Medicine Delivery Platform for Clinics: The University of California, San Francisco, BRIDGE Experience

Despite an ever-expanding number of analytics with the potential to impact clinical care, the field currently lacks point-of-care technological tools that allow clinicians to efficiently select disease-relevant data about their patients, algorithmically derive clinical indices (eg, risk scores), and view these data in straightforward graphical formats to inform real-time clinical decisions. Thus far, solutions to this problem have relied on either bottom-up approaches that are limited to a single clinic or generic top-down approaches that do not address clinical users’ specific setting-relevant or disease-relevant needs. As a road map for developing similar platforms, we describe our experience with building a custom but institution-wide platform that enables economies of time, cost, and expertise. The BRIDGE platform was designed to be modular and scalable and was customized to data types relevant to given clinical contexts within a major university medical center. The development process occurred by using a series of human-centered design phases with extensive, consistent stakeholder input. This institution-wide approach yielded a unified, carefully regulated, cross-specialty clinical research platform that can be launched during a patient’s electronic health record encounter. The platform pulls clinical data from the electronic health record (Epic; Epic Systems) as well as other clinical and research sources in real time; analyzes the combined data to derive clinical indices; and displays them in simple, clinician-designed visual formats specific to each disorder and clinic. By integrating an application into the clinical workflow and allowing clinicians to access data sources that would otherwise be cumbersome to assemble, view, and manipulate, institution-wide platforms represent an alternative approach to achieving the vision of true personalized medicine.

Enhancing clinical information display to improve patient encounters: human-centered design and evaluation of the Parkinson’s Disease-BRIDGE platform (Preprint)

Background

People with Parkinson disease (PD) have a variety of complex medical problems that require detailed review at each clinical encounter for appropriate management. Care of other complex conditions has benefited from digital health solutions that efficiently integrate disparate clinical information. Although various digital approaches have been developed for research and care in PD, no digital solution to personalize and improve communication in a clinical encounter is readily available.

Objective

We intend to improve the efficacy and efficiency of clinical encounters with people with PD through the development of a platform (PD-BRIDGE) with personalized clinical information from the electronic health record (EHR) and patient-reported outcome (PRO) data.

Methods

Using human-centered design (HCD) processes, we engaged clinician and patient stakeholders in developing PD-BRIDGE through three phases: an inspiration phase involving focus groups and discussions with people having PD, an ideation phase generating preliminary mock-ups for feedback, and an implementation phase testing the platform. To qualitatively evaluate the platform, movement disorders neurologists and people with PD were sent questionnaires asking about the technical validity, usability, and clinical relevance of PD-BRIDGE after their encounter.

Results

The HCD process led to a platform with 4 modules. Among these, 3 modules that pulled data from the EHR include a longitudinal module showing motor ratings over time, a display module showing the most recently collected clinical rating scales, and another display module showing relevant laboratory values and diagnoses; the fourth module displays motor symptom fluctuation based on an at-home diary. In the implementation phase, PD-BRIDGE was used in 17 clinical encounters for patients cared for by 1 of 11 movement disorders neurologists. Most patients felt that PD-BRIDGE facilitated communication with their clinician (n=14, 83%) and helped them understand their disease trajectory (n=11, 65%) and their clinician’s recommendations (n=11, 65%). Neurologists felt that PD-BRIDGE improved their ability to understand the patients’ disease course (n=13, 75% of encounters), supported clinical care recommendations (n=15, 87%), and helped them communicate with their patients (n=14, 81%). In terms of improvements, neurologists noted that data in PD-BRIDGE were not exhaustive in 62% (n=11) of the encounters.

Conclusions

Integrating clinically relevant information from EHR and PRO data into a visually efficient platform (PD-BRIDGE) can facilitate clinical encounters with people with PD. Developing new modules with more disparate information could improve these complex encounters even further.

Electronic Health Record Technology Designed for the Clinical Encounter: MS NeuroShare

Objective

Advances in medical discoveries have bolstered expectations of precise and complete care, but delivering on such a promise for complex, chronic neurologic care delivery requires solving last-mile challenges. We describe the iterative human-centered design and pilot process for multiple sclerosis (MS) NeuroShare, a digital health solution that brings practical information to the point of care so that clinicians and patients with MS can view, discuss, and make informed decisions together.

Methods

We initiated a comprehensive human-centered process to iteratively design, develop, and implement a digital health solution for managing MS in the routine outpatient setting of the nonprofit Sutter Health system in Northern California. The human-centered codesign process included 3 phases: discovery and design, development, and implementation and pilot. Stakeholders included Sutter Health's Research Development and Dissemination team, academic domain experts, neurologists, patients with MS, and an advisory group.

Results

MS NeuroShare went live in November 2018. It included a patient- and clinician-facing web application that launches from the electronic health record, visually displays a patient's data relevant to MS, and prompts the clinician to comprehensively evaluate and treat the patient. Both patients and clinicians valued the ability to jointly view patient-generated and other data. Preliminary results suggest that MS NeuroShare promotes patient-clinician communication and more active patient participation in decision-making.

