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.
For incurable diseases, such as multiple sclerosis (MS), the prevention of progression and the preservation of quality of life play a crucial role over the entire therapy period. In MS, patients tend to become ill at a younger age and are so variable in terms of their disease course that there is no standard therapy. Therefore, it is necessary to enable a therapy that is as personalized as possible and to respond promptly to any changes, whether with noticeable symptoms or symptomless. Here, measurable parameters of biological processes can be used, which provide good information with regard to prognostic and diagnostic aspects, disease activity and response to therapy, so-called biomarkers Increasing digitalization and the availability of easy-to-use devices and technology also enable healthcare professionals to use a new class of digital biomarkers—digital health technologies—to explain, influence and/or predict health-related outcomes. The technology and devices from which these digital biomarkers stem are quite broad, and range from wearables that collect patients’ activity during digitalized functional tests (e.g., the Multiple Sclerosis Performance Test, dual-tasking performance and speech) to digitalized diagnostic procedures (e.g., optical coherence tomography) and software-supported magnetic resonance imaging evaluation. These technologies offer a timesaving way to collect valuable data on a regular basis over a long period of time, not only once or twice a year during patients’ routine visit at the clinic. Therefore, they lead to real-life data acquisition, closer patient monitoring and thus a patient dataset useful for precision medicine. Despite the great benefit of such increasing digitalization, for now, the path to implementing digital biomarkers is widely unknown or inconsistent. Challenges around validation, infrastructure, evidence generation, consistent data collection and analysis still persist. In this narrative review, we explore existing and future opportunities to capture clinical digital biomarkers in the care of people with MS, which may lead to a digital twin of the patient. To do this, we searched published papers for existing opportunities to capture clinical digital biomarkers for different functional systems in the context of MS, and also gathered perspectives on digital biomarkers under development or already existing as a research approach.
For safety evaluation, randomized controlled trials (RCTs) are not fully able to identify rare adverse events. The richest source of safety data lies in the post-marketing phase. Real-world evidence (RWE) and observational studies are becoming increasingly popular because they reflect usefulness of drugs in real life and have the ability to discover uncommon or rare adverse drug reactions. Areas covered: Adding the documentation of psychological symptoms and other medical disciplines, the necessity for a complex documentation becomes apparent. The collection of high-quality data sets in clinical practice requires the use of special documentation software as the quality of data in RWE studies can be an issue in contrast to the data obtained from RCTs. The MSDS3D software combines documentation of patient data with patient management of patients with multiple sclerosis. Following a continuous development over several treatment-specific modules, we improved and expanded the realization of safety management in MSDS3D with regard to the characteristics of different treatments and populations. Expert opinion: eHealth-enhanced post-authorisation safety study may complete the fundamental quest of RWE for individually improved treatment decisions and balanced therapeutic risk assessment. MSDS3D is carefully designed to contribute to every single objective in this process.
(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.
The assessment of neuropsychological functions and especially dual-tasking abilities is considered to be increasingly relevant in the assessment of neurological disease and Multiple Sclerosis (MS) in particular. Yet, the assessment of dual-tasking abilities is hindered by specific software requirements and extensive testing times. We designed a novel e-health (progressive web application-based) device for the assessment of dual-tasking abilities usable in “bedside” and outpatient clinic settings and examined its reliability in a sample of N=184 MS patients in an outpatient setting. Moreover, we examined the relevance of dual-tasking assessment using this device with respect to clinically relevant parameters in MS. We show that a meaningful assessment of dual-tasking is possible within 6 minutes and that reliabilities of the behavioral readouts ranged between .81 to .92 depending on dual-tasking difficulty. We show that dual-tasking readouts were correlated with clinically relevant parameters (e.g. EDSS, disease duration, processing speed) and were not affected by fatigue levels. We consider the tested dual-tasking assessment device suitable for routine clinical neuropsychological assessments of dual-tasking abilities. Future studies may further evaluate this test regarding its suitability in the long-term follow up assessments and to assess dual-tasking abilities in other neurological and psychiatric disorders.
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