Two-Year observational study of autonomic skin function in patients with Parkinson's disease compared to healthy individuals.

BACKGROUND AND PURPOSE: We characterized autonomic pilomotor and sudomotor skin function in early Parkinson's disease (PD) longitudinally. METHODS: We enrolled PD patients (Hoehn and Yahr 1-2) and healthy controls from movement disorder centers in Germany, Hungary, and the United States. We evaluated axon-reflex responses in adrenergic sympathetic pilomotor nerves and in cholinergic sudomotor nerves and assessed sympathetic skin response (SSR), predominantly parasympathetic neurocardiac function via heart rate variability, and disease-related symptoms at baseline, after 2 weeks, and after 1 and 2 years. CLINICALTRIALS: gov: NCT03043768. RESULTS: We included 38 participants: 26 PD (60% females, aged 62.4 ± 7.4 years, mean ± SD) and 12 controls (75% females, aged 59.5 ± 5.8 years). Pilomotor function was reduced in PD compared to controls at baseline when quantified via spatial axon-reflex spread (78 [43-143], median [interquartile range] mm2 vs. 175 [68-200] mm2 , p = 0.01) or erect hair follicle count in the axon-reflex region (8 [6-10] vs. 11 [6-16], p = 0.008) and showed reliability absent any changes from baseline to Week 2 (p = not significant [ns]). Between-group differences increased over the course of 2 years (p < 0.05), although no decline was observed within groups (p = ns). Pilomotor impairment in PD correlated with motor symptoms (rho = -0.59, p = 0.017) and was not lateralized (p = ns). Sudomotor axon-reflex and neurocardiac function did not differ between groups (p = ns), but SSR was reduced in PD (p = 0.0001). CONCLUSIONS: Impairment of adrenergic sympathetic pilomotor function and SSR in evolving PD is not paralleled by changes to cholinergic sudomotor function and parasympathetic neurocardiac function, suggesting a sympathetic pathophysiology. A pilomotor axon-reflex test might be useful to monitor PD-related pathology.

Transparent Quality Optimization for Machine Learning-Based Regression in Neurology.

The clinical monitoring of walking generates enormous amounts of data that contain extremely valuable information. Therefore, machine learning (ML) has rapidly entered the research arena to analyze and make predictions from large heterogeneous datasets. Such data-driven ML-based applications for various domains become increasingly applicable, and thus their software qualities are taken into focus. This work provides a proof of concept for applying state-of-the-art ML technology to predict the distance travelled of the 2-min walk test, an important neurological measurement which is an indicator of walking endurance. A transparent lean approach was emphasized to optimize the results in an explainable way and simultaneously meet the specified software requirements for a generic approach. It is a general-purpose strategy as a fractional−factorial design benchmark combined with standardized quality metrics based on a minimal technology build and a resulting optimized software prototype. Based on 400 training and 100 validation data, the achieved prediction yielded a relative error of 6.1% distributed over multiple experiments with an optimized configuration. The Adadelta algorithm (LR=0.000814, fModelSpread=5, nModelDepth=6, nepoch=1000) performed as the best model, with 90% of the predictions with an absolute error of <15 m. Factors such as gender, age, disease duration, or use of walking aids showed no effect on the relative error. For multiple sclerosis patients with high walking impairment (EDSS Ambulation Score ≥6), the relative difference was significant (n=30; 24.0%; p<0.050). The results show that it is possible to create a transparently working ML prototype for a given medical use case while meeting certain software qualities.

Digital Biomarkers in Multiple Sclerosis.

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.

Fear of falling and falls in people with multiple sclerosis: A literature review.

INTRODUCTION: Fear of falling (FOF) is a widespread problem affecting about 60% of people with multiple sclerosis (pwMS). Inflammatory lesions in the brain that are caused by the disease result in gait deficits and increase the risk of fall. Falls induce fear of falling and trigger a vicious circle, which in turn increases the likelihood of falling. Objective of this review was to provide an overview of existing research on the effects of FOF and therapy options in multiple sclerosis. METHODS: A systematic search at Web of Science and PubMed was conducted. The search included the terms (fear of falling) OR (concern about falling) OR (fall anxiety) AND (multiple sclerosis). RESULTS: In included studies, FOF was measured by different instruments. The Falls Efficacy Scale-International (FES-I) was the most frequently used instrument for pwMS. Patients with a higher FOF score fell more frequently, had lower walking speed, shorter stride length, larger ellipse sway area and a more severe disability. At present, therapeutic offers exist mainly in the field of physiotherapy. For reducing FOF, assisted vibration (dz = 0.68), VR (dz =0.87) and bicycle training (dz = 1.23) were the most effective methods. CONCLUSION: It is advisable to develop therapies that incorporate both physical and psychological aspects in neurorehabilitation, like in a cognitive behavioral therapy. Moreover, FOF monitoring should be integrated into the clinical routine.

The Dresden Protocol for Multidimensional Walking Assessment (DMWA) in Clinical Practice.

Walking impairments represent one of the most debilitating symptom areas for people with multiple sclerosis (MS). It is important to detect even slightest walking impairments in order to start and optimize necessary interventions in time to counteract further progression of the disability. For this reason, a regular monitoring through gait analysis is highly necessary. At advanced stages of MS with significant walking impairment, this assessment is also necessary to optimize symptomatic treatment, choose the most suitable walking aid and plan individualized rehabilitation. In clinical practice, walking impairment is only assessed at higher levels of the disease using e.g., the Expanded Disability Status Scale (EDSS). In contrast to the EDSS, standardized functional tests such as walking speed, walking endurance and balance as well as walking quality and gait-related patient-reported outcomes allow a more holistic and sensitive assessment of walking impairment. In recent years, the MS Center Dresden has established a standardized monitoring procedure for the routine multidimensional assessment of gait and balance disorders. In the following protocol, we present the techniques and procedures for the analysis of gait and balance of people with MS at the MS Center Dresden. Patients are assessed with a multidimensional gait analysis at least once a year. This enables long-term monitoring of walking impairment, which allows early active intervention regarding further progression of disease and improves the current standard clinical practice.