Conclusions

Lessons learned in the design and implementation of MS NeuroShare are broadly applicable to the design and implementation of digital tools aiming to improve the experience of delivering and receiving high-quality care for complex, neurologic conditions across large health systems.

Cross-Talk of the CNS With Immune Cells and Functions in Health and Disease

The immune system's role is much more than merely recognizing self vs. non-self and involves maintaining homeostasis and integrity of the organism starting from early development to ensure proper organ function later in life. Unlike other systems, the central nervous system (CNS) is separated from the peripheral immune machinery that, for decades, has been envisioned almost entirely as detrimental to the nervous system. New research changes this view and shows that blood-borne immune cells (both adaptive and innate) can provide homeostatic support to the CNS via neuroimmune communication. Neurodegeneration is mostly viewed through the lens of the resident brain immune populations with little attention to peripheral circulation. For example, cognition declines with impairment of peripheral adaptive immunity but not with the removal of microglia. Therapeutic failures of agents targeting the neuroinflammation framework (inhibiting immune response), especially in neurodegenerative disorders, call for a reconsideration of immune response contributions. It is crucial to understand cross-talk between the CNS and the immune system in health and disease to decipher neurodestructive and neuroprotective immune mechanisms for more efficient therapeutic strategies.

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.

Exploring polypharmacy phenomenon in newly diagnosed relapsing-remitting multiple sclerosis: a cohort ambispective single-centre study

Aims:

We aimed to examine the frequency of polypharmacy in a large cohort of patients at the time of diagnosis of relapsing–remitting multiple sclerosis (RRMS) and to explore its effects on discontinuation of first disease-modifying treatment (DMT) using survival analysis.

Methods:

This was a cohort ambispective single-centre study. We enrolled RRMS patients starting their first DMT between 1st January 2013 and 31st December 2015. According to the number of medicines prescribed (except DMTs), we divided the patients into three groups: no-poly RRMS, minor-poly RRMS (from one to three medications), and major-poly RRMS (more than three medications).

Results:

A total of 392 RRMS patients were enrolled (mean age 41.1). The minor-poly RRMS group included 61 patients (15.6%) and the major-poly RRMS group included 112 (28.6%). Individuals in these groups were older and had higher median body mass index (BMI) than patients in the no-poly RRMS group (p < 0.05). Upon multinomial regression analysis, older age at onset was associated with minor and major polypharmacy (OR 1.050, CI 1.010–1.093, p = 0.015 and OR 1.063, CI 1.026–1.101, p = 0.001, respectively) and higher BMI was associated with major polypharmacy (OR 1.186, CI 1.18–1.29, p = 0.001). The rates of discontinuation of first DMT were similar among the three groups (50.7% for no-Poly RRMS, 50.8% for minor-Poly RRMS, and 53.3% for major-Poly RRMS, p = 0.264). At log-Rank test, there were no differences among the three groups (p = 0.834).

Conclusion:

Polypharmacy was more common in older RRMS patients with high BMI.

Standardization and digitization of clinical data in multiple sclerosis

Standardization is necessary to ensure the reliability of clinical data and to enable longitudinal and cross-sectional comparisons of data obtained in different centres and countries. In patients with multiple sclerosis (MS), standardized clinical data are needed for monitoring of disability and for collecting real-world evidence for use in research. This Perspective describes attempts to improve the standardization and digitization of clinical data in MS, including digital electronic health recording systems and applications that attempt to offer a comprehensive assessment of patients' neurological deficits and their effects on daily life. Despite the challenges raised by regulatory, ethical and data-privacy considerations, the standardization and digitization of clinical data in MS is expected to generate new insights into the pathophysiology of the disease and to contribute to personalized patient care.

Exploring the Interdisciplinary Nature of Precision Medicine：Network Analysis and Visualization

Background

Interdisciplinary research is an important feature of precision medicine. However, the accurate cross-disciplinary status of precision medicine is still unclear.

Objective

The aim of this study is to present the nature of interdisciplinary collaboration in precision medicine based on co-occurrences and social network analysis.

Methods

A total of 7544 studies about precision medicine, published between 2010 and 2019, were collected from the Web of Science database. We analyzed interdisciplinarity with descriptive statistics, co-occurrence analysis, and social network analysis. An evolutionary graph and strategic diagram were created to clarify the development of streams and trends in disciplinary communities.

