PUBLICATIONS

Fair data for next-generation management of multiple sclerosis

Peeters LM. Fair data for next-generation management of multiple sclerosis. Mult Scler. 08 2018;24(9):1151-1156. doi:10.1177/1352458517748475

Abstract: 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.

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

Peeters LM, van Munster CE, Van Wijmeersch B, et al. Multidisciplinary data infrastructures in multiple sclerosis: Why they are needed and can be done! Mult Scler. 04 2019;25(4):500-509. doi:10.1177/1352458518807076

Abstract: Personalized treatment is highly desirable in multiple sclerosis (MS). We believe that multidisciplinary measurement 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.

Machine learning analysis of motor evoked potential time series to predict disability progression in multiple sclerosis

Yperman J, Becker T, Valkenborg D, et al. Machine learning analysis of motor evoked potential time series to predict disability progression in multiple sclerosis. BMC Neurol. Mar 2020;20(1):105. doi:10.1186/s12883-020-01672-w

Abstract: Evoked potentials (EPs) are a measure of the conductivity of the central nervous system. They are used to monitor disease progression of multiple sclerosis patients. Previous studies only extracted a few variables from the EPs, which are often further condensed into a single variable: the EP score. We perform a machine learning analysis of motor EP that uses the whole time series, instead of a few variables, to predict disability progression after two years.
Obtaining realistic performance estimates of this task has been difficult because of small data set sizes. We recently extracted a dataset of EPs from the Rehabilitation & MS Center in Overpelt, Belgium. Our data set is large enough to obtain, for the first time, a performance estimate on an independent test set containing different patients.

Deciphering the Morphology of Motor Evoked Potentials

Yperman J, Becker T, Valkenborg D, et al. Deciphering the Morphology of Motor Evoked Potentials. Front Neuroinform. 2020;14:28. doi:10.3389/fninf.2020.00028

Abstract:

Motor Evoked Potentials (MEPs) are used to monitor disability progression in multiple sclerosis (MS). Their morphology plays an important role in this process. Currently, however, there is no clear definition of what constitutes a normal or abnormal morphology.
To address this, five experts independently labeled the morphology (normal or abnormal) of the same set of 1,000 MEPs. The intra- and inter-rater agreement between the experts indicates they agree on the concept of morphology, but differ in their choice of threshold between normal and abnormal morphology. We subsequently performed an automated extraction of 5,943 time series features from the MEPs to identify a valid proxy for morphology, based on the provided labels. To do this, we compared the cross-validation performances of one-dimensional logistic regression models fitted to each of the features individually. We find that the approximate entropy (ApEn) feature can accurately reproduce the majority-vote labels. The performance of this feature is evaluated on an independent test set by comparing to the majority vote of the neurologists, obtaining an AUC score of 0.92. The model slightly outperforms the average neurologist at reproducing the neurologists consensus-vote labels.We can conclude that MEP morphology can be consistently defined by pooling the interpretations from multiple neurologists and that ApEn is a valid continuous score for this. Having an objective and reproducible MEP morphological abnormality score will allow researchers to include this feature in their models, without manual annotation becoming a bottleneck.
This is crucial for large-scale, multi-center datasets. An exploratory analysis on a large single-center dataset shows that ApEn is potentially clinically useful. Introducing an automated, objective, and reproducible definition of morphology could help overcome some of the barriers that are currently obstructing broad adoption of evoked potentials in daily care and patient follow-up, such as standardization of measurements between different centers, and formulating guidelines for clinical use.

COVID-19 in people with multiple sclerosis: A global data sharing initiative

Peeters LM, Parciak T, Walton C, et al. COVID-19 in people with multiple sclerosis: A global data sharing initiative. Mult Scler. 09 2020;26(10):1157-1162. doi:10.1177/1352458520941485

Abstract
Background: We need high-quality data to assess the determinants for COVID-19 severity in people with MS (PwMS). Several studies have recently emerged but there is great benefit in aligning data collection efforts at a global scale.
Objectives: Our mission is to scale-up COVID-19 data collection efforts and provide the MS community with data-driven insights as soon as possible.

