Digital Twins for Multiple Sclerosis

Voigt, Isabel; Inojosa, Hernán; Dillenseger, Anja; Haase, Rocco; Akgün, Katja; Ziemssen, Tjalf

doi:10.3389/fimmu.2021.669811

Cited by 149 publications

(123 citation statements)

References 203 publications

(288 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…With our integrated care portal and the vision of digital twins for MS, patient involvement will be strengthened by a purposeful assistance in organizing and caring [23,31]. In addition, context-sensitive information for patients and their relatives as well as concrete recommendations and options for action based on this information will be provided.…”

Section: Discussionmentioning

confidence: 99%

Profiles of eHealth Adoption in Persons with Multiple Sclerosis and Their Caregivers

et al. 2021

Self Cite

View full text Add to dashboard Cite

(1) Background: Persons with multiple sclerosis (pwMS) are often characterized as ideal adopters of new digital healthcare trends, but it is worth thinking about whether and which pwMS will be targeted and served by a particular eHealth service like a patient portal. With our study, we wanted to explore needs and barriers for subgroups of pwMS and their caregivers when interacting with eHealth services in care and daily living. (2) Methods: This study comprises results from two surveys: one collecting data from pwMS and their relatives (as informal caregivers) and another one providing information on the opinions and attitudes of healthcare professionals (HCPs). Data were analyzed descriptively and via generalized linear models. (3) Results: 185 pwMS, 25 informal caregivers, and 24 HCPs in the field of MS participated. Nine out of ten pwMS used information technology on a daily base. Individual impairments like in vision and cognition resulted in individual needs like the desire to actively monitor their disease course or communicate with their physician in person. HCPs reported that a complete medication overview, additional medication information, overview of future visits and a reminder of medication intake would be very helpful eHealth features for pwMS, while they themselves preferred features organizing and enriching future visits. (4) Conclusions: A closer look at the various profiles of eHealth adoption in pwMS and their caregivers indicated that there is a broad and robust enthusiasm across several subgroups that does not exclude anyone in general, but constitutes specific areas of interest. For pwMS, the focus was on eHealth services that connect previously collected information and make them easily accessible and understandable.

show abstract

Section: Discussionmentioning

confidence: 99%

Profiles of eHealth Adoption in Persons with Multiple Sclerosis and Their Caregivers

et al. 2021

Self Cite

View full text Add to dashboard Cite

show abstract

“…So continuous phenotyping of people with MS (pwMS) is crucial to detect early signs of progression and non-response to treatment [12][13][14]. Our concept of the implementation of an individualized, innovative management of MS is integrated in the development of digital twins [15]. Our vision is to generate and implement digital twins in the management of MS in order to improve diagnosis, treatment and management strategies as well as patient participation and compliance.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

Self Cite

View full text Add to dashboard Cite

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 (k = 0.49 ± 0.11). For differentiating between pwMS with mild and moderate disability, the GAITRite data with the SVM linear kernel (k = 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.

show abstract

“…Recently, a dual-center retrospective study from Germany in 170 patients treated with alemtuzumab (PROGRAM MS ) described the pretreatment (35 none, 52 basic, 50 natalizumab, 33 fingolimod) and found differences in treatment responses based on the previous use of DMT (33). In the future, linking treatment sequences or other baseline characteristics with effectiveness and safety outcomes might be useful to support treatment decisions (34,35).…”

Section: Discussionmentioning

confidence: 99%

Alemtuzumab in a Large Real-Life Cohort: Interim Baseline Data of the TREAT-MS Study

et al. 2021

Self Cite

View full text Add to dashboard Cite

The non-interventional long-Term study foR obsErvAtion of Treatment with alemtuzumab in active relapsing–remitting MS (TREAT-MS) study collects the so far largest real-life cohort regarding utilization, long-term effectiveness, and safety of alemtuzumab, a humanized monoclonal antibody directed against the cell surface glycoprotein CD52, in adult patients with active relapsing–remitting multiple sclerosis (RRMS). An interim analysis of baseline parameters at inclusion of a non-interventional real-world study about alemtuzumab in Germany including previous multiple sclerosis (MS) medication utilization, MS activity, severity, and duration, as well as comorbidities was performed. Of the 883 patients, 71.6% were women. Mean age was 35.7 ± 9.2 years, time since first MS symptoms (=disease duration) is 8.0 ± 6.8 years, and Expanded Disability Status Scale (EDSS) is 2.7 ± 1.8 points (range, 0.0–7.5 points). The number of relapses in the 12 and 24 months prior to inclusion were 1.6 ± 1.2 and 2.2 ± 1.8, respectively. Of the patients, 14.4% were treatment naive, while for the majority, a wide spectrum of MS disease-modifying treatments (DMTs) and treatment sequences were documented. Overall, interferon beta (IFN-beta) was reported most frequently (52.4%), followed by fingolimod (35.2%), natalizumab (34.9%), and glatiramer acetate (28.9%). Patients with longer disease duration and higher EDSS had a higher number of previous DMTs. Compared to the pivotal phase 2/3 studies, RRMS patients starting alemtuzumab treatment had a longer disease duration in real-world conditions. There was variety of different treatment sequences before the final switch to alemtuzumab. In the future, linking these treatment sequences or other baseline characteristics with effectiveness and safety outcomes might be useful to support treatment decisions. Registered at Paul-Ehrlich-Institut under NIS 281.

show abstract

Digital Twins for Multiple Sclerosis

Cited by 149 publications

References 203 publications

Profiles of eHealth Adoption in Persons with Multiple Sclerosis and Their Caregivers

Profiles of eHealth Adoption in Persons with Multiple Sclerosis and Their Caregivers

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

Alemtuzumab in a Large Real-Life Cohort: Interim Baseline Data of the TREAT-MS Study

Contact Info

Product

Resources

About