Results

The results indicate that 105 disciplines are involved in precision medicine research and cover a wide range. However, the disciplinary distribution is unbalanced. Current cross-disciplinary collaboration in precision medicine mainly focuses on clinical application and technology-associated disciplines. The characteristics of the disciplinary collaboration network are as follows: (1) disciplinary cooperation in precision medicine is not mature or centralized; (2) the leading disciplines are absent; (3) the pattern of disciplinary cooperation is mostly indirect rather than direct. There are 7 interdisciplinary communities in the precision medicine collaboration network; however, their positions in the network differ. Community 4, with disciplines such as genetics and heredity in the core position, is the most central and cooperative discipline in the interdisciplinary network. This indicates that Community 4 represents a relatively mature direction in interdisciplinary cooperation in precision medicine. Finally, according to the evolution graph, we clearly present the development streams of disciplinary collaborations in precision medicine. We describe the scale and the time frame for development trends and distributions in detail. Importantly, we use evolution graphs to accurately estimate the developmental trend of precision medicine, such as biological big data processing, molecular imaging, and widespread clinical applications.

Conclusions

This study can help researchers, clinicians, and policymakers comprehensively understand the overall network of interdisciplinary cooperation in precision medicine. More importantly, we quantitatively and precisely present the history of interdisciplinary cooperation and accurately predict the developing trends of interdisciplinary cooperation in precision medicine.

Harnessing Real-World Data to Inform Decision-Making: Multiple Sclerosis Partners Advancing Technology and Health Solutions (MS PATHS)

Background: Multiple Sclerosis Partners Advancing Technology and Health Solutions (MS PATHS) is the first example of a learning health system in multiple sclerosis (MS). This paper describes the initial implementation of MS PATHS and initial patient characteristics.

Methods: MS PATHS is an ongoing initiative conducted in 10 healthcare institutions in three countries, each contributing standardized information acquired during routine care. Institutional participation required the following: active MS patient census of ≥500, at least one Siemens 3T magnetic resonance imaging scanner, and willingness to standardize patient assessments, share standardized data for research, and offer universal enrolment to capture a representative sample. The eligible participants have diagnosis of MS, including clinically isolated syndrome, and consent for sharing pseudonymized data for research. MS PATHS incorporates a self-administered patient assessment tool, the Multiple Sclerosis Performance Test, to collect a structured history, patient-reported outcomes, and quantitative testing of cognition, vision, dexterity, and walking speed. Brain magnetic resonance imaging is acquired using standardized acquisition sequences on Siemens 3T scanners. Quantitative measures of brain volume and lesion load are obtained. Using a separate consent, the patients contribute DNA, RNA, and serum for future research. The clinicians retain complete autonomy in using MS PATHS data in patient care. A shared governance model ensures transparent data and sample access for research.

Results: As of August 5, 2019, MS PATHS enrolment included participants (n = 16,568) with broad ranges of disease subtypes, duration, and severity. Overall, 14,643 (88.4%) participants contributed data at one or more time points. The average patient contributed 15.6 person-months of follow-up (95% CI: 15.5–15.8); overall, 166,158 person-months of follow-up have been accumulated. Those with relapsing–remitting MS demonstrated more demographic heterogeneity than the participants in six randomized phase 3 MS treatment trials. Across sites, a significant variation was observed in the follow-up frequency and the patterns of disease-modifying therapy use.

Conclusions: Through digital health technology, it is feasible to collect standardized, quantitative, and interpretable data from each patient in busy MS practices, facilitating the merger of research and patient care. This approach holds promise for data-driven clinical decisions and accelerated systematic learning.

A Precision Medicine Tool for Patients With Multiple Sclerosis (the Open MS BioScreen): Human-Centered Design and Development

Background

Patients with multiple sclerosis (MS) face several challenges in accessing clinical tools to help them monitor, understand, and make meaningful decisions about their disease course. The University of California San Francisco MS BioScreen is a web-based precision medicine tool initially designed to be clinician facing. We aimed to design a second, openly available tool, Open MS BioScreen, that would be accessible, understandable, and actionable by people with MS.

Objective

This study aimed to describe the human-centered design and development approach (inspiration, ideation, and implementation) for creating the Open MS BioScreen platform.

Methods

We planned an iterative and cyclical development process that included stakeholder engagement and iterative feedback from users. Stakeholders included patients with MS along with their caregivers and family members, MS experts, generalist clinicians, industry representatives, and advocacy experts. Users consisted of anyone who wants to track MS measurements over time and access openly available tools for people with MS. Phase I (inspiration) consisted of empathizing with users and defining the problem. We sought to understand the main challenges faced by patients and clinicians and what they would want to see in a web-based app. In phase II (ideation), our multidisciplinary team discussed approaches to capture, display, and make sense of user data. Then, we prototyped a series of mock-ups to solicit feedback from clinicians and people with MS. In phase III (implementation), we incorporated all concepts to test and iterate a minimally viable product. We then gathered feedback through an agile development process. The design and development were cyclical—many times throughout the process, we went back to the drawing board.

Results

This human-centered approach generated an openly available, web-based app through which patients with MS, their clinicians, and their caregivers can access the site and create an account. Users can enter information about their MS (basic level as well as more advanced concepts), visualize their data longitudinally, access a series of algorithms designed to empower them to make decisions about their treatments, and enter data from wearable devices to encourage realistic goal setting about their ambulatory activity. Agile development will allow us to continue to incorporate precision medicine tools, as these are validated in the clinical research arena.