Multiple Sclerosis Data Alliance - A global multi-stakeholder collaboration to scale-up real world data research

Peeters LM, Parciak T, Kalra D, et al. Multiple Sclerosis Data Alliance - A global multi-stakeholder collaboration to scale-up real world data research. Mult Scler Relat Disord. Jan 2021;47:102634. doi:10.1016/j.msard.2020.102634

Abstract: The Multiple Sclerosis Data Alliance (MSDA), a global multi-stakeholder collaboration, is working to accelerate research insights for innovative care and treatment for people with multiple sclerosis (MS) through better use of real-world data (RWD). Despite the increasing reliance on RWD, challenges and limitations complicate the generation, collection, and use of these data. MSDA aims to tackle sociological and technical challenges arising with scaling up RWD, specifically focused on MS data. MSDA envisions a patient-centred data ecosystem in which all stakeholders contribute and use big data to co-create the innovations needed to advance timely treatment and care of people with MS.

Associations of DMT therapies with COVID-19 severity in multiple sclerosis.

Simpson-Yap S, De Brouwer E, Kalincik T, et al. Associations of Disease-Modifying Therapies With COVID-19 Severity in Multiple Sclerosis. Neurology. Oct 05 2021;doi:10.1212/WNL.0000000000012753

Abstract:

Longitudinal machine learning modeling of MS patient trajectories improves predictions of disability progression

De Brouwer E, Becker T, Moreau Y, et al. Longitudinal machine learning modeling of MS patient trajectories improves predictions of disability progression. Comput Methods Programs Biomed. May 2021;208:106180. doi:10.1016/j.cmpb.2021.106180

Abstract:

Background and Objectives:Research in Multiple Sclerosis (MS) has recently focused on extracting knowledge from real-world clinical data sources. This type of data is more abundant than data produced during clinical trials and potentially more informative about real-world clinical practice. However, this comes at the cost of less curated and controlled data sets. In this work we aim to predict disability progression by optimally extracting information from longitudinal patient data in the real-world setting, with a special focus on the sporadic sampling problem.

Changes on the Health Care of People with Multiple Sclerosis from Latin America during the COVID-19 Pandemic

Chertcoff A, Bauer J, Silva, BA, et al. Changes on the health care of people with multiple sclerosis from Latin America during the COVID-19 pandemic. Mult Scler Relat Disord. Sept 2021;54. https://doi.org/10.1016/j.msard.2021.103120

Treg-resistant cytotoxic CD4+ T cells dictate T helper cells in their vicinity: TH17 skewing and modulation of proliferation

Hoeks C, Vanheusden M, Peeters LM, et al. Treg-resistant cytotoxic CD4+ T cells dictate T helper cells in their vicinity: TH17 skewing and modulation of proliferation. Int J Mol Sci. May 2021; 22(11):5660.doi: 10.3390/ijms22115660

Motor evoked potentials for multiple sclerosis: A 2 multiyear follow-up dataset.

Yperman, J., Popescu, V., Van Wijmeersch, B. et al. Motor evoked potentials for multiple sclerosis, a multiyear follow-up dataset. Sci Data 9, 207 (2022). https://doi.org/10.1038/s41597-022-01335-0

Abstract:

Multiple sclerosis (MS) is a chronic disease affecting millions of people worldwide. The signal conduction through the central nervous system of MS patients deteriorates. Evoked potential measurements allow clinicians to monitor the degree of deterioration and are used for decision support. We share a dataset that contains motor evoked potential (MEP) measurements, in which the brain is stimulated and the resulting signal is measured by electrodes in the hands and feet. This results in time series of 100 milliseconds long. Typically, both hands and feet are measured in one hospital visit. The dataset consists of 5586 visits of 963 patients, performed in day-to-day clinical care over a period of 6 years. The dataset consists of approximately 100,000 MEP. Clinical metadata such as the expanded disability status scale, sex, and age is also available.

This dataset can be used to explore the role of evoked potentials in MS research and patient care. It may also be used as a real-world benchmark for machine learning techniques for time series analysis and predictive modelling.

The MSDA Catalogue: enabling web-based discovery of metadata from real-world multiple sclerosis data sources

Geys L, Parciak T, Pirmani A, et al. The Multiple Sclerosis Data Alliance Catalogue: Enabling Web-Based Discovery of Metadata from Real-World Multiple Sclerosis Data Sources. Dec 29 2021; doi:10.7224/1537-2073.2021-006

A national representative, cross-sectional study by the Hellenic Academy of Neuroimmunology (HEL.A.NI.) on COVID-19 and Multiple Sclerosis: overall impact and willingness towards vaccination

Boziki M, Styliadis C, Bakirtzis C, Grigoriadou E, Sintila AS, Nikolaidis I, et al. A National Representative, Cross-Sectional Study by the Hellenic Academy of NeuroImmunology (HEL.A.NI.) on COVID-19 and Multiple Sclerosis: Overall Impact and Willingness Toward Vaccination. Front Neurol. 2021;12:757038.