Conclusions

After engaging intended users into the iterative human-centered design of the Open MS BioScreen, we will now monitor the adaptation and dissemination of the tool as we expand its functionality and reach. The insights generated from this approach can be applied to the development of a number of self-tracking, self-management, and user engagement tools for patients with chronic conditions.

The Multiple Sclerosis Surveillance Registry: A Novel Interactive Database Within the Veterans Health Administration

OBJECTIVE

To demonstrate the infrastructure and utility of an interactive health system database for multiple sclerosis (MS), we present the MS Surveillance Registry (MSSR) within the US Department of Veterans Affairs (VA).

BACKGROUND

Disease specific databases can be helpful in the management of neurologic conditions but few are fully integrated into the electronic health record and linked to health system data. Creating a consistent information technology (IT) architecture and with ongoing support within disease specific registries has been a challenge.

METHODS

Building the MSSR was initiated by an iterative process with an IT team and MS health care providers. A common registry platform shared by other VA disease specific registries (eg, traumatic brain injury and cancer) was used to develop the IT infrastructure. MS cases were entered online into the MS Assessment Tool at selected MS Centers of Excellence (MSCoE) clinics in the US. Other large VA databases linked to MSSR are reviewed. Patient demographic and clinical characteristics were compared and contrasted with the broader VA population and other US registry populations.

RESULTS

We have enrolled 1,743 patients with MS in the MSSR through fiscal year 2019 from selected MS regional programs in the VA MSCoE network. The mean age of patients was 56.0 years, with a 2.7 male:female ratio. Among those with definite MS, the mean European Database for MS Disability Score was 4.7 and 75% had ever used an MS disease modifying therapy. A summary electronic dashboard was developed for health care providers to easily access demographic and clinical data for individuals and groups of patients. Data on comorbid conditions, pharmacy and prosthetics utilization, outpatient clinic visits, and inpatient admission were documented for each patient.

CONCLUSIONS

The MSSR is a unique electronic database that has enhanced clinical management of MS and serves as a national source for clinical outcomes.

Needs for Systems Approaches to Better Treat Individuals With Severe Asthma: Predicting Phenotypes and Responses to Treatments

Asthma is a frequent heterogeneous multifactorial chronic disease whose severe forms remain largely uncontrolled despite the availability of many drugs and educational therapy. Several phenotypes and endotypes of severe asthma have been described over the last two decades. Typical type-2-immunity-driven asthma remains the most frequent phenotype, and several targeted therapies have been developed and are now available. On the contrary, non-type-2 immunity-driven severe asthma is less understood and still requires efficient innovative therapies. A personalized approach would allow improving asthma control with the help of robust biomarkers able to predict phenotypes/endotypes, exacerbations, response to targeted treatments and, in the future, possible curative options. Some data from large multicenter cohorts have emerged in recent years, especially in transcriptomics. These data have to be integrated and reproduced longitudinally to provide a systems approach for asthma care. In this focused review, the needs for such an approach and the available data will be reviewed as well as the next steps for achieving personalized medicine in asthma.

The Dilemma of When to Stop Disease-Modifying Therapy in Multiple Sclerosis: A Narrative Review and Canadian Regional Reimbursement Policies

BACKGROUND

Disease-modifying therapy (DMT) has changed the landscape of multiple sclerosis (MS) care. However, there is lack of consensus on the duration of treatment and the selection of individuals most likely to benefit from continued treatment. Current evidence, practice guidelines, health policy, and ethical considerations presented together may further inform challenging clinical decision making and future directions. The objectives of this study were to conduct a narrative review of original research and practice guideline recommendations on discontinuation of DMTs in MS; to collect information regarding Canadian regional reimbursement policies for DMT coverage in MS; and to present ethical considerations applicable to such decision making.

METHODS

A literature review was conducted of the MEDLINE/PubMed, OneFile (GALE), Scopus (Elsevier), and ProQuest Biological Science Collection databases. Data regarding Canadian regional reimbursement policies for DMT coverage in MS were collected from the ministry/government websites. Ethical considerations were reviewed in the context of the identified evidence, guidelines, and policies.

RESULTS

The literature lacks evidence from prospective randomized controlled trials that directly addresses the issue of discontinuation of DMTs in MS. Current practice guidelines advocate the vital role of patient choice in decision making. There are regional variations in Expanded Disability Status Scale criteria scores for continuing MS DMT coverage among Canadian provinces/territories.

CONCLUSIONS

In the absence of strong evidence on discontinuation of DMTs, shared decision making and consideration of the ethical complexities could help in the decision-making process.

Reaching an evidence-based prognosis for personalized treatment of multiple sclerosis

Personalized treatment is ideal for multiple sclerosis (MS) owing to the heterogeneity of clinical features, but current knowledge gaps, including validation of biomarkers and treatment algorithms, limit practical implementation. The contemporary approach to personalized MS therapy depends on evidence-based prognostication, an initial treatment choice and evaluation of early treatment responses to identify the need to switch therapy. Prognostication is directed by baseline clinical, environmental and demographic factors, MRI measures and biomarkers that correlate with long-term disability measures. The initial treatment choice should be a shared decision between the patient and physician. In addition to prognosis, this choice must account for patient-related factors, including comorbidities, pregnancy planning, preferences of the patients and their comfort with risk, and drug-related factors, including safety, cost and implications for treatment sequencing. Treatment response has traditionally been assessed on the basis of relapse rate, MRI lesions and disability progression. Larger longitudinal data sets have enabled development of composite outcome measures and more stringent standards for disease control. Biomarkers, including neurofilament light chain, have potential as early surrogate markers of prognosis and treatment response but require further validation. Overall, attainment of personalized treatment for MS is complex but will be refined as new data become available.

Phase II Randomized Controlled Trial of Constraint-Induced Movement Therapy in Multiple Sclerosis. Part 1: Effects on Real-World Function

BACKGROUND

Constraint-Induced Movement therapy (CIMT) has controlled evidence of efficacy for improving real-world paretic limb use in non-progressive physically disabling disorders (stroke, cerebral palsy).

OBJECTIVE

This study sought to determine whether this therapy can produce comparable results with a progressive disorder such as multiple sclerosis (MS). We conducted a preliminary phase II randomized controlled trial of CIMT versus a program of complementary and alternative medicine (CAM) treatments for persons with MS, to evaluate their effect on real-world disability.

METHODS

Twenty adults with hemiparetic MS underwent 35 hours of either CIMT or CAM over 10 consecutive weekdays. The primary clinical outcome was change from pretreatment on the Motor Activity Log (MAL).

RESULTS

The CIMT group improved more on the MAL (2.7 points, 95% confidence interval 2.2-3.2) than did the CAM group (0.5 points, 95% confidence interval -0.1 to 1.1; P < .001). These results did not change at 1-year follow-up, indicating long-term retention of functional benefit for CIMT. The treatments were well tolerated and without adverse events.

CONCLUSION

These results suggest that CIMT can increase real-world use of the more-affected arm in patients with MS for at least 1 year.

CLINICAL TRIAL REGISTRATION

ClinicalTrials.gov NCT01081275.

Multidisciplinary data infrastructures in multiple sclerosis: Why they are needed and can be done!

Personalized treatment is highly desirable in multiple sclerosis (MS). We believe that multidisciplinary measurements including clinical, functional and patient-reported outcome measures in combination with extensive patient profiling can enhance personalized treatment and rehabilitation strategies. We elaborate on four reasons behind this statement: (1) MS disease activity and progression are complex and multidimensional concepts in nature and thereby defy a one-size-fits-all description, (2) functioning, progression, treatment, and rehabilitation effects are interdependent and should be investigated together, (3) personalized healthcare is based on the dynamics of system biology and on technology that confirms a patient's fundamental biology and (4) inclusion of patient-reported outcome measures can facilitate patient-relevant healthcare. We discuss currently available multidisciplinary MS data initiatives and introduce joint actions to further increase the overall success. With this topical review, we hope to drive the MS community to invest in expanding towards more multidisciplinary and longitudinal data collection.

SUMMIT (Serially Unified Multicenter Multiple Sclerosis Investigation): creating a repository of deeply phenotyped contemporary multiple sclerosis cohorts

BACKGROUND

There is a pressing need for robust longitudinal cohort studies in the modern treatment era of multiple sclerosis.

OBJECTIVE

Build a multiple sclerosis (MS) cohort repository to capture the variability of disability accumulation, as well as provide the depth of characterization (clinical, radiologic, genetic, biospecimens) required to adequately model and ultimately predict a patient's course.

METHODS

Serially Unified Multicenter Multiple Sclerosis Investigation (SUMMIT) is an international multi-center, prospectively enrolled cohort with over a decade of comprehensive follow-up on more than 1000 patients from two large North American academic MS Centers (Brigham and Women's Hospital (Comprehensive Longitudinal Investigation of Multiple Sclerosis at the Brigham and Women's Hospital (CLIMB; BWH)) and University of California, San Francisco (Expression/genomics, Proteomics, Imaging, and Clinical (EPIC))). It is bringing online more than 2500 patients from additional international MS Centers (Basel (Universitätsspital Basel (UHB)), VU University Medical Center MS Center Amsterdam (MSCA), Multiple Sclerosis Center of Catalonia-Vall d'Hebron Hospital (Barcelona clinically isolated syndrome (CIS) cohort), and American University of Beirut Medical Center (AUBMC-Multiple Sclerosis Interdisciplinary Research (AMIR)).

RESULTS AND CONCLUSION

We provide evidence for harmonization of two of the initial cohorts in terms of the characterization of demographics, disease, and treatment-related variables; demonstrate several proof-of-principle analyses examining genetic and radiologic predictors of disease progression; and discuss the steps involved in expanding SUMMIT into a repository accessible to the broader scientific community.