Abstract:

Background: In the context of the coronavirus disease 2019 (COVID-19) pandemic, the constant needs of people with multiple sclerosis (PwMS) and their caregivers were urgently highlighted.

Aim: The present study aims to capture the effects of the COVID-19 pandemic in several aspects of the quality of life of PwMS, in perception and behavior to COVID-19 and multiple sclerosis (MS), as well as concerning healthcare, working conditions, and the willingness toward COVID-19 vaccination.

Methods: This study is an initiative of the Hellenic Academy of Neuroimmunology (HEL.A.NI.) and it has been included in the MS Data Alliance (MSDA) Catalog, which can be accessed after creating an account on https://msda.emif-catalogue.eu/login. Two online questionnaires were administered: (i) impact of the COVID-19 pandemic on the quality of life, behavior, and healthcare of PwMS (Questionnaire A) and (ii) vaccination against COVID-19 (Questionnaire B). People with MS were invited to participate by the Hellenic Federation of Persons with Multiple Sclerosis (HFoPwMS).

Results: Three-hundred-ninety PwMS responded to Questionnaire A, whereas 176 PwMS provided answers for Questionnaire B. Older age, longer disease duration, and higher MS-related disability were associated with the increased perceived sensitivity toward severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, as well as the increased perceived severity of COVID-19 upon potential infection. A significant proportion of PwMS experienced restricted access to MS-related health professionals, disease-modifying therapy (DMT) prescription, and/or to MS-related laboratory examination due to the pandemic. Subgroups of PwMS reported exacerbated symptoms (i.e., chronic MS-related symptoms, fatigue and/or worsening of pre-existing fatigue, and sexual dysfunction and or/worsening of pre-existing sexual dysfunction). Overall, the majority of the participants reported either a strong willingness to get vaccinated against COVID-19 or a likeliness to undergo vaccination. Being aware of the HEL.A.NI. recommendations regarding COVID-19 vaccination for PwMS were reported to increase the willingness of the participants to receive the vaccine.

Conclusions: Our results highlight the necessity of scientific and patient organizations in taking joint action to increase awareness on health-related issues during the pandemic and to provide accurate and up-to-date guidance for PwMS. Online information and communications technology (ICT) tools for polling public belief and behavior may prove valuable as means of retaining active routes of communication between stakeholders.

Severity of COVID19 infection among patients with multiple sclerosis treated with interferon-β

Simpson-Yap S, Pirmani A, De Brouwer E, Peeters LM, Geys L, Parciak T, et al. Severity of COVID19 infection among patients with multiple sclerosis treated with interferon-β. Mult Scler Relat Disord. 2022;66:104072.

Abstract:

Updated results of the COVID-19 in MS Global Data Sharing Initiative: anti-CD20 and other risk factors associated with COVID-19 severity

Simpson-Yap S, Pirmani A, Kalincik T, De Brouwer E, Geys L, Parciak T, et al. Updated Results of the COVID-19 in MS Global Data Sharing Initiative: Anti-CD20 and Other Risk Factors Associated With COVID-19 Severity. Neurol Neuroimmunol Neuroinflamm. 2022;9(6).

Abstract:

Background and Objectives: Certain demographic and clinical characteristics, including the use of some disease-modifying therapies (DMTs), are associated with severe acute respiratory syndrome coronavirus 2 infection severity in people with multiple sclerosis (MS). Comprehensive exploration of these relationships in large international samples is needed.

Safety and effectiveness of cladribine tablets for multiple sclerosis: Results from a single-center real-world cohort

Aerts S, Khan H, Severijns D, Popescu V, Peeters LM, Van Wijmeersch B. Safety and effectiveness of cladribine tablets for multiple sclerosis: Results from a single-center real-world cohort. Mult Scler Relat Disord. 2023;75:104735.

Abstract:

Background:Cladribine tablets are a highly effective immune reconstitution therapy licensed for treating relapsing multiple sclerosis (RMS) in Europe since 2017. Currently, there is a high demand for real-world data from different clinical settings on the effectiveness and safety profile of cladribine in MS.