The Use of Digital and Remote Communication Technologies as a Tool for Multiple Sclerosis Management: Narrative Review

Despite recent advances in multiple sclerosis (MS) care, many patients only infrequently access health care services, or are unable to access them easily, for reasons such as mobility restrictions, travel costs, consultation and treatment time constraints, and a lack of locally available MS expert services. Advances in mobile communications have led to the introduction of electronic health (eHealth) technologies, which are helping to improve both access to and the quality of health care services. As the Internet is now readily accessible through smart mobile devices, most people can take advantage of eHealth apps. The development of digital applications and remote communication technologies for patients with MS has increased rapidly in recent years. These apps are intended to complement traditional in-clinic approaches and can bring significant benefits to both patients with MS and health care providers (HCPs). For patients, such eHealth apps have been shown to improve outcomes and increase access to care, disease information, and support. These apps also help patients to participate actively in self-management, for example, by tracking adherence to treatment, changes in bladder and bowel habits, and activity and mood. For HCPs, MS eHealth solutions can simplify the multidisciplinary approaches needed to tailor MS management strategies to individual patients; facilitate remote monitoring of patient symptoms, adverse events, and outcomes; enable the efficient use of limited resources and clinic time; and potentially allow more timely intervention than is possible with scheduled face-to-face visits. These benefits are important because MS is a long-term, multifaceted chronic condition that requires ongoing monitoring, assessment, and management. We identified in the literature 28 eHealth solutions for patients with MS that fall within the four categories of screening and assessment, disease monitoring and self-management, treatment and rehabilitation, and advice and education. We review each solution, focusing on any clinical evidence supporting their use from prospective trials (including ASSESS MS, Deprexis, MSdialog, and the Multiple Sclerosis Performance Test) and consider the opportunities, barriers to adoption, and potential pitfalls of eHealth technologies in routine health care.

Do people with multiple sclerosis want to know their prognosis? A UK nationwide study

Background

Multiple sclerosis (MS) has a varied and uncertain trajectory. The recent development of analytical processing tools that draw on large longitudinal patient databases facilitates personalised long-term prognosis estimates. This has the potential to improve both shared treatment decision-making and psychological adjustment. However, there is limited research on how people with MS feel about prognosis communication and forecasting. This study investigated the prognosis communication experiences and preferences of people with MS and explored whether clinical, demographic and psychological factors are associated with prognosis information preferences.

Methods

3175 UK MS Register members (59% of those with active accounts) completed an online survey containing 17 questions about prognosis communication experiences, attitudes and preferences. Participants also completed validated questionnaires measuring coping strategies, tendencies to seek out (‘monitor’) or avoid (‘blunt’) information in threatening situations, and MS risk perceptions and reported their clinical and sociodemographic characteristics. Data already held on the MS Register about participants’ quality of life, anxiety and depression symptoms and MS impact were obtained and linked to the survey data.

Results

53.1% of participants had never discussed long-term prognosis with healthcare professionals. 54.2% lacked clarity about their long-term prognosis. 76% had strong preferences for receiving long-term prognosis information. 92.8% were interested in using tools that generate personalised predictions. Most participants considered prognostication useful for decision-making. Participants were more receptive to receiving prognosis information at later time-points, versus at diagnosis. A comprehensive set of sociodemographic, clinical and psychological variables predicted only 7.9% variance in prognosis information preferences.

Conclusions

People with MS have an appetite for individualised long-term prognosis forecasting and their need for information is frequently unmet. Clinical studies deploying and evaluating interventions to support prognostication in MS are now needed. This study indicates suitable contexts and patient preferences for initial trials of long-term prognosis tools in clinical settings.

Harnessing electronic medical records to advance research on multiple sclerosis

Background:

Electronic medical records (EMR) data are increasingly used in research, but no studies have yet evaluated similarity between EMR and research-quality data and between characteristics of an EMR multiple sclerosis (MS) population and known natural MS history.

Objectives:

To (1) identify MS patients in an EMR system and extract clinical data, (2) compare EMR-extracted data with gold-standard research data, and (3) compare EMR MS population characteristics to expected MS natural history.

Methods:

Algorithms were implemented to identify MS patients from the University of California San Francisco EMR, de-identify the data and extract clinical variables. EMR-extracted data were compared to research cohort data in a subset of patients.

Results:

We identified 4142 MS patients via search of the EMR and extracted their clinical data with good accuracy. EMR and research values showed good concordance for Expanded Disability Status Scale (EDSS), timed-25-foot walk, and subtype. We replicated several expected MS epidemiological features from MS natural history including higher EDSS for progressive versus relapsing–remitting patients and for male versus female patients and increased EDSS with age at examination and disease duration.

Conclusion:

Large real-world cohorts algorithmically extracted from the EMR can expand opportunities for MS clinical research.