Methods:Within this report, we retrospectively evaluated the outcomes of RMS patients who received cladribine between August 2018 and November 2021 at our Belgian institute. Patients with data for three effectiveness endpoints, more specifically, relapses, MRI observations, and confirmed disability worsening were incorporated into the analysis of 'no evidence of disease activity' (NEDA-3) re-baselined at 3 months. Safety endpoints included lymphopenia, liver transaminases, and adverse events (AEs) during follow-up. Descriptive statistics and time-to-event analysis were performed, including subgroup analysis by pre-treatment.

Results:Of the 84 RMS patients included in this study (age 42 [33-50], 64.3% female, diagnosis duration 6 [2-11] years, baseline EDSS 2.5 [1.5-3.6]), 14 (16.7%) patients experienced relapses, while disability progression and brain MRI activity occurred in 8.5% (6/71) and 6.3% (5/79). This resulted in 72.6% (n = 69, standard error 6%) retaining NEDA-3 status at the mean follow-up time of 22.6 ± 11.5 months. During the first year after cladribine initiation, disease activity prevailed more in patients with ≥2 prior DMTs and those switching from fingolimod, although both trends were not statistically significant. In terms of safety, 67.9% reported at least one AE during follow-up, the most frequent being fatigue (64.9%) and skin-related problems (38.6%).

Conclusion: Overall, our research results confirm cladribine's safety and effectiveness among RMS patients in real-world conditions. After the re-baseline, we observed high rates of NEDA-3-retention, and no new safety signals were noted.

Patient-level dataset to study the effect of COVID-19 in people with Multiple Sclerosis

Khan, H., Geys, L., baneke, p., Comi, G., & Peeters, L. (2023). Patient-level dataset to study the effect of COVID-19 in people with Multiple Sclerosis (version 1.0.0). PhysioNet. https://doi.org/10.13026/feem-fn23.

Abstract:

Multiple Sclerosis (MS) is an inflammatory autoimmune disease of the central nervous system, causing increased vulnerability to infections and disability among young adults. Ever since the coronavirus disease 2019 (COVID-19) outbreak, caused by severe acute respiratory syndrome coronavirus 2 infections, there have been concerns among people with MS (PwMS) about the potential interactions between various disease-modifying therapies and COVID-19. The COVID-19 in MS Global Data Sharing Initiative (GDSI) was initiated in 2020 to address these concerns. This paper focuses on the anonymisation and open-sourcing of a GDSI sub-dataset, comprising data entered by people with MS and clinicians using a fast data entry tool. The dataset includes demographics, comorbidities, hospital stay, and COVID-19 symptoms of PwMS. The dataset can be used to perform different statistical analyses to improve our understanding of COVID-19 in MS. Furthermore, this dataset can also be used within the context of educational activities to educate different stakeholders on the complex data science topics that were used within the GDSI.

FAIRness through automation: development of an automated medical data integration infrastructure for FAIR health data in a maximum care university hospital

Parciak M, Suhr M, Schmidt C, Bönisch C, Löhnhardt B, Kesztyüs D, et al. FAIRness through automation: development of an automated medical data integration infrastructure for FAIR health data in a maximum care university hospital. BMC Med Inform Decis Mak. 2023;23(1):94.

Abstract:

Background: Secondary use of routine medical data is key to large-scale clinical and health services research. In a maximum care hospital, the volume of data generated exceeds the limits of big data on a daily basis. This so-called “real world data” are essential to complement knowledge and results from clinical trials. Furthermore, big data may help in establishing precision medicine. However, manual data extraction and annotation workflows to transfer routine data into research data would be complex and inefficient. Generally, best practices for managing research data focus on data output rather than the entire data journey from primary sources to analysis. To eventually make routinely collected data usable and available for research, many hurdles have to be overcome. In this work, we present the implementation of an automated framework for timely processing of clinical care data including free texts and genetic data (non-structured data) and centralized storage as Findable, Accessible, Interoperable, Reusable (FAIR) research data in a maximum care university hospital.

Methods: We identify data processing workflows necessary to operate a medical research data service unit in a maximum care hospital. We decompose structurally equal tasks into elementary sub-processes and propose a framework for general data processing. We base our processes on open-source software-components and, where necessary, custom-built generic tools.

Results: We demonstrate the application of our proposed framework in practice by describing its use in our Medical Data Integration Center (MeDIC). Our microservices-based and fully open-source data processing automation framework incorporates a complete recording of data management and manipulation activities. The prototype implementation also includes a metadata schema for data provenance and a process validation concept. All requirements of a MeDIC are orchestrated within the proposed framework: Data input from many heterogeneous sources, pseudonymization and harmonization, integration in a data warehouse and finally possibilities for extraction or aggregation of data for research purposes according to data protection requirements.

Conclusion:Though the framework is not a panacea for bringing routine-based research data into compliance with FAIR principles, it provides a much-needed possibility to process data in a fully automated, traceable, and reproducible manner.

The journey of data within a Global Data Sharing Initiative: A federated three-layer data analysis pipeline to scale up multiple sclerosis research

Pirmani A, De Brouwer E, Geys L, Parciak T, Moreau Y, Peeters LM The Journey of Data Within a Global Data Sharing Initiative: A Federated 3-Layer Data Analysis Pipeline to Scale Up Multiple Sclerosis Research JMIR Med Inform 2023;11:e48030 doi: 10.2196/48030

Abstract:

©Ashkan Pirmani, Edward De Brouwer, Lotte Geys, Tina Parciak, Yves Moreau, Liesbet M Peeters. Originally published in JMIR Medical Informatics (https://medinform.jmir.org), 09.11.2023.

Measuring Approximate Functional Dependencies: a Comparative Study

Parciak, M., Weytjens, S., Hens, N., Neven, F., Peeters, L. M., & Vansummeren, S. (2023). Measuring Approximate Functional Dependencies: a Comparative Study. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2312.06296

Abstract:

Approximate functional dependencies (AFDs) are functional dependencies (FDs) that "almost" hold in a relation. While various measures have been proposed to quantify the level to which an FD holds approximately, they are difficult to compare and it is unclear which measure is preferable when one needs to discover FDs in real-world data, i.e., data that only approximately satisfies the FD. In response, this paper formally and qualitatively compares AFD measures. We obtain a formal comparison through a novel presentation of measures in terms of Shannon and logical entropy. Qualitatively, we perform a sensitivity analysis w.r.t. structural properties of input relations and quantitatively study the effectiveness of AFD measures for ranking AFDs on real world data. Based on this analysis, we give clear recommendations for the AFD measures to use in practice.

Introducing a core dataset for real-world data in multiple sclerosis - recommendations from a global task force

Parciak T, Geys L, Helme A, van der Mei I, Hillert J, Schmidt H, et al. Introducing a core dataset for real-world data in multiple sclerosis registries and cohorts: Recommendations from a global task force. Mult Scler. 2023:13524585231216004.

Abstract:

Strategic Oversight Across Real-World Health Data Initiatives in a Complex Health Data Space: A call for collective responsibility

Geys, L. and Peeters, L. Strategic Oversight Across Real-World Health Data Initiatives in a Complex Health Data Space: A Call for Collective Responsibility. In Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2024) https://doi.org/10.5281/zenodo.10451144

List of initiatives curated by the authors of the paper: Strategic Oversight Across Real-World Health Data Initiatives in a Complex Health Data Space: A call for collective responsibility. In the context of this work, the term 'initiative' refers to plans, projects, or studies focused on unlocking real-world health data, making it accessible for research, innovation, and policy purposes. For each initiative, details about the covered regions, associated countries, website links, and whether the initiative is specific to healthcare or encompasses multiple domains are presented, as available. Important Disclaimer: The authors acknowledge that this list is neither exhaustive nor free from bias, influenced by their geographical location and their research emphasis on chronic disorders. However, this list offers a starting point to address the issues discussed in this position paper, aiming to provide readers with insights gained after extensive online exploration. The list resulting from our work can assist individuals seeking clarity on the evolving landscape of health data initiatives. While a comprehensive overview remains elusive, this list serves as a valuable resource to navigate the intricate ecosystem.

The paper is accepted (on 22 December 2023) as short paper for the the 17th International Joint Conference on Biomedical Engineering Systems and Technologies (HEALTHINF 2024).

Publication:the paper will be included in the Conference Proceedings, which will be published under an ISBN number by SCITEPRESS, on paper and digital support, and made available for online consultation at the SCITEPRESS Digital Library. Online publication is exclusive to papers which have been both published and presented at the event.