Fair data for next-generation management of multiple sclerosis

Multiple sclerosis (MS) is a progressive demyelinating and degenerative disease of the central nervous system with symptoms depending on the disease type and the site of lesions and is featured by heterogeneity of clinical expressions and responses to treatment strategies. An individualized clinical follow-up and multidisciplinary treatment is required. Transforming the population-based management of today into an individualized, personalized and precision-level management is a major goal in research. Indeed, a complex and unique interplay between genetic background and environmental exposure in each case likely determines clinical heterogeneity. To reach insights at the individual level, extensive amount of data are required. Many databases have been developed over the last few decades, but access to them is limited, and data are acquired in different ways and differences in definitions and indexing and software platforms preclude direct integration. Most existing (inter)national registers and IT platforms are strictly observational or focus on disease epidemiology or access to new disease modifying drugs. Here, a method to revolutionize management of MS to a personalized, individualized and precision level is outlined. The key to achieve this next level is FAIR data.

Exploration of machine learning techniques in predicting multiple sclerosis disease course

Objective

To explore the value of machine learning methods for predicting multiple sclerosis disease course.

Methods

1693 CLIMB study patients were classified as increased EDSS≥1.5 (worsening) or not (non-worsening) at up to five years after baseline visit. Support vector machines (SVM) were used to build the classifier, and compared to logistic regression (LR) using demographic, clinical and MRI data obtained at years one and two to predict EDSS at five years follow-up.

Results

Baseline data alone provided little predictive value. Clinical observation for one year improved overall SVM sensitivity to 62% and specificity to 65% in predicting worsening cases. The addition of one year MRI data improved sensitivity to 71% and specificity to 68%. Use of non-uniform misclassification costs in the SVM model, weighting towards increased sensitivity, improved predictions (up to 86%). Sensitivity, specificity, and overall accuracy improved minimally with additional follow-up data. Predictions improved within specific groups defined by baseline EDSS. LR performed more poorly than SVM in most cases. Race, family history of MS, and brain parenchymal fraction, ranked highly as predictors of the non-worsening group. Brain T2 lesion volume ranked highly as predictive of the worsening group.

Interpretation

SVM incorporating short-term clinical and brain MRI data, class imbalance corrective measures, and classification costs may be a promising means to predict MS disease course, and for selection of patients suitable for more aggressive treatment regimens.

Remotely engaged: Lessons from remote monitoring in multiple sclerosis

OBJECTIVES

Evaluate web-based patient-reported outcome (wbPRO) collection in MS subjects in terms of feasibility, reliability, adherence, and subject-perceived benefits; and quantify the impact of MS-related symptoms on perceived well-being.

METHODS

Thirty-one subjects with MS completed wbPROs targeting MS-related symptoms over six months using a customized web portal. Demographics and clinical outcomes were collected in person at baseline and six months.

RESULTS

Approximately 87% of subjects completed wbPROs without assistance, and wbPROs strongly correlated with standard PROs (r>0.91). All wbPROs were completed less frequently in the second three months (p<0.05). Frequent wbPRO completion was significantly correlated with higher step on the Expanded Disability Status Scale (EDSS) (p=0.026). Nearly 52% of subjects reported improved understanding of their disease, and approximately 16% wanted individualized wbPRO content. Over half (63.9%) of perceived well-being variance was explained by MS symptoms, notably depression (r_s=-0.459), fatigue (r_s=-0.390), and pain (r_s=-0.389).

CONCLUSIONS

wbPRO collection was feasible and reliable. More disabled subjects had higher completion rates, yet most subjects failed requirements in the second three months. Remote monitoring has potential to improve patient-centered care and communication between patient and provider, but tailored PRO content and other innovations are needed to combat declining adherence.

Multifaceted interactions between adaptive immunity and the central nervous system

Neuroimmunologists seek to understand the interactions between the central nervous system (CNS) and the immune system, both under homeostatic conditions and in diseases. Unanswered questions include those relating to the diversity and specificity of the meningeal T cell repertoire; the routes taken by immune cells that patrol the meninges under healthy conditions and invade the parenchyma during pathology; the opposing effects (beneficial or detrimental) of these cells on CNS function; the role of immune cells after CNS injury; and the evolutionary link between the two systems, resulting in their tight interaction and interdependence. This Review summarizes the current standing of and challenging questions related to interactions between adaptive immunity and the CNS and considers the possible directions in which these aspects of neuroimmunology will be heading over the next decade.

Retinal thickness measured with optical coherence tomography and risk of disability worsening in multiple sclerosis: a cohort study

BACKGROUND

Most patients with multiple sclerosis without previous optic neuritis have thinner retinal layers than healthy controls. We assessed the role of peripapillary retinal nerve fibre layer (pRNFL) thickness and macular volume in eyes with no history of optic neuritis as a biomarker of disability worsening in a cohort of patients with multiple sclerosis who had at least one eye without optic neuritis available.

METHODS

In this multicentre, cohort study, we collected data about patients (age ≥16 years old) with clinically isolated syndrome, relapsing-remitting multiple sclerosis, and progressive multiple sclerosis. Patients were recruited from centres in Spain, Italy, France, Germany, Czech Republic, Netherlands, Canada, and the USA, with the first cohort starting in 2008 and the latest cohort starting in 2013. We assessed disability worsening using the Expanded Disability Status Scale (EDSS). The pRNFL thickness and macular volume were assessed once at study entry (baseline) by optical coherence tomography (OCT) and was calculated as the mean value of both eyes without optic neuritis for patients without a history of optic neuritis or the value of the non-optic neuritis eye for patients with previous unilateral optic neuritis. Researchers who did the OCT at baseline were masked to EDSS results and the researchers assessing disability with EDSS were masked to OCT results. We estimated the association of pRNFL thickness or macular volume at baseline in eyes without optic neuritis with the risk of subsequent disability worsening by use of proportional hazards models that included OCT metrics and age, disease duration, disability, presence of previous unilateral optic neuritis, and use of disease-modifying therapies as covariates.

FINDINGS

879 patients with clinically isolated syndrome (n=74), relapsing-remitting multiple sclerosis (n=664), or progressive multiple sclerosis (n=141) were included in the primary analyses. Disability worsening occurred in 252 (29%) of 879 patients with multiple sclerosis after a median follow-up of 2·0 years (range 0·5-5 years). Patients with a pRNFL of less than or equal to 87 μm or less than or equal to 88 μm (measured with Spectralis or Cirrus OCT devices) had double the risk of disability worsening at any time after the first and up to the third years of follow-up (hazard ratio 2·06, 95% CI 1·36-3·11; p=0·001), and the risk was increased by nearly four times after the third and up to the fifth years of follow-up (3·81, 1·63-8·91; p=0·002). We did not identify meaningful associations for macular volume.

INTERPRETATION

Our results provide evidence of the usefulness of monitoring pRNFL thickness by OCT for prediction of the risk of disability worsening with time in patients with multiple sclerosis.

FUNDING

Instituto de Salud Carlos III.

Evaluating more naturalistic outcome measures: A 1-year smartphone study in multiple sclerosis

OBJECTIVE

In this cohort of individuals with and without multiple sclerosis (MS), we illustrate some of the novel approaches that smartphones provide to monitor patients with chronic neurologic disorders in their natural setting.

METHODS

Thirty-eight participant pairs (MS and cohabitant) aged 18-55 years participated in the study. Each participant received an Android HTC Sensation 4G smartphone containing a custom application suite of 19 tests capturing participant performance and patient-reported outcomes (PROs). Over 1 year, participants were prompted daily to complete one assigned test.

RESULTS

A total of 22 patients with MS and 17 cohabitants completed the entire study. Among patients with MS, low scores on PROs relating to mental and visual function were associated with dropout (p < 0.05). We illustrate several novel features of a smartphone platform. First, fluctuations in MS outcomes (e.g., fatigue) were assessed against an individual's ambient environment by linking responses to meteorological data. Second, both response accuracy and speed for the Ishihara color vision test were captured, highlighting the benefits of both active and passive data collection. Third, a new trait, a person-specific learning curve in neuropsychological testing, was identified using spline analysis. Finally, averaging repeated measures over the study yielded the most robust correlation matrix of the different outcome measures.

CONCLUSIONS

We report the feasibility of, and barriers to, deploying a smartphone platform to gather useful passive and active performance data at high frequency in an unstructured manner in the field. A smartphone platform may therefore enable large-scale naturalistic studies of patients with MS or other neurologic diseases.

The Alzheimer's Prevention Clinic at Weill Cornell Medical College / New York - Presbyterian Hospital: Risk Stratification and Personalized Early Intervention

In July 2013, Weill Cornell Medical College founded the first Alzheimer's Prevention Clinic (APC) in the United States, providing direct clinical care to family members of patients with Alzheimer's disease (AD) as part of the Weill Cornell Memory Disorders Program. At the APC, patients seeking to lower their AD risk undergo a comprehensive assessment, receive a personalized plan based on rapidly evolving scientific evidence, and are followed over time using validated as well as emerging clinical and research technologies. The APC approach applies the principles of pharmacogenomics, nutrigenomics and clinical precision medicine, to tailor individualized therapies for patients. Longitudinal measures currently assessed in the clinic include anthropometrics, cognition, blood biomarkers (i.e., lipid, inflammatory, metabolic, nutritional) and genetics, as well as validated, self-reported measures that enable patients to track several aspects of health-related quality of life. Patients are educated on the fundamental concepts of AD prevention via an interactive online course hosted on Alzheimer's Universe (www.AlzU.org), which also contains several activities including validated computer-based cognitive testing. The primary goal of the APC is to employ preventative measures that lower modifiable AD risk, possibly leading to a delay in onset of future symptoms. Our secondary goal is to establish a cohort of at-risk individuals who will be primed to participate in future AD prevention trials as disease-modifying agents emerge for testing at earlier stages of the AD process. The clinical services are intended to lower concern for future disease by giving patients a greater sense of control over their brain health.